Copyright 2020, Phillip McElroy

Improving cement wellbore integrity with nanomaterials: Design of experiments and

machine learning techniques

by

Phillip McElroy, B.Sc., M.Sc.

A Dissertation

In

Petroleum Engineering

Submitted to the Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Dr. Hossein Emadibaladehi

Chair of Committee

Dr. Lloyd Heinze

Dr. Marshall C. Watson

Dr. James Sheng

Dr. Habib K. Menouar

Mark Sheridan

Dean of the Graduate School

December, 2020

Texas Tech University, Phillip McElroy, December 2020

ii

ACKNOWLEDGMENTS

I would like to express my deepest appreciation to my committee chair, Dr.

Hossein Emadibaladehi. I attribute much of my growth as a researcher due to your

guidance, mentorship, and encouragement throughout my project. I want to thank you

for entrusting me as your first research assistant. This has helped me with my finances

towards completing my doctorate program and allowed me to better focus on my

research work. I am blessed to work with such a great scholar and affable person.

I would like to extend my appreciation to my other graduate committee

members: Dr. Lloyd Heinze, Dr. Marshall C. Watson, Dr. James Sheng, and Dr. Habib

K. Menouar for providing valuable comments and serving on my defense committee. I

would also like to thank Dr. Dominick J. Casadonte in the department of chemistry for

his help and allowing me to use his equipment.

I would like to thank the lab technician Cecil Millikan who was always willing

to help in the lab. I would also like to extend my gratitude to my lab colleagues,

Alexander Anya, Athar Hussain, and Heber Bibang. I really appreciate all the help you

have provided me. Additionally, I would like to thank Heather Johnson and Charlotte

Stockton who worked in the front office and were always willing to help.

A special appreciate to my parents Angela and Roosevelt. I would not be

where I am today without your unwavering love, upbringing, and prayer which has

sustained me thus far. Special thanks to my siblings Jairus and Justin for all your

support. I would also like to thank all my friends for helping me achieve my goal.

Most importantly I thank God for providing me with the energy and strength to

accomplish this task. I am eternally grateful for his mercy and grace.


iii

TABLE OF CONTENTS

ACKNOWLEDGMENTS..................................................................................ii

ABSTRACT ...................................................................................................... vi

LIST OF TABLES ........................................................................................... vii

LIST OF FIGURES ........................................................................................viii

1. INTRODUCTION .......................................................................................... 1

1.1 Background .............................................................................................. 1

1.2 Problem Statement ................................................................................... 4

1.3 Objective of this research ......................................................................... 6

1.4 Organization of the dissertation ................................................................ 7

2. LITERATURE REVIEW .............................................................................. 9

2.1 Singly-reinforced 1-Dimensinoal (1-D) nanomaterial applications in

cementitious materials .............................................................................. 9

2.1.1 Carbon nanotubes (CNTs)................................................................ 9

2.1.2 Carbon nanofibers (CNFs) ............................................................. 12 2.1.3 Alumina nanofibers (ANFs) ........................................................... 15

2.2 Hybrid-reinforcement technologies in cementitious materials ................. 17

2.3 Machine learning techniques to predict strength properties of

cementitious materials ............................................................................ 19

3. EXPERIMENTAL MATERIALS ............................................................... 22

3.1 Cement singly-reinforced with ANFs: Phase one .................................... 22

3.1.1 ANFs ............................................................................................. 22 3.1.2 Cement and wellbore additives ...................................................... 23

3.2 Cement hybridization scheme of ANFs and micro-synthetic

polypropylene (PP) fibers: Phase two ..................................................... 23

3.2.1 PP fibers ........................................................................................ 23 3.2.2 Sol gel treatment ............................................................................ 24

3.2.3 Cement and wellbore additives ...................................................... 24

3.3 Artificial Neural Network (ANN) model development: Phase three ........ 24

3.3.1 Nanoparticles ................................................................................. 24 3.3.2 Cement and wellbore additives ...................................................... 25

4. EXPERIMENTAL PROCEDURES ............................................................ 26


iv


4.1.1 Morphology of highly bundled ANFs ............................................ 26

4.1.2 ANF size reduction ........................................................................ 27 4.1.3 Ultrasonication of ANFs ................................................................ 29

4.1.4 Characterization of aqueous ANF solutions through quantitative

and qualitative analysis .................................................................. 31

4.1.5 Cement slurry preparation .............................................................. 33 4.1.6 Uniaxial compressive strength (UCS) tests preparation .................. 35

4.1.7 Splitting tensile (Brazilian tensile) strength tests preparation .......... 37 4.1.8 Cement slurry rheological properties .............................................. 38

4.1.9 Cement slurry stability tests preparation ......................................... 39 4.1.10 Thickening time tests preparation ................................................. 41

4.1.11 Cement permeability test preparation ........................................... 42 4.1.12 Cement elastic properties test preparation .................................... 45

4.1.13 Cement phase transformations ..................................................... 47 4.1.14 Degree of hydration (DOH) of cement ......................................... 47


polypropylene (PP) fibers: Phase two ..................................................... 48

4.2.1 Deposition of SiO2 nanoparticles on PP fibers ................................ 48 4.2.2 Cement slurry preparation .............................................................. 50

4.2.3 Cement mechanical property tests .................................................. 50

4.3 ANN model development: Phase three ................................................... 50

4.3.1 Assessment of pre-dispersed nanoparticle solutions ....................... 50 4.3.2 Cement slurry preparation .............................................................. 50

4.3.3 UCS tests preparation .................................................................... 52

5. RESPONSE SURFACE METHOD (RSM) THROUGH THE DESIGN

OF EXPERIMENTS (DOE) ........................................................................ 53

6. ARTIFIAL NEURAL NETWORK (ANN) ................................................. 56

6.1 Background ............................................................................................ 56

6.2 ANN architecture ................................................................................... 58

7. RESULTS AND DISCUSSION ................................................................... 60


7.1.1 UV-vis spectra characterization of ANF solutions

(quantitative analysis) .................................................................... 60

7.1.2 TEM characterization of ANF solution (qualitative analysis) ......... 62 7.1.3 Strength properties of cured cements samples reinforced with

ANFs ............................................................................................. 64 7.1.4 Cement slurry rheological behavior................................................ 68

7.1.5 Cement free fluid and sedimentation behavior................................ 69


v

7.1.6 Cement thickening time ................................................................. 70 7.1.7 Cement permeability measurements under cyclic confining

pressures ....................................................................................... 71 7.1.8 Cement elastic properties measurements under cyclic confining

pressures ....................................................................................... 75 7.1.9 XRD analysis ................................................................................. 82

7.1.10 TGA analysis ............................................................................... 85 7.1.11 ANF construction cost ................................................................. 87

7.2 Cement hybridization scheme of ANFs and micro-synthetic PP fibers:

Phase two ............................................................................................... 89

7.2.1 Morphology of SiO2 nanoparticles on PP fibers ............................. 89 7.2.2 UCS of ANFs and PP fibers ........................................................... 92

7.2.3 Tensile strength of ANFs and PP fibers .......................................... 95 7.2.4 MOE of ANFs and PP fibers .......................................................... 99

7.2.5 Poisson’s Ratio of ANFs and PP fibers ........................................ 101 7.2.6 ANOVA and RSM model analysis ............................................... 104

7.2.7 Multi-objective optimization of the responses and experimental

validation .................................................................................... 109

7.3 ANN model development: Phase three ................................................. 112

7.3.1 Data set description...................................................................... 112

7.3.2 Data set analysis .......................................................................... 112 7.3.3 Data set preprocessing ................................................................. 113

7.3.4 Assessment of pre-dispersed nanoparticles ................................... 114 7.3.5 ANN model selection ................................................................... 117

7.3.6 ANN prediction of UCS ............................................................... 119

8. CONCLUSIONS AND RECOMMENDATIONS ..................................... 123

8.1 Cement singly-reinforced with ANFs: Phase one .................................. 123

8.2 Cement hybridization scheme of ANFs and micro-synthetic PP fibers:

Phase two ............................................................................................. 124

8.3 ANN model development: Phase three ................................................. 124

8.4 Future work .......................................................................................... 125

9. BIBLIOGRAPHY....................................................................................... 126

10. APPENDIX ............................................................................................... 140


vi

ABSTRACT

The cementing process is an integral part of drilling a wellbore which ensures

wellbore integrity throughout the life of the well. Failure of the cement sheath can lead

to hazardous rig operations and deleterious environmental issues. Embedding strength

enhancing nanomaterials into the cement slurry is a promising approach for creating

multifunctional, durable composites capable of withstanding stressful wellbore

conditions. In recent years carbon nanotubes (CNTs) and carbon nanofibers (CNFs)

have received ubiquitous attention in oil well cement research. However, due to their

high hydrophobicity and tendency for graphene material to agglomerate, obtaining an

adequate dispersion is an arduous task and the cost is substantially high.

This study focuses on embedding alumina nanofibers (ANFs), a relatively

newly discovered material, in the cement slurry. The dispersibility of ANFs were

assessed before implementation into the cement matrix. Afterwards, cement specimens

were singly-reinforced with ANFs and cured in simulated wellbore conditions with

various properties tested upon removal. Also, since cement failure is a multistep

process, hybrid-reinforcement was implemented on the nanoscale and microscale

levels. Sol-gel treated micro-synthetic polypropylene (PP) fibers were added as micro

reinforcement and the mechanical properties of the cement composite was assessed.

Considering the mixture design is a multi-variable and multi-objective optimization

formulation, the response surface method (RSM) through the design of experiment

(DOE) was utilized.

The applicability of using supervised machine learning to predict the

unconfined compressive strength (UCS) of cement samples was also assessed. 195

cement samples were embedded with varying dosages of strength enhancing pre-

dispersed nanoparticles consisting of nanosilica (nano-SiO2), nanoalumina (nano-

Al2O3), and nanotitanium dioxide (nano-TiO2) at various simulated wellbore

temperatures. The developed model can replace, or be used in combination with,

destructive UCS tests which can save the petroleum industry time, resources, and

capital.


vii

LIST OF TABLES

3.1 Properties of 2% pre-dispersed ANF solution............................................ 23

3.2 Properties of PP fibers .............................................................................. 24

3.3 Properties of pre-dispersed nanoparticle solutions ..................................... 25

5.1 Cement hybridization mixture proportions ................................................ 55

7.1 Cement batch compositions (batch one) .................................................... 65

7.2 Cement batch compositions with bentonite (batch two) ............................. 67

7.3 Rheological properties for cement specimens ............................................ 69

7.4 Free fluid tests for cement specimens ........................................................ 70

7.5 Sedimentation tests for cement specimens................................................. 70

7.6 Cement thickening time tests .................................................................... 71

7.7 Weight percentage of hydration products in cement composites ................ 83

7.8 wb and DOH analyzed per gram of cement paste with different

ANF weight fractions................................................................................ 86

7.9 ANOVA model analysis ......................................................................... 104

7.10 Summary of Regression Model ............................................................... 105

7.11 Multi-objective optimization and experimental validation ....................... 111

7.12 Summary statistics of data set ................................................................. 112

7.13 Pearson's correlation coefficient (r) between input features ..................... 113

7.14 Pearson's correlation coefficient (r) between input features and

the output parameter ............................................................................... 113

7.15 Statistical performance measures ............................................................ 121

10.1 UCS tests: Phase three ............................................................................ 140


viii

LIST OF FIGURES

1.1 Schematic of axial, tangential, and radial stresses on the cement

sheath ......................................................................................................... 4

2.1 Nanomaterial dimension classification ........................................................ 9

2.2 Graphene and single-wall carbon nanotube (SWCNT) structure ................ 10

2.3 Multi-walled carbon nanotube (MWCNT) structure .................................. 10

2.4 Image of carbon nanofibers (CNFs) .......................................................... 13

4.1 Macroscopic image of highly bundled ANFs ............................................ 26

4.2 SEM image of highly bundled ANFs ........................................................ 27

4.3 High energy ball mill device ..................................................................... 28

4.4 ANFs after ball milling process ................................................................. 28

4.5 Ultrasonication bath .................................................................................. 30

4.6 ANF ultrasonication samples from left to right: 10 minutes, 30

minutes, 60 minutes, pre-dispersed solution .............................................. 31

4.7 Ofite model 20 constant speed blender ...................................................... 33

4.8 Ofite ultrasonic cement analyzer (UCA) ................................................... 34

4.9 Demolded cement sample taken from UCA .............................................. 35

4.10 Cement sample before UCS testing ........................................................... 36

4.11 Cement sample after UCS testing .............................................................. 36

4.12 Cement sample after splitting tensile strength failure ................................ 37

4.13 Couette coaxial cylinder rotational viscometer .......................................... 39

4.14 Free water development in cement ............................................................ 40

4.15 Sedimentation development in cement ...................................................... 40

4.16 Sedimentation cement sample before wet cutting ...................................... 41

4.17 Automated HTHP (high temperature high pressure)

consistometer ............................................................................................ 42

4.18 (NER) AutoLab 1500 system for ultrasonic velocity and

permeability measurements ....................................................................... 43

4.19 Assembled sample for permeability measurements ................................... 44

4.20 Assembled cement sample for ultrasonic velocity measurements .............. 46

4.21 Mettler Toledo TGA/SDTA851e Module.................................................. 48

4.22 Micro-synthetic polypropylene (PP) fibers ................................................ 49

4.23 Brass cement mold assembly .................................................................... 51


ix

4.24 Isothermal water bath................................................................................ 51

5.1 Central composite design (CCD) for k = 2 factors ..................................... 54

6.1 Representation of a biological neural network ........................................... 56

6.2 Schematic diagram of typical artificial neural network .............................. 58

7.1 Absorption spectra of sonicated samples at 10 minutes, 30

minutes, 60 minutes, and the pre-dispersed solution .................................. 60

7.2 Typical absorption spectra of deionized water with different

suspended ANF concentrations for the pre-dispersed solution ................... 61

7.3 Linear relationship between the absorbance and the concentration

at wavelength 227 nm ............................................................................... 62

7.4 Ball milled dispersed solution ................................................................... 63

7.5 2% pre-dispersed solution ......................................................................... 64

7.6 UCS of cement samples at 8 and 24 hours (batch one) .............................. 65

7.7 Tensile strengths of cement samples at 8 and 24 hours .............................. 66

7.8 UCS of cement samples (batch two) ......................................................... 68

7.9 Rheological flow curves for cement specimens ......................................... 69

7.10 Permeability measurement of Ref cement composite under cyclic

confining pressure..................................................................................... 72

7.11 Permeability measurement of ANF-1 cement composite under

cyclic confining pressure .......................................................................... 73





7.14 MOE results for each cement composite at the corresponding

confining pressure increment for both pressure cycles ............................... 76

7.15 Average values of Young’s Modulus, the percentage increase,

and standard deviations of cement composites under cyclic

pressure increments: (a) Ref sample (b) ANF-1 (c) ANF-2 (d)

ANF-3 ...................................................................................................... 78

7.16 Poisson’s Ratio results for each cement composite at the

corresponding confining pressure increment for both pressure

cycles ....................................................................................................... 79

7.17 Average values of Poisson’s ratio, the percentage change, and

standard deviations of cement composites under corresponding

cyclic pressure increments: (a) Ref sample (b) ANF-1 (c) ANF-2

(d) ANF-3 ................................................................................................. 81


x

7.18 Diffraction patterns of ANF-cement composites ....................................... 83

7.19 TGA results from 140 to 1100°C with the mass at 140°C as the

base (100%) .............................................................................................. 85

7.20 Schematic representation of cement hydration .......................................... 87

7.21 Schematic representation of cement hydration with ANF .......................... 87

7.22 Schematic of horizontal wellbore trajectory .............................................. 88

7.23 Cross-sectional view of the cemented lateral section ................................. 89

7.24 SEM images of untreated PP fibers ........................................................... 90

7.25 SEM images of sol-gel treated PP fibers ................................................... 90

7.26 EDX spectra of untreated PP fibers ........................................................... 91

7.27 EDX spectra of sol-gel treated PP fibers ................................................... 92

7.28 UCS of ANFs and PP fibers ...................................................................... 92

7.29 UCS 3-D response surface plot ................................................................. 93

7.30 UCS 2-D contour plot ............................................................................... 94

7.31 Tensile strength of ANFs and PP fibers ..................................................... 96

7.32 Tensile strength 3-D response surface plot ................................................ 96

7.33 Tensile strength 2-D contour plot .............................................................. 97

7.34 MOE of ANFs and PP fibers ..................................................................... 99

7.35 MOE 3-D response surface plot .............................................................. 100

7.36 MOE 2-D contour plot ............................................................................ 100

7.37 Poisson's Ratio of ANFs and PP fibers .................................................... 102

7.38 Poisson's Ratio 3-D response surface plot ............................................... 102

7.39 Poisson's Ratio 2-D contour plot ............................................................. 103

7.40 Normal plot of residuals .......................................................................... 106

7.41 Model predicted vs. actual values ........................................................... 107

7.42 Standardized residuals vs. model predicted line ...................................... 108

7.43 Perturbation plot ..................................................................................... 109

7.44 Ramp diagrams for optimization ............................................................. 111

7.45 Nano-SiO2 pre-dispersed solution ........................................................... 114

7.46 Nano-Al2O3 pre-dispersed solution ......................................................... 115

7.47 Nano-TiO2 pre-dispersed solution ........................................................... 116


xi

7.48 The performance of each network with differing number of

hidden nodes ........................................................................................... 118

7.49 Final network architecture for optimum performance .............................. 119

7.50 MSE training record of training, validation, and testing data ................... 120

7.51 Square root of the coefficient of determination for the ANN

model ..................................................................................................... 121


1

1. INTRODUCTION

In this section, the background for this research is discussed. Next, the problem

statement and objective of this research is presented. Lastly, the organization of this

document is briefed.

1.1 Background

In the petroleum industry, drilling a wellbore involves using drilling mud to

remove cuttings from the borehole, help support the weight of the drillstring, provide

hydrostatic pressure against the walls of the borehole to prevent the caving of

unconsolidated formations, and to cool and lubricate the drillstring along with the drill

bit. Casing (steel tubular) is then placed into the borehole with a Portland cement-

based slurry through the cementing process. The cementing process entails mixing

powered cement, water, and additives at the surface which forms the cement slurry.

The slurry is then pumped by hydraulic displacement into the annulus between two

different outer diameter sized casings or the geological formation and the casing. In

most cases, the cement slurry must remain pumpable at temperature and pressure

conditions above ambient to allow placement at the desired location. The slurry

progressively hardens and becomes “set”, developing into the cement sheath.

The cement sheath is paramount for wellbore integrity which has several

functions. These functions consist of the following (Mitchell and Miska 2011):

1. Prevent the movement of fluid through the annular space outside the

casing.

2. Provide structural support for the casing and wellbore.

3. Protection against corrosive fluids.

4. Close and abandoned a portion of the well.

5. Stops the movement of fluid into vugular or fractured formations.


2

In order for the cement sheath to adequately fulfill the described functions, the

cement slurry must develop into a rigid solid exhibiting favorable cementitious

properties. The cementitious properties are largely attributed to the chemical

composition and exothermic hydration reaction of the cement slurry. The principal

components of Portland cement consist of four crystalline compounds in the clinker

which hydrate to form a rigid structure. These compounds consist of:

1. Tricalcium silicate (3CaO · SiO2 or C3S), with mineral phase alite

2. Dicalcium silicate (2CaO · SiO2 or C2S), with mineral phase belite

3. Tricalcium aluminate (3CaO · Al2O3 or C3A), with mineral phase

aluminate or celite

4. Tetracalcium aluminoferrite (4CaO · Al2O3 · Fe2O3 or C4AF), with

mineral phase ferrite

Each principal component displays different hydration kinetics and forms

different hydration products. However, the silicate phases in Portland cement are the

most abundant, often comprising more than 80% of the composition. C3S, the

principal constituent, can reach concentrations as high as 68% while C2S normally

does not exceed 30%. The hydration products of the silicate phases are calcium

silicate hydrate and calcium hydroxide (portlandite). The idealized chemical equations

are shown below (Nelson and Guillot 2006):

2C3S → C3S2H3 + 3CH

2C2S + 4H → C3S2H3 + CH

The calcium silicate hydrate has varying C:S and H:S ratios meaning it does

not have the exact composition of C3S2H3. The varying ratios are a result of

temperature, calcium concentration in the aqueous phase, aging, and the presence of

additives. The material is quasi-amorphous and is therefore commonly termed the “C-

S-H” phase. The C-S-H phase is considered the principal binder of hardened cement

and comprises approximately 65% of the fully hydrated Portland cement at ambient

conditions. Conversely, calcium hydroxide is highly crystalline and occurs as


3

hexagonal plates. Its concentration is usually between 15% and 20% in hardened

cement. The C-S-H phase formed by the hydration of C3S is very similar to that

formed by C2S. C3S hydrates rapidly and is largely responsible for the early strength

development of cement, especially during the first 28 days of curing. However, C2S

hydrates slowly and contributes mainly to the long-term strength of cement. Despite

the different reaction rates, the hydration mechanisms of both silicate phases are very

similar. C3A comprises roughly 8% to 14% of the chemical composition. C3A

hydrates the fastest and is responsible for the initial setting of cement. It also produces

most of the heat of hydration observed during the first few days. Gypsum is added to

the clinker to control this reaction. Unfortunately, the C3A portion of cement is readily

attacked by water containing sulfates. Lastly, C4AF comprises roughly 8% to 12% of

the chemical composition. C4AF hydrates slowly and has only minor effects on the

physical properties of cement. Experience has shown sulfate resistant cement can be

produced by decreasing the concentration of C3A and increasing the concentration of

C4AF.

The cement sheath is subjected to various operational procedures and

geological formation shifting which generate stresses, throughout the life of the

wellbore. Operational procedures consist of pressure testing, hydraulic fracturing,

perforating, cement hydration causing autogenous shrinkage, subsequent drilling,

hydrocarbon production, EOR processes, and fluid injection. The produced stresses

will directly impact the axial, tangential, and radial stresses on the cement sheath

behind the casing (Ramos et al. 2009; Li et al. 2017). Figure 1.1 shows the

aforementioned stresses where (𝜎𝑣𝑐) represents the vertical stress, (𝜏𝑟𝜃𝑓) represents

the tangential stress, and (𝜎𝑟𝑓) represents the radial stress.


4

Figure 1.1: Schematic of axial, tangential, and radial stresses on the cement sheath

Typically, the tensile strength represents the ultimate tangential stress acting

perpendicular from the direction of radial stress while the compressive strength

represents the ultimate radial stress acting perpendicular to the axis of the wellbore

(Mueller et al. 2004). Hence, researchers routinely conduct various tests to measure

the mechanical properties (strength and elastic properties) of the cement sheath. These

tests provide insight on the ability of the cement sheath to withstand stress events after

curing under simulated downhole conditions. The durability of cement sheath is also

characterized by the impermeability, hydration reactions, stability, and ability to

withstand cyclic mechanical loading (Lavrov and Torsæter 2016). Thus, the cement

sheath should be designed to withstand the plethora of various stresses throughout the

life of the wellbore.

1.2 Problem Statement

Conventional cement-based materials are naturally brittle and display low

tensile strength. Failure of a poorly designed cement sheath can result in reduction in

production capacity, poor performance in bottom hole conditions, costly repair

operations, environmental issues, and in worst case loss of the well. According to the

BP Deepwater Horizon Accident Investigation Report (Bly 2011), the blowout of

Macondo in the Gulf of Mexico (USA) was primarily attributed to cement failure.


5

This catastrophe happening on the evening of April 20, 2010 caused a rig explosion

killing 11 people, injuring 17 others, and causing a major oil spill. In addition, failure

of the cement sheath can result in annulus pressurization and gas migration towards

the surface. According to (Davies et al. 2014), data from around the world (Australia,

Austria, Bahrain, Brazil, Canada, the Netherlands, Poland, the UK and the USA)

indicate that up to 75% of wells have experienced some form of wellbore integrity

failure. Ultimately, the search for alternative materials to improve mechanical

performance, in the oil well cementing operation, is still continuing.

In recent years, there has been considerable efforts to improve the mechanical

properties of cement beginning on the nanoscale. Cracks in cement-based materials

initiate from the nanoscale where micro additives and macro additives are not

effective. According to (Jafariesfad et al. 2017), using nanomaterials in oil well

cement is a promising method to alter conventional cement systems into

multifunctional durable composites; cement composites reinforced with nano

additives, have the potential of withstanding stressful conditions for the entire life of

the well. Among composite nanoscale reinforcement material, graphene ring-based

materials have received majority of research efforts. Specifically, carbon nanotubes

(CNTs) and nanofibers (CNFs) have attracted substantial attention due to their

extraordinary strength properties in terms of tensile strength (Giga Pascal (GPa)) and

modulus of elasticity (Tetra Pascal (TPa)). However, due to the high hydrophobicity

and the tendency of graphene material to agglomerate, owing to Van der Waals forces,

obtaining an adequate dispersion is an arduous task and the cost of such material is

substantially high (Sun et al. 2016; Liew, Kai, and Zhang 2017) . Nanofiber

agglomeration infamously diminishes the mechanical properties of fiber-reinforced

composite materials (Romanov et al. 2015). This can be catastrophic in wellbores

considering the cement sheath, between the formation and casing, is subjected to

various loads (Iverson, Darbe, and McMechan 2008). According to (Jafariesfad, et al.

2017), despite recent technological advancement in smart polymeric materials, fibers,

and self-healing materials, it is still a big challenge to provide adequate long-term

zonal isolation in severe oil well conditions. Additionally, it is a challenge to produce


6

reliable and accurate predictive models to estimate the mechanical property values of

nano reinforced oil well cement samples in wellbore conditions. The use of predictive

models can replace, or be used in combination with, destructive tests which can

significantly save the petroleum industry time, resources, and capital.

1.3 Objective of this research

This research encompasses three major phases. The first phase examines the

applicability of using alumina nanofibers (ANFs) to increase the mechanical

properties of oil well cement. ANFs (discovered in 2010) are a relatively new type of

reinforcement nanomaterial with the potential to add significant positive impact to the

oil well cementing operation. Cement samples, embedded with ANFs (singly-

reinforced), are cured in simulated oil well conditions with various properties tested.

According to (Ravi, Bosma, and Gastebled 2002), it is imperative to test various

properties of cement rather than one property (compressive strength, tensile strength,

Poisson’s ratio, etc.) to characterize the durability of the cement sheath in wellbore

conditions.

In the second phase we used an innovative fiber hybridization scheme of ANF

and micro-synthetic polypropylene (PP) fibers; the performance of the mechanical

properties of cement samples were assessed after curing the samples in simulated

wellbore conditions. Cement samples are simultaneously reinforced on the nano and

microscale levels. The hypothesis is that the hybrid-reinforcement improves the

cement composite material vis-à-vis singly-reinforced cement composites.

Considering the mixture design is a multi-variable and multi-objective optimization

formulation, the response surface method (RSM) through the Design of Experiment

(DOE) was utilized. Essentially, a simulation model was constructed and used to

calculate the optimum dosages of fibers needed to enhance the cementitious

properties.

In the third phase, we used machine learning techniques to predict the uniaxial

compressive strength (UCS) of cement samples cured at elevated temperatures (above

ambient). Cement samples were embedded with nanoparticles commonly used in oil

well cement operations and research. Our intent was to produce a highly accurate


7

model which could replace, or be used in combination with, destructive UCS tests.

With a highly accurate model, the number of UCS tests can be eliminated or reduced

which can significantly reduce time, resources, and capital.

1.4 Organization of the dissertation

This dissertation is partitioned into seven sections.

Section one introduces the background information for this project, presents

the motivation for this project, provides the objectives of this project, and describes

the organization of the dissertation.

Section two provides a literature review of singly-reinforced one-dimensional

(1-D) nanomaterial and hybrid-reinforced nanomaterial that has been researched and

applied to date in cement research. Additionally, an up to date literature review is

provided on the use of machine learning techniques to predict the properties of

cementitious materials.

Section three gives a detailed description of the materials used for all three

phases of this project.

Section four gives a detailed description of the laboratory procedures and

experimental workflow for all three phases of this project.

Section five explains the computational simulation work implemented to

calculate the optimum dosages of hybrid-reinforcement material to improve the

mechanical properties of oil well cement. The RSM through the DOE is discussed.

Section six explains the machine learning techniques used to predict the UCS

of cement samples reinforced with various nanoparticles at elevated temperatures.

This was accomplished by constructing an artificial neural network (ANN) which

provides high prediction accuracy.

Section seven presents the results and discussion of all three phases. The order

of the results and discussion consists of singly-reinforced ANF cement performance,

hybrid-reinforced cement performance, and the predictive performance of the ANN.


8

Section eight summarizes the research work presented in this dissertation and

presents the conclusions drawn from the results. The dissertation is concluded by

providing ideas about future work.


9

2. LITERATURE REVIEW

2.1 Singly-reinforced 1-Dimensinoal (1-D) nanomaterial applications in

cementitious materials

Cracks in cement-based materials initiate from the nanoscale where

macrofibres and microfibers are not effective (Metaxa, Konsta-Gdoutos, and Shah

2010). Nanomaterials are classified based on the number of dimensions which are not

confined to the nanoscale range (<100 nm) (Figure 2.1).

Figure 2.1: Nanomaterial dimension classification

Nanofibers and nanotubes are 1-D materials which have displayed the ability

to enhance the mechanical properties of ceramic composites better than traditional

nanoparticles (Khitab and Tausif Arshad 2014; Aghayan 2016). Due to their

extraordinary strength properties, CNTs and CNFs have attracted the most research

interest in cementing applications. However, ANFs have not received such ubiquitous

research attention despite their potential upsides.

2.1.1 Carbon nanotubes (CNTs)

Due to their enhanced tensile strength, elastic modulus, thermal and electrical

conductivity, CNTs have gained incredible scientific attention (Dresselhaus,

Dresselhaus, and Eklund 1996). CNTs can be imagined as a rolled graphene sheet

whose structure comprises one layer of carbon atoms bonded by carbon sp2 bonds in a

hexagonal pattern. CNTs consists of single-walled carbon nanotubes (SWCNTs) and


10

multi-walled carbon nanotubes (MWCNTs). Below (Figure 2.2) is a depiction of a

SWCNT with a single sheet of graphene rolled up into a tube (Zuo 2018).

Figure 2.2: Graphene and single-wall carbon nanotube (SWCNT) structure

Conversely, MWCNTs are made up of many concentric single-walled tubes as

shown in Figure 2.3 (Program 2019). Due to their additional layers, MWCNTs are

stronger than SWCNTs and are generally favored for use in composite materials.

Figure 2.3: Multi-walled carbon nanotube (MWCNT) structure

(Konsta-Gdoutos, Metaxa, and Shah 2010) reported the fracture properties of

ordinary Portland cement increased through proper dispersion of small amounts of

MWCNTs (0.048 wt.% and 0.08 wt.%). In particular, lower amounts of longer

MWCNTs are needed to achieve effective reinforcement, while higher concentrations

of short MWCNTs are required to achieve the same level of mechanical performance.


11

Lastly, the nanoindentation results suggest that MWCNTs can strongly reinforce the

cement paste matrix at the nanoscale by increasing the amount of high stiffness C-S-H

and decreasing the porosity.

(Nochaiya and Chaipanich 2011) reported up to 1 wt. % of CNTs by weight of

cement (BWOC), embedded in Portland cement type I, acted as a filler in the cement

matrix reducing the total porosity. This resulted in a denser microstructure and higher

strength when compared to the control mix.

(Sobolkina et al. 2012) concluded an appropriate dispersion of CNTs is

mandatory in order to make use of the positive effects CNTs can provide on the

mechanical properties of cement-based materials. An appropriate dispersion of CNTs

led to a 40% increase in compressive strength.

(de Morais, Haddad, and Haurie 2013) investigated the effect of embedding

MWCNTs at 0.2%, 0.4%, and 0.6% BWOC on the microstructure and physical-

mechanical properties of the cement composite structure. The study suggests 0.4%

MWCNTs showed the best performance, increasing the dynamic modulus of elasticity

(MOE) about 25% and achieving a higher rate of structural compaction.

(de Paula et al. 2014) analyzed the influence of MWCNTs on the rheological

and mechanical properties of oil well cement Brazilian type “G” slurries. The

nanotubes were grown directly onto the cement clinker by chemical vapor deposition.

Cement pastes containing 0.1% and 0.3% BWOC of nanotubes were compared with

nanotube cement free slurries. The compressive and tensile strengths of cement

samples were evaluated at 48 hours and seven days. Considering the use of

dispersants, the results show that the addition of MWCNTs does not alter the

rheological behavior and stability of the cement slurries. With respect to the

mechanical properties, no difference in compressive strength was observed between

cement samples with and without MWCNTs. Although, an average of 15% gain was

achieved in tensile strength at the test times of 48 hours and seven days for 0.1%

MWCNTs BWOC.


12

(Rahman et al. 2016) embedded multi-walled carbon nanotubes MWCNTs in

type “G” oil well cement and reported a higher compressive strength compared to the

base cement samples. Incorporating MWCNTs in the cement slurry improved the

rheological properties of the cement slurries and their displacement efficiency in

wellbore conditions.

(Li et al. 2019) analyzed the effect of MWCNTs on the mechanical properties

of lightweight cement under triaxial loading conditions. Conventional cement was

compared against two different lightweight cement systems, foam and microsphere

cements, mixed with MWCNTs at the concentration of 0.5% BWOC. Under elevated

confining pressures, the Young’s modulus, compressive strength, brittleness, and

strain capacity were measured. Additionally, the test data was used to calculate the

permeability and splitting tensile strength. The results suggest MWCNTs enhances the

compressive and splitting tensile strength, Young’s modulus, strain capacity and

ductility, and decreases the permeability of the lightweight cement systems.

(Rzepka and Kędzierski 2020) reported an increase in the plastic viscosity of

Portland cement and class “G” oil well cement slurries with the addition of MWCNTs.

The authors concluded the addition of 0.1% BWOC is the optimal amount of

MWCNTs to improve the strength parameters of the cement matrix which densifies

the microstructure. Increasing the concentration of the MWCNTs past 0.1% lowers the

mechanical properties of the cement matrix.

Unfortunately, the cost of using MWCNTs in oil well cementing operations is

exceptionally high which limits their use in operations. The functionalization required

to facilitate chemical bonding with the cement matrix requires creating defect sites for

attaching functional groups. This requires several processing steps and can be difficult

and costly to scale-up. In the absence of costly proper dispersion techniques,

MWCNTs will agglomerate and their effectiveness could be lost.

2.1.2 Carbon nanofibers (CNFs)

Compared to CNTs, fewer studies have been conducted analyzing the

integration of CNFs in cementitious materials. Unlike the inert edges MWCNTs


13

possess, CNFs (Figure 2.4) have graphene sheets exposed on their surface (Friedlander

2017). These edges provide a high concentration of active sites for bonding to the

cement matrix (Peyvandi et al. 2017).

Figure 2.4: Image of carbon nanofibers (CNFs)

(Ohama 1989) documented one of the first uses of dispersed CNFs in cement

for practical application use in the construction industry. The author states carbon-

fiber-reinforced cement and concrete has advantages over conventional concrete and

mortar in its mechanical properties. The mechanical properties consist of the tensile

and flexural strengths, toughness and impact resistance, dimensional stability, electric

conductivity, wave absorbing property, and durability.

(Tyson et al. 2011) stated that CNFs, due to their size, can be distributed on a

much finer scale than micro-reinforcing fibers. Thus, microcracks are interrupted more

quickly during propagation in a nano-reinforced cement matrix. This results in much

smaller crack widths at the point of first contact between the moving crack front and

the reinforcement. The authors dispersed CNFs by using an ultrasonic mixer which

were then cast into cement molds. Each cement specimen was tested in a custom-made

three-point flexural test fixture to record certain mechanical properties. The

mechanical properties consisted of the Young’s modulus, flexural strength, ultimate

strain capacity, and fracture toughness, at 7, 14, and 28 days. A scanning electron

microscope (SEM) was used to discern the difference between crack bridging and


14

fiber pullout. Their test results show that the ductility, fracture toughness, and flexural

strength can be improved with the addition of low concentrations CNFs.

(Abu Al-Rub et al. 2012) conducted the same described experiment above as

(Tyson, et al. 2011), except the CNFs were functionalized in a solution of sulfuric acid

(H2SO4) and nitric acid (HNO3). This time, the acid-treated CNFs degraded the

mechanical properties of the cementitious materials rather than improving the

cementitious properties. The authors state the degradation in mechanical properties is

attributed to the excessive formation of ettringite caused by the presence of sulfates.

(Mo and Roberts 2013) used CNFs in self-consolidating carbon nanofiber

concrete to improve the strength and stiffness of concrete. They concluded excessive

amounts of CNFs leads to a poor dispersion generating clumps inside the concrete

which can negatively impact both the strength and electrical sensitivity. Also, the

highly workable and stable self-consolidation concrete was able to maintain its

workability and stability with the addition of CNFs. Lastly, self-consolidating carbon

nanofiber concrete can be used for self-structural health monitoring.

(Barbhuiya and Chow 2017) added 0.2% BWOC CNFs to ordinary Portland

cement and concluded that CNFs are capable of forming strong interfacial bonding

with cement matrices. Experimental results using nanoindentation reveal that the

addition of CNFs in cement composites increases the proportions of high-density C-S-

H gel. It was also found that the inclusion of CNFs increases the compressive strength

of cement composites with the ability to bridge across cracks in the cement matrix.

(Hogancamp and Grasley 2017) focused on the ability to create and maintain a

stable dispersion of CNFs in Portland cement-based materials. CNFs in concentrations

up to 5% BWOC were dispersed, added to microfine cement, and added to Type I/II

cement grains. A computational simulation was used to examine the geometric

clustering on the dispersed material. In their simulation results, a higher achievable

dispersion for microfine cement was ascertained as compared to the Type I/II cement.

Additionally, SEM images indicated excessive CNF clumping among Type I/II

cement grains, while the dispersion of microfine cement mortar continued to improve


15

as the concentration of CNFs increased up to 5% by mass of cement. Mortar cube

elastic stiffness and mortar prism flexure tests revealed that high concentrations of

CNFs had detrimental effects in hybrid Type I/II cement mortar, whereas similar

concentrations of CNFs had negligible or beneficial effects in hybrid microfine cement

mortar.

CNFs are priced significantly lower than CNTs which make CNFs a viable

option to use in cementitious materials. Generally, CNFs range in price from as low as

$100 per pound to as much as $500 per pound. Conversely, the price of CNTs vary

widely and are dependent on the purity and quality of the material. Generally, CNTs

range in price from as low as $100 per pound to as much as $750 per gram. It should

be mentioned this is only the cost of the raw material, functionalizing the material will

add additional costs.

Although much lower in price, CNFs do not possess the high strength

properties CNTs possess. Thus, the mechanical performance of CNF reinforced

cement will be lower than CNT reinforced cement. One reason is CNTs require

significant higher energies for crack propagation around a tube as compared to across

it with CNFs. Additionally, the strength properties of CNFs are lower than the strength

properties of CNTs. Lastly, both CNTs and CNFs suffer from van der Waals

interactions which causes them to settle out of aqueous media if not properly dispersed

(Tabatabaei, Dahi Taleghani, and Alem 2019).

2.1.3 Alumina nanofibers (ANFs)

Although reinforcing cementitious materials with 1-D nanomaterials provides

promising results, reinforcing cementitious materials with ANFs has not received such

ubiquitous research attention.

Alumina, Al2O3, is a ceramic metal oxide of great importance as building

material, refractory material, electrical and heat insulator, attributed to its high

strength, corrosion resistance, chemical stability, and low thermal conductivity. ANFs

are made from aluminum metal or aluminum containing materials. ANFs have aspect


16

ratios (length-to-diameter ratio) of 18-120:1, allowing effective bonding with various

hydration products in cement (Fu et al. 2017).

(Rahman, Hossain, and Radford 2018) conducted compressive and fracture

toughness strength tests on neat MEYEB, which is a polysialate geopolymer in a

suspension form containing various chemicals including potassium silicate, aluminum

oxide (Al2O3), silicon dioxide (SiO2), aluminum phosphate (AlPO4) and water. The

strength was measured at various temperatures along with analyzing the phase

transformations. The tests were also conducted with ANF reinforcement. The

compressive strength and fracture toughness tests indicate both neat and ANF

embedded geopolymer samples increased with temperature until a significant

microstructural changes and phase transformation occurred. The fracture toughness

also increased proportionally with the addition of nanofibers, which resulted from the

increase in crack bridging and pull-out mechanism. However, the phase

transformation of the geopolymers samples and thermal degradation of ANFs affected

the expected performance of nanofibers at high temperatures. Additionally, ANFs

assisted in increasing the viscosity of the geopolymer matrix which resulted in

increasing the ferroelectric performance.

(Muzenski, Flores-Vivian, and Sobolev 2019) used ANFs in cement-based

mortar at a concentration of 0.25% BWOC. The inclusion of ANFs significantly

improved the compressive strength of the cementitious material. In fact, the strength

increase was 30% higher than when compared to the strength of the same material

with only 1% BWOC of silica fume. The authors stated additional quantities of ANFs

did not improve the performance of the cementitious material, but rather decreased the

performance. It was revealed an adequate dispersion of ANFs is necessary to achieve

top performance. A longer dispersion time should result in lesser agglomeration of

fibers, which should thereby boost the performance of the composite material. The

authors state ANFs are able to act as a seed for the formation of hydration products

and provide reinforcement effect which reduces the development of micro-cracks.

Additionally, they demonstrated that the chemical shrinkage of cementitious

composites with aluminum oxide nanofibers was reduced by 34.1% vs. the reference


17

Portland cement system produced at the same water-to-cement (W/C) ratio and dosage

of superplasticizer. The proposed performance enhancement theories are based on the

results and analysis of heat flow curves, chemical shrinkage data, and microstructure

observations. However, the authors believe more in-depth studies may be required to

verify these concepts.

2.2 Hybrid-reinforcement technologies in cementitious materials

In addition to singly-reinforced cement composite enhancements, hybrid-

reinforcement has attracted considerable research efforts. Below, are examples of

prominent research findings pertaining to hybrid reinforcement in cementitious

materials.

(Yao, Li, and Wu 2003) analyzed concretes containing different types of

hybrid fibers at the same volume fraction (0.5%). Samples were compared in terms of

their flexural, compressive, and splitting tensile properties. Three types of hybrid

composites were constructed using fiber combinations of PP and carbon, carbon and

steel, and steel and PP fibers. The authors demonstrated that fiber hybridization could

result in superior composite performance compared to their individual fiber-reinforced

concretes. Among the three types of hybrids, the carbon–steel combination gave

concrete of the highest strength and flexural toughness because of the similar modulus

and the synergistic interaction between the two reinforcing fibers.

(Banthia and Sappakittipakorn 2007) investigated the toughness of fiber

reinforced concrete with large diameter crimped fiber and smaller diameter crimped

fibers. The workability of the hybridized composite material was maintained. The

results show that hybridized fibers were able to significantly enhance toughness,

thereby rendering fiber hybridization a promising concept.

(Metaxa, Konsta-Gdoutos, and Shah 2010) reported ladder scale reinforcement

in Type I ordinary Portland cement (OPC) using carbon nanofibers (CNFs) and

polyvinyl alcohol (PVA) microfibers. Their results show that the incorporation of

nanofibers and microfibers enhanced the flexural strength, Young’s Modulus (MOE),

and toughness of the cement matrix.


18

(Sbia et al. 2014) conducted an optimization experimental program in order to

determine the optimum combination of steel fiber and CNF in ultrahigh performance

concrete. The optimum volume fractions of steel fiber and CNF identified for balanced

improvement of flexural strength, ductility, energy sorption capacity, impact, and

abrasion resistance of UHPC were 1.1% and 0.04%, respectively. The fibers displayed

reinforcing effects at different scales which improved the composite material.

(Yu et al. 2018) used polyvinyl alcohol fibers with recycled polyethylene

terephthalate fibers in cementitious composite materials. This hybridized combination

of fibers ultimately reduced the cost of the overall material and displayed acceptable

crack control ability.

(Hari and Mini 2019) studied high strength self-compacting concrete with the

inclusion of sisal and Nylon 6 fibers. Hybrid fiber combinations proportioned at 0/100,

25/75, 50/50, 75/25, and 100/0 of total fiber volume were analyzed. It was observed

that fiber hybridization improved the flexural and tensile properties of the composite

material better than the mono fiber mixes. The irregular fiber distribution beyond the

optimum fiber content lead to uneven load transfer in the composite which leads to a

reduction in the mechanical properties. The hybridization of factors also improved the

ductility of the composite material.

(Ragalwar et al. 2020) studied the mechanism of adding steel fibers and steel

wool simultaneously in ultra-high-performance concrete. Their research aimed to

improve the bond between steel fibers by use of steel wool. Single fiber pullout tests

and microscopic observations were conducted to analyze the fiber-matrix bond.

Compared to the control cementitious samples, mixtures with no steel wool,

significant improvement in the flexural behavior was observed with the hybrid

material combination. Thus, the addition of steel wool in steel fiber-reinforced ultra-

high-performance concrete provides multi-scale reinforcement that leads to significant

improvement in fiber-matrix bond and mechanical properties of the cementitious

material.


19

Despite the amount of hybrid-reinforcement research completed, there is

limited research conducted using a hybrid-reinforced cement system in a simulated

wellbore environment using oil well cement at elevated curing conditions. To the

authors’ knowledge, only (Song et al. 2018) have conducted experiments whereby 3

wt.% BWOC latex powder in conjunction with 2 wt.% BWOC flexible rubber was

incorporated into cement class “G” at elevated curing conditions of 90°C and 21 MPa.

They reported increases in impact and flexural strength but a decrease in the

compressive strength, when compared to singly-reinforced cement and cement without

reinforcement additives. Moreover, there is no investigation in the literature on the

hybridization of two or more different types of fibers at the nano and micro scale

levels in wellbore conditions. Fibers differing in shape, length, elastic modulus, tensile

strength, and surface polarity, can potentially improve the high strain capacity, tensile

strength, multistage crack resistance, strain hardening behavior, and ultimate strength

of the composite material (Ahmed and Mihashi 2011; Silva, Coelho, and Bordado

2013; Yoo and Banthia 2016; Pakravan, Jamshidi, and Latifi 2016; Yang et al. 2019).

Essentially, cement failure is a gradual multi-scale process with cracks beginning on

the nanoscale level, which coalesce to induce cracks at the microscale level eventually

causing failure at the macroscopic level (Metaxa, Konsta-Gdoutos, and Shah 2010).

Fibers are capable of bridging cracks, which transfer loads, thereby abating or

preventing the coalescence of cracks.

2.3 Machine learning techniques to predict strength properties of cementitious materials

In recent years, soft computing (SC) and machine learning (ML) techniques

have been utilized by researchers to predict the properties of concrete materials.

Among these methods, ANNs have increased in popularity. ANNs have shown the

ability to accurately predict strength properties of concrete in civil engineering

applications. Below, are examples of prominent research findings pertaining to the

prediction of cementitious properties using machine learning techniques.

(Słoński 2010) used nonlinear regression modelling with feed forward neural

networks, involving evidence framework and full Bayesian inference with Markov


20

chain Monte Carlo stochastic sampling to model the compressive strength of high-

performance concrete. The author presents an empirical assessment of the described

approaches using a benchmark regression problem for compressive strength prediction

of high-performance concrete. The results indicate the Bayesian approach with the

Markov chain Monte Carlo sampling approximation of learning and prediction gives

the best prediction accuracy.

(Uysal and Tanyildizi 2012) developed an ANN model to predict the loss of

compressive strength of self-compacting concretes containing mineral additives and

PP fibers exposed to elevated temperatures. The authors found that the empirical

model, developed by the ANN, had high prediction capabilities.

(Duan, Kou, and Poon 2013) constructed a 14-16-1 ANN model which was

able to accurately predict the compressive strength of recycled aggregate concrete

prepared with varying types and sources of recycled aggregates.

(Nikoo, Torabian Moghadam, and Sadowski 2015) predicted the compressive

strength of concrete using evolutionary artificial neural networks as a combination of

ANN and evolutionary search procedures, such as genetic algorithms. Samples of

cylindrical concrete parts with different characteristics, were used with 173

experimental data patterns. Water-cement ratio, maximum sand size, amount of

gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were

considered as inputs. The compressive strength of concrete was calculated as the

output of the model. Additionally, using genetic algorithms, the number of layers,

nodes, and weights were optimized in the ANN model. The developed model was

compared with a multiple linear regression model. According to their results, the ANN

model has more flexibility, capability, and accuracy in predicting the compressive

strength than the multiple linear regression method.

(Zhou, Wang, and Zhu 2016) proposed the use of ANNs and adaptive neuro-

fuzzy inference systems for estimating the compressive strength of hollow concrete

block masonry prisms. The prisms’ height-to-thickness ratio, compressive strengths of

hollow concrete blocks and mortars, were used as inputs to the models. Their results


21

show that the proposed models have excellent prediction abilities with insignificant

error rates.

(Onyari and Ikotun 2018) predicted the compressive and flexural strength of

mortar made with a modified zeolite additive. The input layer had six parameters:

cement quantity, silica sand quantity, modified zeolite additive quantity, water

quantity, curing period, and load weights. The output layer consisted of either the

compressive or the flexural strength. After model optimization and determining the

number of nodes in the hidden layer, the authors concluded their developed ANN

algorithm can be used to determine both the compressive and flexural strength of

mortar.

(Awoyera et al. 2020) modeled the strength characteristics of geopolymer self-

compacting concrete, made by the addition of mineral admixtures, with both genetic

programming and the ANN techniques. Their developed model involved using raw

materials and fresh mix properties as predictors, and strength properties as response.

The results show both genetic programming and ANN methods exhibited good

prediction of the experimental data, with minimal errors.

The popularity of ANNs are attributed to their ability to tolerate relatively

incomplete or imprecise tasks, ability to learn from examples, high accuracy, and

reduced vulnerability to outliers. Despite their popularity, to our knowledge, there is

no published research concerning the use of ANNs to predict oil well cement

compressive strength embedded with strength enhancing nanoparticles.


22

3. EXPERIMENTAL MATERIALS

In sections 3 and 4, the materials, procedures, and experimental workflow are

presented for each of the three phases studied in this project. The first phase examines

the applicability using singly-reinforced 1-D ANFs to improve wellbore integrity. The

second phase compares using an innovative fiber hybridization scheme of ANF and

micro-synthetic (PP) fibers compared to the singly-reinforced ANF cement

formulation. In the third phase, an ANN model was constructed. The ability of the

model to accurately predict the UCS of cement samples, reinforced with commonly

used nanoparticles, was assessed.

3.1 Cement singly-reinforced with ANFs: Phase one

3.1.1 ANFs

-alumina nanofibers (ANFs) is a relatively new type of reinforcement

nanomaterial functionalized by aluminum, zirconium, nickel, and copper oxides.

Al2O3 exists in several polymorphs, such as γ-, δ-, η-, θ-, κ-, and α- phases. Among the

transition aluminas, γ-alumina is one of the most important. Boehmite or γ-alumina

forms through dehydration of aluminum hydroxide when heated at elevated

temperatures (~500°C). ANFs have drawn noteworthy attention, in various industries,

as a low cost 1D nanofiber possessing high strength, stability at 1100 °C, low thermal

conductivity, and corrosion resistance. Beyond 1100 °C the structure changes

drastically resulting in the formation of the α phase. The applicability of γ-Al2O3 is

traced to a unique combination of its structural properties (e.g. pore size and

distribution, high surface area, specific acid/base features), (Aghayan 2016). ANFs

also have aspect ratios (length-to-diameter ratio) of 18-120:1, which can allow

effective bonding to composite materials. Typically, nanomaterials with high aspect

ratios allow for a nearly homogenous spacing at the nano-scale, which provides the

ability to stop crack growth at the micro-scale level by physically interrupting

propagation in cementitious materials.

As received highly bundled γ-Al2O3 (99.9% purity) nanofibers, along with as

received 2% pre-dispersed (in deionized water) ANF solution, were purchased from


23

ANF technology (Europe). The physical properties of the 2% pre-dispersed solution of

ANFs are shown in Table 3.1.

Table 3.1: Properties of 2% pre-dispersed ANF solution

OD Length Tensile

Strength

MOE TS Density SA pH

2.7-10 nm

100-900 nm

12 GPa

400 GPa

1100°C

3.89 g/cm3

150-160 m2/g

6.4-6.8

Note: OD-Outer Diameter; MOE- Modulus of Elasticity; TS-Thermal Stability; SA-Surface

area; pH- Potential of Hydrogen

3.1.2 Cement and wellbore additives

Class “H” oil well cement, which is used by nearly 80% of oil drilling

companies, was used as the binder material (Guner and Ozturk 2015; Sun, et al. 2016).

The chemical composition consists of 52% C3S, 25% C2S, 12% C4AF, 5% C3A, and

3.3% CaSO4. Bentonite, which increases the yield of cement slurry thereby reducing

the cost of the cementing operation, was used as an extender. Bentonite is

predominately composed of the mineral smectite, NaAl2(AlSi3O10)(OH)2, which is

composed of two flat sheets of silica tetrahedra sandwiching one sheet of alumina

octahedra (Nelson and Guillot 2006). Due to the increased solid volume fraction

(SVF) (volume of particles/volume of slurry), as a result of including strength

enhancing nanomaterials, dispersant is added as a friction reducer. CFR-3 (cement

friction reducing) agent was the dispersant used which is a sulfonic acid salt; the

dispersant has a specific gravity of 1.17, bulk density of .61 g/cm3, and a ph value of

7-9. All cement and additives were provided by Halliburton Energy Services. Distilled

water was used as the cement mixing water since water from different field sources

vary and to avoid any ion interference (API 2013).


polypropylene (PP) fibers: Phase two

3.2.1 PP fibers

PP fibers are excellent candidates in oil well cementing environments due to

their enhanced flexibility and chemical durability, the ability to reduce porosity and

permeability, and the ability to accelerate cement thickening time (Di Maida et al.

2018; Ahmed et al. 2018b; Dopko 2018). However, due to the chemical inertness of


24

PP fibers, the fiber/matrix bond is particularly weak leaving significant potential for

improved bonding characteristics(Coppola et al. 2015). Thus, the fiber/matrix

interaction was improved by surface functionalization of PP fibers through the sol-gel

reaction, which deposits silica (SiO2) nanoparticles on their surface. Nano-silica, a

reactive material with cement, has been extensively used in oil well cement research to

improve cement mechanical properties and slurry impermeability (El-Gamal, Hashem,

and Amin 2017). The physical properties of PP fibers are shown in Table 3.2.

Table 3.2: Properties of PP fibers

Fiber Type OD Length Tensile Strength MOE TS Density

PP (C3H6)n 18 μm 12 mm 557 MPa 4.1 GPa 168°C 0.91 g/cm3

Note: OD-Outer Diameter; MOE- Modulus of Elasticity; TS-Thermal Stability

3.2.2 Sol gel treatment

The materials used for the sol-gel treatment consists of pure ethanol (EtOH,

CH3CH2OH), ammonium hydroxide solution (NH4OH, 28% NH3 in H2O), and

tetraethyl orthosilicate (TEOS, C8H20O4Si, 98%). All chemicals are high purity

reactants purchased from Sigma-Aldrich (USA).


Class “H” oil well cement, bentonite, ANFs, CFR-3, and distilled water were

used to formulate cement slurries.

3.3 Artificial Neural Network (ANN) model development: Phase three

3.3.1 Nanoparticles

Pre-dispersed, in deionized water, nanoparticles solutions of nanosilica (nano-

SiO2), nanoalumina (nano-Al2O3), and nanotitanium dioxide (nano-TiO2) were

purchased from US Research Nanomaterials Incorporated. The physical properties of

the nanoparticle solutions are presented in Table 3.3.


25

Table 3.3: Properties of pre-dispersed nanoparticle solutions

Particle dispersion

type

Particle solution

appearance

Crystal structure

and type

Particle size

(nm)

Particle (wt%)

Water (wt%)

Particle purity

(%)

Nano-SiO2

Translucent

liquid Amorphous 30 25 75 99.9

Nano-Al2O3

Translucent

liquid-white Gamma 30 20 80 99.9

Nano-TiO2

Milky white

liquid Rutile 30-50 20 80 99.9

It is well understood nanoparticles possess a high surface area-to-volume ratio

which behave as a nuclei for cement hydrates and promotes cement hydration

(Jafariesfad, et al. 2017), thereby improving the properties of the composite

cementitious material. For instance, various researchers have concluded nano-SiO2

particles increases the compressive strength of cement, reduces the fluid loss within

cement, reduces the porosity and permeability within cement, reduces channeling

within cement which eliminates gas migration at high temperatures, and reduces the

thickening time of cement (Ershadi et al. 2011; Choolaei et al. 2012; Ridha and

Yerikania 2015; El-Gamal, Hashem, and Amin 2017; Qalandari, Aghajanpour, and

Khatibi 2018; Murthy, Chavali, and Mohammad 2020; Maagi, Lupyana, and Gu

2020). Similarly, nano-Al2O3 particles are effective at increasing the compressive

strength of cement, behaving as accelerators when added to cement, and reducing the

free water of the cement slurry (Campillo et al. 2007; Santra, Boul, and Pang 2012;

Hadi and Ameer 2017; Jian, Xing, and Sun 2019). Nano-TiO2 particles also possess

the ability to increase the compressive strength of cement, increase the nucleation sites

for hydration reactions in cement, and densify the microstructure of cement (Chen,

Kou, and Poon 2012; Teixeira et al. 2016; Maagi, Lupyana, and Gu 2019).


Class “H” oil well cement, CFR-3, and distilled water were used to formulate

cement slurries.


26

4. EXPERIMENTAL PROCEDURES


4.1.1 Morphology of highly bundled ANFs

As received ANF appear as large fiber bundles; a macroscopic view is

provided in Figure 4.1.

Figure 4.1: Macroscopic image of highly bundled ANFs

In order to study the morphology of ANFs, an SEM image of the highly

bundled ANF material, is shown below in Figure 4.2. Upon observation, it is apparent

ANF has a relatively rough exterior. The roughness of the fibers is advantageous

because this increases the fiber/matrix interactions. The fiber roughness essentially

improves the anchorage and frictional bonding between the fiber/matrix interaction

which can potentially lead to an increased amount of hydration products (Akhlaghi,

Bagherpour, and Kalhori 2020). This can ultimately lead to an increase in the overall

strength of the composite material.


27

Figure 4.2: SEM image of highly bundled ANFs

For many applications, ANF should be functionalized in order to improve their

mechanical properties and chemical stability for processing of specific functional

materials. Thus, the highly bundled ANFs must be reduced to powdered form.

4.1.2 ANF size reduction

ANFs should be reduced to powder form and match the size properties shown

in Table 3.1. In order to achieve this size to properly facilitate ANF dispersion

throughout the cement matrix, the fibers were balled milled using an 8000M,

Mixer/Mill (Figure 4.3) for a total of 15 minutes.


28

Figure 4.3: High energy ball mill device

The ball milling device was set to pulverize one gram (1g) of ANFs at 1080

cycles per minute. The final result of this process is shown in Figure 4.4.

Figure 4.4: ANFs after ball milling process


29

4.1.3 Ultrasonication of ANFs

Before mixing the powdered ANFs in the cement matrix, it is imperative to

assess the dispersibility of the material in the aqueous solution (deionized water).

Ensuing the powder conversion, there is an infamous issue of nanofiber

agglomeration. Due to the attractive Van der Waal forces of nanoelements,

agglomerates readily form among nanofibers; especially those possessing high aspect

ratios. Thus, it is an arduous task to formulate a cementitious composite that has a

uniform dispersion of nanoelements throughout the cement matrix. Although, there are

various techniques utilized by researchers for obtaining a homogenous nanofiber

admixture dispersion. The four most common methods are chemical surface

modification, physical surface modification (through surfactants or polymer

wrappings), and mechanical methods of ultrasonication and stirring (Bastos et al.

2016). Among these, ultrasonication is the most frequently used technique for

nanomaterial dispersion (Gkikas, Barkoula, and Paipetis 2012). In this research,

ultrasonication was utilized to characterize ANF dispersions.

Ultrasonication is essentially a method that applies ultrasound energy to agitate

particles in a solution. Researchers typically use an ultrasonic bath or an ultrasonic

probe/horn, known as a sonicator to accomplish this task. Essentially, ultrasound

propagates via compression which results in attenuated waves that pass through the

molecules of the medium. These shock waves promote the “peeling off” of each

nanoparticle located at the outer portion of the nanoparticle bundle, or agglomerate,

which induces separation of each nanoparticle from the bundles. In this research, an

8891, Cole-Parmer ultrasonic bath (Figure 4.5) was used to disperse ANFs throughout

the solution.


30

Figure 4.5: Ultrasonication bath

Essentially, ANF powder is dispersed with deionized water using

ultrasonication and the dispersion was compared against the pre-dispersed 2%

concentrated ANF solution. The dispersion basis for the pre-dispersed solution is

deionized water along with a disintegrator used to formulate the solution. The mixture

is placed in a dispersion container where the maximum ultrasonic sonotrode strength

is 1000 W. It should be mentioned the exact methodology of producing the 2% pre-

dispersed solution is absent due to the company’s trademark restriction. In order to

assess the efficacy of the 2% pre-dispersed solution, the ball milled fiber solution was

compared against the pre-dispersed solution. The optimum solution, between the ball

milled ANFs and the pre-dispersed ANFs, was selected as the nanofiber admixture for

oil well cement testing. Thus, a parametric study was implemented.

One gram (1g) of ANF powder was placed in a 50g solution of deionized water

producing a 2% ANF aqueous concentration. Three different samples were

ultrasonicated for 10 minutes, 30 minutes, and 60 minutes. The ultrasonication bath


31

was set to deliver energy at 210 W for mentioned times. The pre-dispersed solution

was not sonicated. Afterwards, samples were collected in order to conduct a

characterization analysis. Figure 4.6 shows the opacity of the samples after conducting

the procedure.

Figure 4.6: ANF ultrasonication samples from left to right: 10 minutes, 30 minutes, 60

minutes, pre-dispersed solution

4.1.4 Characterization of aqueous ANF solutions through quantitative and

qualitative analysis

ANF aqueous suspensions were characterized quantitatively by ultraviolet–

visible spectrophotometry (UV-Vis) (after 24 hours of preparation) to measure the

absorption intensity of prepared suspensions. UV-Vis is an effective means to

characterize the dispersibility of nanomaterials in aqueous solutions. Essentially, a

UV-Vis light is passed through a sample and the transmittance of light by a sample is

measured. The Beer-Lambert law (shown in the equation below), is used to calculate

the (decadic) absorption intensity of the nanofluid.


32

𝐴 = − log𝐼𝑜

𝐼= ɛ𝑙𝑐

In the above equation, “𝐴” is the absorbance, 𝐼𝑜 is the initial light intensity,

“𝐼” is the light intensity after it passes through the sample, “ɛ” is the molar

absorptivity, “𝑙” is the optical path length of the cuvette in which the sample is

contained, and “𝑐” is the concentration of the nanomaterials in suspension.

Suspensions with lower absorbance indicate a lower amount of dispersed

nanomaterials due to reagglomeration and settling issues. However, suspensions with

higher absorbance indicates a more thorough dispersion with a higher

deagglomeration of nanomaterials (Alshaghel et al. 2018). The Agilent 8453 UV-

Visible spectrophotometer with a wavelength range of 190 to 1100 nm was used to

conduct the analysis. Cuvettes were thoroughly cleaned before conducting the

experiment. The cuvette was filled with the solvent and a blank reading was measured

to ensure minimal absorbance. Due to the large opacity of the ANF samples, serial

dilutions were conducted on all samples. Without diluting the sample, all the light was

absorbed causing a transmittance of zero and an infinite absorption. Samples must be

diluted to ensure a low concentration of ANF since the Beer-Lambert law is only well-

obeyed at lower concentrations (Elkashef, Wang, and Abou-Zeid 2016). Hence, it was

confirmed that samples diluted by a factor of 25 result in ANF contents that are

suitable for UV-vis measurements. Dilutions lower than this value resulted in a

transmittance of zero while higher dilutions were examined to validate the dependence

of absorbance on the ANF concentrations.

In order to conduct a qualitative analysis, transmission electron microscope

(TEM) images were taken. TEM images are one of the three common methods used to

assess nanomaterial morphology and dispersibility (Yazdanbakhsh et al. 2009). In this

case, one gram (1g) of bundled ANFs were ball milled for 25 minutes, placed in the

dispersion container containing 50g of deionized water, and place in the

ultrasonication bath for 10 minutes at an energy delivery of 210 W.

Before taking the TEM images, the ball milled solution and the 2% pre-

dispersed solution were both diluted with deionized water by a factor of 25. Each


33

sample is then sonicated for 10 minutes before observations. The diluted samples are

drop cast onto carbon coated copper grids to dry. TEM images are taken by Hitachi

H8100 at an accelerating voltage of 200 kV. Two samples were prepared to observe

the pre-dispersed solution’s efficacy and to determine if there is any noticeable

difference between the dispersibility of ANFs in aqueous solutions with varying

dispersive methodologies.

4.1.5 Cement slurry preparation

The Ofite model 20 constant speed blender (Figure 4.7), was used to blend all

cement slurry samples for all three phases of this project. The mix water, along with

the respective pre-dispersed solutions, were placed in the mixing cup and blended at a

constant rotational speed of 419 rad/s for 15 seconds. During the 15 second duration,

the dry blended solids were uniformly added to the water. Immediately after adding

the dry blended solids, the lid was placed on top of the container and the rotational

speed was automatically increased to 1257 rad/s for an additional 35 seconds.

Figure 4.7: Ofite model 20 constant speed blender

A slurry volume of 250 ml was poured into the Ofite ultrasonic cement

analyzer (UCA) test cell (Figure 4.8), which was previously thinly greased for ease of

sample removal. The test cell was then placed in the UCA heating jacket and


34

connected to the pressure supply line, with a thermocouple used to measure the

temperature. The UCA is programmed to cure composite samples at user defined

temperatures and pressures. All procedures from the (UCA) manufacturer were

followed to ensure consistent results.

Figure 4.8: Ofite ultrasonic cement analyzer (UCA)

The demolded sample (Figure 4.9) is shaped as a conical frustrum with a top

diameter of approximately 6.48 cm and a bottom diameter of approximately 6.83 cm

(to aid in sample removal). Samples were then re-sized to appropriate dimensions

depending upon the specified test. Three specimens were prepared for each test to

ensure repeatability and the results were averaged. All procedures were done in

accordance with (API 2013).


35

Figure 4.9: Demolded cement sample taken from UCA

4.1.6 Uniaxial compressive strength (UCS) tests preparation

An electric saw was utilized to wet cut demolded samples, with water serving

as the lubricating and cooling fluid, into a sample surface area of 101.6 mm2 and a

height of 48 mm. These dimensions are acceptable to determine the uniaxial

compressive strength (API 2013). To ensure a level surface, samples were surface

grinded.

The Ofite model CLF-40 automated compressive load frame was used to test

for the UCS. The cement specimens were loaded onto the platen as shown in Figure

4.10.


36

Figure 4.10: Cement sample before UCS testing

A constant load of 71.2 kN per minute was applied. The load was released

after cement failure as shown in Figure 4.11.

Figure 4.11: Cement sample after UCS testing


37

Accompanied with Ofite software, the equation below was used to calculate

the UCS. In the equation below, “F” (Newton) represents the instantaneous maximum

force of the specimen at failure, “L” (m) represents the length of the specimen, and

“W” (m) represents the width of the specimen. All cement samples were tested three

times to ensure repeatability and the results were averaged. All tests procedures were

done in accordance with (API 2013).

𝑃𝑐 =𝐹

𝐿 × 𝑊

4.1.7 Splitting tensile (Brazilian tensile) strength tests preparation

A Wilton variable speed drill press (equipped with a 50.8 mm diamond-tip

core drill bit) was used to subsection cement cores into 50.8 mm diameter × 25.4 mm

length cylinders for splitting tensile (Brazilian tensile) strength tests, again using water

for lubrication. A loading rate of 20.68 MPa/min was used, which failed the specimen

between 1 and 10 min. The procedures were performed according to (ASTM 2016),

considering there are no standard American Petroleum Institute (API) procedures to

determine tensile strength. Figure 4.12 displays a result of the process. Three

specimens were prepared for each test to ensure repeatability and the results were

averaged.

Figure 4.12: Cement sample after splitting tensile strength failure


38

4.1.8 Cement slurry rheological properties

The workability of the cement composite was assessed by examining the

rheological properties. Testing of the rheological properties is vital for simulating

pump profiles and flow regimes. It is also important for measuring the viscosity which

should provide good pumpability to reduce the horsepower necessary to overcome

friction losses during the cement job (Nelson and Guillot 2006; Sarmah et al. 2016).

Tests were performed at 21.6°C and atmospheric pressure 101.35 KPa. Tests

were conducted according to (API 2013). The cement slurry is represented by the

Bingham plastic model presented in the equation below.

𝜏 = 𝜏𝑦 + 𝜇𝑝̇

In the above equation, “𝜏” is the shear stress (Pa), 𝜏𝑦 is the yield point (Pa) ,

𝜇𝑝 is the plastic viscosity (Pa·s), and ̇ is the shear rate (s-1). The yield point is defined

as the minimum stress required to deform the fluid flow. Below the yield point the

fluid behaves as an elastic solid and above the yield point fluid flows with a plastic

viscosity. The plastic viscosity is essentially the resistance of fluid flow once the yield

point is exceeded (Clark, Sundaram, and Balakrishnan 1990). The gel strength was

also tested which is a measure of the attractive forces between particles in a fluid

under static or non-flow conditions (Shahriar and Nehdi 2012). The slurry was

preconditioned at 511.5 s-1 for one minute (1 min) to disperse the gel already formed.

Afterwards, the slurry remained static for 10 s and the maximum deflection was

recorded by applying a shear rate of 5.1 s-1. The reading was used to calculate the 10 s

gel strength. Subsequently, the slurry remained static for 10 min with the shear rate

again applied at 5.1 s-1 after the elapsed time. The reading was used to calculate the 10

min gel strength. The measurements were taken using a couette coaxial cylinder

rotational viscometer (Figure 4.13) equipped with a transducer to measure the induced

angle of rotation of the bob by the fluid sample. All procedures were repeated three

times with a freshly prepared cement slurry and the results were averaged.


39

Figure 4.13: Couette coaxial cylinder rotational viscometer

4.1.9 Cement slurry stability tests preparation

Free fluid and sedimentation tests were conducted at 21.6°C at atmospheric

pressure 101.35 KPa in order to measure the stability of alumina nanofibers in class

“H” cement slurry. Free fluid can develop with minimal sedimentation, and

sedimentation can occur without free fluid forming. Therefore, both free fluid (Figure

4.14) and sedimentation (Figure 4.15) values are measured. Excessive free fluid and

sedimentation are considered injurious to the quality of the cement sheath (API 2013).


40

Figure 4.14: Free water development in cement

Figure 4.15: Sedimentation development in cement

The free fluid test requires pouring a freshly mixed cement slurry into a

graduated cylinder at 250 ml. The cylinder is then sealed to prevent evaporation and

the amount of free fluid is measured after two hours. The test was conducted three

times with the average of the experimental values reported.


41

The sedimentation test requires placing freshly mixed cement slurry inside a

tube length of 200 mm with an inner diameter of 25 mm. The slurry is first poured 20

mm from the top of the tube in which the slurry is paddled to dislodge any air bubbles.

Afterwards, the tube is completely filled. A top cover is placed on the test tube to

prevent evaporation. After a 24-hour curing period, a mark is placed 20 mm from the

top and bottom of the sample. The sample is then divided into four equal sections as

shown in Figure 4.16.

Figure 4.16: Sedimentation cement sample before wet cutting

The samples are then wet cut at each of the marks. Using Archimedes

Principle, the relative density of each of the four sections are calculated by dividing

the dry mass of each specimen by the mass of the specimen submerged in a beaker of

water. The first specimen is used as the reference density and the remaining percent

density differences are calculated thereof. This process was conducted three times

with the average of the experimental values reported.

4.1.10 Thickening time tests preparation

The thickening time provides an indication of the length of time that a cement

slurry will remain pumpable in a well. The consistency or pumpability of the slurry is

measured in Bearden units of consistency (Bc). Each slurry sample is placed in a


42

slurry cup assembly which is then placed in the Automated HTHP (high temperature

high pressure) consistometer (Figure 4.17).

Figure 4.17: Automated HTHP (high temperature high pressure) consistometer

The slurry cup assembly is rotated at a speed of 150 r/min at elevated

temperature and pressure conditions. The torque required to rotate the paddle is

measured, the time at which the torque reaches 70 (Bc) units is taken as the thickening

(Umeokafor and Joel 2010). The HTHP consistometer software was used to facilitate

the test in which the static bottom hole temperature and static bottom hole pressure

was set to 82.2°C and 34.47 MPa, respectively. The experiment was conducted three

times with the average value reported.

4.1.11 Cement permeability test preparation

After demolding cement specimens from the UCA, 38.1mm diameter cement

cores were sub sectioned using a Wilton variable speed drill press with a 38.1 mm

diamond-tipped core drill bit producing cylindrical specimens. Specimens were then

placed in a vacuum oven at 100°C until there was ≤ 0.1% weight variation over 24

hours.


43

NER AutoLab 1500 (New England Research, Inc., White River Junction, VT,

USA) (Figure 4.18), equipped with a data acquisition system, was used to calculate the

permeability of all composite formulations under cyclic confining pressure schedules.

Figure 4.18: (NER) AutoLab 1500 system for ultrasonic velocity and permeability

measurements

After sample jacketing and preparation, the sample was placed in the high

pressure (HP) vessel mounted on a base plug (Figure 4.19). An electronic console

along with three pressure intensifiers were used to accurately control confining

pressure.


44

Figure 4.19: Assembled sample for permeability measurements

The confining pressure was initiated at 6.89 MPa using the panel mode on the

electronic console. The vent valve was opened with nitrogen, an inert gas, allowing

the pore pressure intensifier to be loaded with gas. The permeability specifications

were loaded into the data acquisition system and the panel mode was diverted to

computer mode, allowing better control of the confining and pore pressures. The pore

pressure valve was opened allowing gas to flow in the sample from the upstream side.

After the downstream pressure is equal to the upstream pressure (approximately 3.45

MPa) the permeability measurements commenced.

The permeability was measured using the transient step method developed by

(Brace, Walsh, and Frangos 1968). During every measurement the confining pressure

was maintained at a specified value in order to simulate wellbore conditions. A

pressure pulse (represented by the equation below) was introduced into the upstream

volume and the pressure in the downstream volume is recorded.

𝑝|𝑥=0 = 𝑝(𝑡)

The pressure is recorded using the equation below. Based upon the

downstream pressure pulse the permeability can be derived.


45

𝜕𝑝

𝜕𝑡=

𝑄

𝛽𝑉

The equation below, is the partial differential equation that governs pressure

changes as a function of distance and time through the composite material.

𝜕2𝑝

𝜕𝑥2+ 𝛽′ (

𝜕𝑝

𝜕𝑥)

2

=𝛽′𝜇

𝑘

𝜕𝑝

𝜕𝑡

"𝑝" is the pressure within the sample (MPa), "𝑥" is the axial distance measured

from the upstream face of the sample (m), "t" is the time (s), "𝑄" is the flow rate

through the sample (m3/s), "𝛽" is the compressibility of the fluid (N-1·m2), "𝑉" is the

downstream volume (m3), 𝛽′ is the lumped compressibility (N-1·m2), "𝜇" is the pore

fluid viscosity (Pa·s), and "𝑘" is the permeability (Darcy). Only a brief mathematical

description of the transient step method is provided. The mathematical details can be

found elsewhere (Brace, Walsh, and Frangos 1968; Bredehoeft and Papadopulos 1980;

Evans and Wong 1992; Roy et al. 1993). Beginning at 6.89 MPa the confining

pressure was increased to 20.68 MPa in 3.45 MPa increments and finally decreased in

the same manner with the permeability measured at each step. This was done twice,

totaling two full pressure cycles.

4.1.12 Cement elastic properties test preparation

The same procedures used to produce the cement samples for permeability

testing, were used to test the elastic properties.

NER AutoLab 1500 was also used to calculate the dynamic elastic properties

MOE (E) and Poisson’s Ratio (𝜈) under cyclic confining pressure. An ultrasonic

system generated shear pulses (𝑉𝑠 waves) and compressional pulses (𝑉𝑝 waves)

propagating through the specimen jacketed in a HTHP rubber sleeve using a source

and receiver transducer with wired connections (Figure 4.20).


46

Figure 4.20: Assembled cement sample for ultrasonic velocity measurements

The equation below was used to calculate the bulk density (𝜌) (g/cm3) where

"𝑚" is the mass (g), "𝑟" is the radius (cm), and "ℎ" is the height measured in (cm).

𝜌 = 𝑚

𝜋𝑟2ℎ

The specimen was assembled in the high pressure (HP) vessel mounted on a

base plug. The confining pressure was initiated at 6.89 MPa then increased to 20.68

MPa in 3.45 MPa increments and finally decreased in the same manner for two cycles.

Both velocities (𝑉𝑃 and 𝑉𝑠) (m/s) were measured at each confining pressure resulting

in the final calculation of MOE (𝐸) and Poisson’s Ratio (𝜈). The equations for both

"𝐸" and "𝜈" are presented below.

𝐸 = ƿ𝑉𝑆

2(3𝑉𝑃2 − 4𝑉𝑆

2)

𝑉𝑃2 − 𝑉𝑆

2

𝜈 = 1 − 2(𝑉𝑆/𝑉𝑃)2

2[1 − (𝑉𝑆/𝑉𝑃)2]


47

4.1.13 Cement phase transformations

X-ray diffraction (XRD) analysis was conducted to identify polycrystalline

phases of hardened cementitious composites through the recognition of X-ray patterns

that are unique to cement crystalline phases. At the conclusion of uniaxial

compression tests, cement fragments were taken from the centroid and ground into

fine powder (10-20 μm) using a mortar and pestle. Cement hydration was arrested by

grounding in an organic solvent (isopropyl alcohol) and rinsing with diethyl ether,

afterwards samples were dried in a vacuum oven at 105°C (Singh and Rai 2001;

Schöler et al. 2015; Jeong et al. 2017). A powder X-ray diffraction analysis was

performed immediately afterwards using a Rigaku Ultima III powder diffractometer in

a 𝜃 − 2𝜃 configuration with CuΚα (1.5418 Å) radiation (anode voltage 40 kV and 44

mA). The incident X-ray beam was modified using the Rigaku Cross Beam Optics

system to create a parallel beam geometry. Diffraction intensities were recorded on a

scintillation detector after being filtered through a Ge monochromator. The patterns

were obtained for specimens using a 2θ range of 3 – 90°. In all instances the data

collection rate was set at 3.5 min/° with a step width of 0.02°. The diffraction patterns

were analyzed using the software JADE v9.1 in combination with PDF 4+ (2019

version).

4.1.14 Degree of hydration (DOH) of cement

The thermogravimetric analysis (TGA) was executed using the Mettler Toledo

TGA/SDTA851e Module (Figure 4.21) in order to calculate the degree of hydration

(DOH).


48

Figure 4.21: Mettler Toledo TGA/SDTA851e Module

At the conclusion of uniaxial compression tests, cement fragments were taken

from the centroid and ground into fine powders with a mortar and pestle while

evaporation was minimized. Approximately 30 mg of powder was transferred into the

TGA chamber for measurement. The experiment was conducted by placing the

specimen in the chamber at ambient temperature then increasing the temperature to

140°C by 20°C/min in a purged nitrogen environment. This is the critical temperature

for evaporable water as reported in the literature (Pane and Hansen 2005). This

temperature was held constant for a total of 25 min to remove evaporable water in the

specimen. Afterwards, the specimen was heated from 140°C to 1100°C at a rate of

20°C/min which is necessary to extract all chemically bound water (CBW). The data

was normalized with the samples weight at 140°C taken as the baseline for

measurements.


polypropylene (PP) fibers: Phase two

4.2.1 Deposition of SiO2 nanoparticles on PP fibers

Surface functionalization of PP fibers (Figure 4.22) was executed by

depositing SiO2 nanoparticles on the surface of PP fibers, by means of the sol-gel


49

process, based upon previous studies (Pinto et al. 2008; Yang et al. 2011; Raabe et al.

2014).

Figure 4.22: Micro-synthetic polypropylene (PP) fibers

The sol-gel process constitutes the synthesis of a colloidal suspension of solid

particles (sol) in a liquid (gel). Typically, the sol-gel process is divided into two steps:

the production of hydroxyl groups (hydrolysis) and the polycondensation of hydroxyl

groups along with residual alkoxyl groups (condensation). As the reaction continues,

polymerization occurs creating large molecules containing silicon. One gram of PP

fibers was immersed in a 250 ml flask containing EtOH (85 mL) as the solvent,

distilled water (9 mL), and NH4OH (1.5 mL) as the basic catalyzer for 10 minutes at

60°C. TEOS (4.5 mL), which is the metal alkoxide precursor, was then gradually

added into the solution under magnetic stirring. PP fibers were removed after two

hours, washed with clean water, and dried at room temperature (21°C). The

morphology and distribution of SiO2 nanoparticles on the surface of PP fibers was

assessed using scanning electron microscopy (SEM) performed with Zeiss crossbeam

540 (ZEISS, German). All images were acquired on the PP fibers surface in high

vacuum with an In-lens SE detector at an accelerating voltage of 3 kV. Fibers were

coated with gold/palladium (Au/Pd) before imaging for image enhancement. Chemical


50

elemental analysis was carried out with an Oxford energy-dispersive X-ray

spectroscopy system (EDX) equipped on the Zeiss microscope.


The slurry was prepared with the same procedures as section 4.4.5. All cement

samples were cured at 82.2°C with 20.68 MPa for 24 hours.

4.2.3 Cement mechanical property tests

Cement specimens prepared to test for the UCS, splitting tensile strength,

MOE, and Poisson’s Ratio were prepared and tested in accordance with sections 4.4.6,

4.4.7, and 4.4.12 respectively.

4.3 ANN model development: Phase three

4.3.1 Assessment of pre-dispersed nanoparticle solutions

The efficacy of the pre-dispersed nanoparticle solutions was assessed by TEM

images. Solutions were diluted by ethanol in 1:100 volume ratios. The diluted

solutions were then sonicated for 10 minutes and drop cast onto copper grids until

drying was complete. After drying, images were taken by Hitachi 7650 at an

accelerating voltage of 60 kV.


After blending cement slurries in the Ofite model 20 constant speed blender,

the slurry is immediately poured into previously thinly greased 50.8 mm cube brass

molds (Figure 4.23). The molds were thinly greased to make removal of the cement

samples easier after the curing period. The slurry is filled to approximately one-half of

each mold’s depth, puddled 25 times each, and stirred to remove segregation and

dislodge any air bubbles. Afterwards, the remaining one-half of the molds were filled

and the puddling process was repeated for each specimen.


51

Figure 4.23: Brass cement mold assembly

All cement specimens were cured for 24 hours in an isothermal water bath

(Figure 4.24) at atmospheric pressure. At 45 min prior to the 24-hour curing duration,

cement specimens were demolded and immediately immersed in a water-cooling bath

at room temperature to slowly cool. This is done to minimize the cement’s defects

from thermal shock, prevent cement shrinkage due to dehydration, and abate the

formation of cracks.

Figure 4.24: Isothermal water bath


52

4.3.3 UCS tests preparation

Cement specimens prepared to test for the UCS were tested in accordance with

section 4.4.6. In total 195 cement specimens were tested with varying dosages of

nanoparticles. Nanoparticles were added to a maximum concentration of 0.3%

BWOC. All cement specimens were tested three times to ensure repeatability and the

results were averaged. The results of these tests are provided in the appendix (Table

10.1).


53

5. RESPONSE SURFACE METHOD (RSM) THROUGH THE

DESIGN OF EXPERIMENTS (DOE)

The DOE methodology entails planning experiments appropriately so data can

be collected and analyzed by statistical methods; this allows the conclusion of

objective and meaningful results. Considering the hybrid mixture design of ANFs and

PP fibers is a multi-variable and multi-objective optimization formulation, RSM is

deployed. RSM is a statistical modeling technique used for analyzing problems in

which several independent variables influence a response of interest and the objective

is to optimize the response (Montgomery 2017; Dean, Voss, and Draguljić 2017).

When attempting to derive the functional relationship between experimental

variables and the response, the most commonly adopted method of RSM is the central

composite design (CCD). CCD is a factorial design composed of 2𝑘 factorial with 𝑛𝐹

factorial runs, 2𝑘 axial runs (star runs), and 𝑛𝑐 center runs; whereby "𝑘" represents the

factors (independent variables). In this project the factors are ANF and PP fibers with

responses as UCS, tensile strength, MOE and Poisson’s Ratio. For each CCD

developed, a total of 13 experimental runs were conducted comprising of four factorial

runs, four axial runs, and five center runs. Generally, 3-5 center runs are

recommended; the replication tests for model lack of fit and pure error. A CCD is

rotatable if the variance of the predicted response is equal for all coded points at any

given distance from the center of the design. Essentially, a design is made rotatable

according to the calculation of "𝛼" (as shown below).

𝛼 = (𝑛𝐹)1/4

According to above equation, "𝛼" has a value of 1.414. In a CCD, design

variables are typically rescaled to coded variables whereby zero is the center of the

design and ±1 are factorial coded levels. Figure 5.1 displays the CCD for the

described problem.


54

Figure 5.1: Central composite design (CCD) for k = 2 factors

Based upon preliminary studies and extant literature, ANF was added to a

maximum concentration of 0.2% BWOC (Muzenski, Flores-Vivian, and Sobolev

2019; McElroy et al. 2019; McElroy, Emadi, and Unruh 2020) and PP fibers were

added to a maximum concentration of 0.5% (BWOC) (Nelson and Guillot 2006; Ede

and Ige 2014; Ahmed et al. 2018a). Increasing the dosage of ANF past 0.2% (BWOC)

is deleterious for the cement composite due to agglomeration and irregularities in the

hydration microstructure causing a decrease in mechanical properties. Likewise,

increasing the PP fiber dosage past 0.5% (BWOC) causes a decrease in mechanical

properties. Higher PP fiber dosages can also become arduous to mix in cement slurries

developing “fur balls” which tend to plug pump plungers and float equipment.

Additionally, preliminary tests were conducted to determine the minimum

concentration values to perform the analysis in the optimum design region. ANF was

added to a minimum concentration of 0.03% and PP fibers were added to a minimum

concentration of 0.09%. Lower concentrations resulted in exceedingly dismal


55

performances for all responses. All cement slurry specimens were prepared with a

W/C ratio of 0.55 and were designed according to the CCD method. Each cement

composite formulation is shown in Table 5.1.

Table 5.1: Cement hybridization mixture proportions

Run

Number

Factors (% BWOC) grams for 250 mL cement slurry

ANFs PP fibers Cement Water Bentonite CFR-3 ANFs PP

fibers

1 0.03(-1) 0.09(-1) 280 154 8.40 1.12 0.08 0.25

2 0.20(1) 0.09(-1) 280 154 8.40 1.12 0.56 0.25

3 0.03(-1) 0.50(1) 280 154 8.40 1.12 0.08 1.40

4 0.20(1) 0.50(1) 280 154 8.40 1.12 0.56 1.40

5 0.00(-1.41) 0.30(0) 280 154 8.40 1.12 0.00 0.84

6 0.24(1.41) 0.30(0) 280 154 8.40 1.12 0.67 0.84

7 0.12(0) 0.00(-1.41) 280 154 8.40 1.12 0.34 0.00

8 0.12(0) 0.58(1.41) 280 154 8.40 1.12 0.34 1.62

9 0.12(0) 0.30(0) 280 154 8.40 1.12 0.34 0.84

10 0.12(0) 0.30(0) 280 154 8.40 1.12 0.34 0.84

11 0.12(0) 0.30(0) 280 154 8.40 1.12 0.34 0.84

12 0.12(0) 0.30(0) 280 154 8.40 1.12 0.34 0.84

13 0.12(0) 0.30(0) 280 154 8.40 1.12 0.34 0.84

Since the optimum responses are in the vicinity of the defined region, a second

order polynomial model (shown below) was used to calculate each response.

𝑦 = 𝛽0 + ∑ 𝛽𝑖𝑥𝑖 +

𝑘

𝑖=1

∑ 𝛽𝑖𝑖𝑥𝑖2 + ∑ ∑ 𝛽𝑖𝑗𝑥𝑖𝑥𝑗 + ɛ

𝑗=1

𝑖=1

𝑘

𝑗=2

𝑘

𝑖=1

"𝑦" represents the predicted response for each composite formulation, 𝑥𝑖 and 𝑥𝑗

represent the experimental factors, 𝛽0 is the y-intercept of the model for which case

𝑥1 = 𝑥2 = 0, 𝛽𝑖 represents the linear coefficients, 𝛽𝑖𝑖 represents the quadratic

coefficients, 𝛽𝑖𝑗 represents the interaction (cross product) coefficients, and ɛ is the

residual model error. The second order polynomial model was fitted by the Design

Expert® software version 12.0.6.0 multivariate regression method. The interaction

between factors and the responses are investigated using analysis of variance

(ANOVA).


56

6. ARTIFIAL NEURAL NETWORK (ANN)

6.1 Background

ANNs are information processing systems that attempt to simulate, using

computational models, the human biological nervous system that is comprised of

many nerve cells (neurons) connected in a complex network (Figure 6.1) (Ciaburro

2017).

Figure 6.1: Representation of a biological neural network

In the biological nervous system, each neuron (termed node in ANN systems)

is connected, on average, with tens of thousands of other neurons totaling hundreds of

billions of connections. Intelligent behavior emerges from the interactions between the

vast number of interconnected units. Although significantly smaller than the human

biological nervous system, ANNs exhibit the ability to mimic the capabilities of a

human brain.

The feed-forward multilayer perceptron network is the most commonly used

ANN architecture with numerous nodes distributed into three or more layers. The

layers consist of an input layer, one or more hidden layers, and an output layer. In each


57

layer, all the nodes are interconnected with the conversion of data occurring through

nonlinear techniques.

The backpropagation (BP) algorithm is one of the most ubiquitously used

training methods in a feed-forward multilayer network. The BP algorithm consists of

the forward stage and the backward stage. In the forward stage, the input data passes

from the input layer to the hidden layer. A weighted sum is calculated by each node in

the hidden layer according to the equation below.

net𝑗 = ∑ 𝑤𝑖𝑗𝑥𝑖

𝑛

𝑖=1

+ 𝑏𝑗

net𝑗 is the weighted sum of the jth neuron in the upper layer, “𝑛” is the number

of nodes in the lower layer, 𝑤𝑖𝑗 is the connective weight between the ith neuron in the

lower layer and the jth neuron in the upper layer, 𝑥𝑖 is the input value of the ith neuron

in the lower layer, and 𝑏𝑗 is the bias value of the jth neuron in the upper layer.

Typically, the result of net𝑗 is passed through a nonlinear activation function such as

the sigmoid function (shown in the equation below). This results in output value of the

jth neuron, 𝑜𝑗, in the upper layer.

𝑜𝑗 = 𝑓(net𝑗) = 1

1+𝑒−net𝑗

In the backward stage, gradient descent techniques are used to minimize the

error between the experimental and predicted values. This is accomplished by

adjusting the weights, biases, and hyperparameters of the ANN model by propagating

backward from the output layer to the input layer (Figure 6.2). The process is repeated

until the network error converges to an acceptable minimum value (Shanmuganathan

2016).


58

Figure 6.2: Schematic diagram of typical artificial neural network

6.2 ANN architecture

In this research, the developed ANN network consists of five nodes in the

input layer, one hidden layer, and one node in the output layer. The sigmoid activation

function was used in the hidden layer, and the linear activation function was used in

the output layer. This combination allows for the network to learn the nonlinear and

linear relationships between the input and output vectors. Rather than the commonly

used BP method, the Levenberg-Marquardt backpropagation (LMBP) algorithm was

used to train the ANN model. The LMBP algorithm is an iterative technique that

locates the minimum of a function that is expressed as the sum of squares of nonlinear

functions. Essentially, it is a hybrid optimization technique that combines the

advantages of the steepest descent algorithm and the Gauss-Newton method (Lourakis

2005). LMBP is also considered the fastest BP algorithm (Zhou, Wang, and Zhu

2016). In order to simulate the ANN model, MATLAB (R2019b) software was used to

write the computer program code. The developed model used 70% (137 examples

from the data set) of the data for training, 15% (29 examples from the data set) for

validation, 15% (29 examples from the data set) to test the model, and a maximum of

10,000 epochs (iterations).

Unfortunately, determining the number of nodes in the hidden layer is not a

straightforward process. This is a type of model selection (model comparison)

problem. Thus, the number of nodes in the hidden layer are determined by trial and

error. A trial and error approach was implemented by changing the number of nodes in


59

the hidden layer from 1 to 20 in increments of 1. A suitable ANN architecture is a

model that possesses the least mean squared error (MSE) (shown in the equation

below) between the target (experimental) and predicted outputs of the data set.

MSE =1

𝑁∑(𝑡𝑗 − 𝑝𝑗)

2𝑁

𝑗=1

This process is repeated by reanalyzing the network using different sets of

random weights and biases. Thus, the network with the least MSE was selected. The

accuracy of the selected network was assessed by examining the MSE, the mean

absolute percentage error (MAPE), and the square root of the coefficient of

determination (R2). The equations for the MAPE and the square root of the coefficient

of determination are shown below.

MAPE = (100

𝑁) ∑ |

𝑡𝑗 − 𝑝𝑗

𝑡𝑗

|

𝑁

𝑗=1

R = √R2 = √1 −

∑ (𝑡𝑗 − 𝑝𝑗)2𝑁

𝑗=1

∑ (𝑡𝑗 − 𝑡̅)2

𝑁

𝑗=1

“𝑁” is the number of respective data set examples, 𝑡𝑗 is target value of the jth

data set example, 𝑝𝑗 is the predicted value of the jth data set example, and “𝑡̅” is the

average of the target values.


60

7. RESULTS AND DISCUSSION


7.1.1 UV-vis spectra characterization of ANF solutions (quantitative

analysis)

Figure 7.1 shows the absorption spectra of deionized water, as a solvent, with

suspended ANF samples sonicated at 60 minutes, 30 minutes, 10 minutes, and the pre-

dispersed sample all diluted by a factor of 25. It is evident the pre-dispersed solution

has the highest absorption among the other samples.

Figure 7.1: Absorption spectra of sonicated samples at 10 minutes, 30 minutes, 60

minutes, and the pre-dispersed solution

To validate the dependence of the absorbance on the ANF concentration, the

ANF pre-dispersed solution was diluted with deionized water at various

concentrations. “C” is the initial concentration with variations consisting of C/25,

C/50, C/100, and C/200. Figure 7.2 displays the results of the aforementioned process.

For each concentration, an absorption peak around 227 nm wavelength is identified

through the spectra pattern. After the absorption peak is reached, the absorbance

decreases as the wavelength increases as a monotonic sequence. The peak wavelength

200 300 400 500 600 700 800

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

Abso

rban

ce (

a.u.)

Wavelength (nm)

Pre-Dispersed D_25

S60m D_25

S30m D_25

S10m D_25


61

and the spectra pattern correspond to the results previously obtained by (Piriyawong et

al. 2012; Adio, Sharifpur, and Meyer 2016) whereby γ-Al2O3 nanoparticles where

dispersed in deionized water by pulsed laser ablation and the effect of ultrasonication

energy on Al2O3-glycerol nanofluids was observed.

Figure 7.2: Typical absorption spectra of deionized water with different suspended

ANF concentrations for the pre-dispersed solution

It can be concluded that ANF absorbance decreases when the dilution factor

increases, affirming Beer-Lambert’s Law. Figure 7.3 shows the plot between ANF

concentration versus the absorbance. Similar results were also obtained for sonicated

samples at 60 minutes, 30 minutes, and 10 minutes.

200 300 400 500 600 700 800

227 nm

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

Abso

rban

ce (

a.u.)

Wavelength (nm)

C/25

C/50

C/100

C/200


62

Figure 7.3: Linear relationship between the absorbance and the concentration at

wavelength 227 nm

7.1.2 TEM characterization of ANF solution (qualitative analysis)

Figure 7.4 displays the ANFs that were prepared for TEM analysis as

described in section 4.1.4. It is evident there are various zones of nanofiber

agglomeration throughout the dispersion, examples are enclosed in hashed red shapes.

It is also evident that the ball milling procedure was not effective in producing ANFs

in the appropriate size range (Table 3.1) in various areas, examples are enclosed in

yellow circles.

C/25C/50C/100C/200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Abso

rban

ce (

a.u

.)

Concentration (a.u.)

Absorbance


63

Figure 7.4: Ball milled dispersed solution

However, the 2% pre-dispersed solution (Figure 7.5) displays good

dispersibility with ANFs indicative of the size in Table 3.1. These results indicate the

ball milling procedure is chaotic and modifications are necessary to achieve the

appropriate size range. Furthermore, without proper sonication ANFs can easily

agglomerate, meaning the ultrasonication process should also be modified. (Reches et

al. 2018) also experienced suspended -Al2O3 nanoparticle agglomeration without

proper sonication. These results affirm the notation that without proper preparation

and ultrasonication methods, nanofibers with large aspect ratios and Van der Waals

forces cause significant agglomeration. Essentially the 2% pre-dispersed solution

displayed an exceptional dispersibility and was used for the remainder of the cement

experiments.


64

Figure 7.5: 2% pre-dispersed solution

7.1.3 Strength properties of cured cements samples reinforced with ANFs

The first batch of cement samples were cured in the UCA at 82.2°C with 34.47

MPa. With average well depths at approximately 3596.64 m according to Reliant

Energy (Cookson 2013), these pressure and temperature conditions are indicative of

the Permian Basin. The Permian Basin is the second largest onshore oil field in the

world and the largest in the U.S. Samples were cured for a duration of eight hours and

24 hours to assess strength development. Table 7.1 displays the cement batch

compositions (with a constant W/C ratio of 0.45) and the identifiers thereof. Cement

formulations were analyzed at varying ANF concentrations consisting of 0.10%,

0.20%, 0.30%, and 0.40% (BWOC). There were cement specimens prepared with no

ANFs having (CFR) as the only additive. This was done in order to conduct a

parametric analysis.


65

Table 7.1: Cement batch compositions (batch one)

Composite Identifier Cement (g) Water (g) ANF (g) CFR-3 (g) ANF/cement wt. %

CFR02 320 144 0 0.64 0

CFR02PDANF01 320 144 0.32 0.64 0.10

CFR02PDANF02 320 144 0.64 0.64 0.20

CFR02PDANF03 320 144 0.96 0.64 0.30

CFR02PDANF04 320 144 1.28 0.64 0.40

Figure 7.6 displays the results of the UCS of cement specimens that were cured

for 8 and 24 hours. It can be observed that the UCS significantly increases after the

addition of 0.1% ANF (CFR02PDANF01) at 8 and 24 hours. Indeed, the addition of

0.1% ANF possesses the greatest compressive strength among all the prepared cement

batches at both time frames. Cement sample (CFR02) has an average compressive

strength of 17.1 MPa at 8 hours and 21.6 MPa at 24 hours. Cement sample

(CFR02PDANF01) has an average compressive strength of 21.9 MPa at 8 hours and

32.5 MPa at 24 hours. Conversely, there is a 28% and 50% increase in compressive

strength at 8 hours and 24 hours between batches with no ANF and 0.1% ANF.

Figure 7.6: UCS of cement samples at 8 and 24 hours (batch one)

Similarly, Figure 7.7 displays the addition of 0.1% ANF (CFR02PDANF01) at

8 and 24 hours increases the tensile strength of the cement composite most effectively.

Cement sample (CFR02) has an average tensile strength of 2.392 MPa at 8 hours and

2.66 MPa at 24 hours. Cement sample (CFR02PDANF01) has an average tensile

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

CFR02 CFR02PDANF01 CFR02PDANF02 CFR02PDANF03 CFR02PDANF04

Com

pre

ssiv

e S

tren

gth

(M

pa)

8-hour compressive strength

24-hour compressive strength


66

strength of 2.609 MPa at 8 hours and 4.08 MPa at 24 hours. There is a 9% and 53%

increase in tensile strength at 8 hours and 24 hours between batches with no ANF and

0.1% ANF.

Figure 7.7: Tensile strengths of cement samples at 8 and 24 hours

The strength gain from all the experimental composites is primarily a result of

the hydration products at elevated temperatures. Hydration is a function of

temperature which affects the kinetics and mechanisms of hydration. Cement slurries

cured at temperatures above ambient conditions result in a hydration acceleration

effect leading to a higher hydration degree and subsequently, producing a more

condensed composite with higher compressive strength. The difference is ANFs act as

a nucleation site in cement composites, with hydration products (mainly C-S-H)

forming around the nanofibers providing nano-reinforcement. Essentially when

nanocracks begin to form at the C-S-H level, ANFs can impede their progression due

to the “bridging effect” in the elastic region. In particular, a concentration of 0.10%

ANF BWOC results in the largest strength gain for both the UCS and the tensile

strength of cement samples. This dramatic increase is crucial considering the fleeting

wait on cement (WOC) time. Nanofibers essentially bridge the gap between pores and

nanocracks, thereby transferring much of the load that would be applied to the cement

matrix to the fibers. This essentially delays the formation of cracks coalescing into

microcracks and macrocracks which eventually results in ultimate failure.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

CFR02 CFR02PDANF01 CFR02PDANF02 CFR02PDANF03 CFR02PDANF04

Ten

sile

Str

ength

(M

pa)

8-hour tensile strength

24-hour tensile strength


67

Increasing the amount of ANF beyond 0.1% for both the compressive and

tensile failure tests introduces ANF clustering throughout the cement matrix. ANF is

not effectively bonding to the cement particles because there is an excess amount of

nanomaterial that leads to clumping. (Noordin and Liew 2010) discussed a similar

phenomenon when a high loading rate of alumina nanofibers decreased the strength

properties of the polymer polyaniline due to clustering. Essentially, entangled clumps

of nanomaterial form in the cement nanopores that create weak zones in the

cementitious matrix. Cement is not able to bond to other cement hydration products or

ANFs due to the blockage caused by ANF clustering. This ultimately lowers the

strength properties of the cement.

In order to expound upon the results, a second batch of cement samples were

cured with the inclusion of bentonite. The intent was to analyze the differences in

strength properties with a slightly lower density cement. In the second batch of cement

samples, samples were cured at 82.2°C with 20.68 MPa for 24 hours. All cement

slurry specimens were prepared with a W/C ratio of 0.55. Plain cement batches,

without any ANFs, were cast as a reference. Three different batches of varying ANF

concentrations consisting of 0.10%, 0.20%, and 0.30% BWOC were also prepared.

Table 7.2 displays the experimental cement compositions.

Table 7.2: Cement batch compositions with bentonite (batch two)

Composite

Identifier

Cement

(g)

Water

(g)

ANF

(g)

CFR-3

(g)

Bentonite

(g)

ANF/cement

(wt. %)

Ref 800 440 0 1.50 20.0 0

ANF-1 800 440 0.80 1.50 20.0 0.10

ANF-2 800 440 1.60 1.50 20.0 0.20

ANF-3 800 440 2.40 1.50 20.0 0.30

Figure 7.8 illustrates the variation of compressive strength values with the

cement formulations presented in Table 7.2. According to the graph it is evident that

composition ANF-1, containing 0.1% ANF by weight of cement (BWOC), possesses

superior strength gain at 25.6 MPa compared to the Ref composite at 17.8 MPa. This

estimates to a 44% increase in uniaxial compressive strength. The results are similar to

the UCS results in Figure 7.6. ANFs are able to increase the UCS of cement samples

even after slightly lowering the density of the composite material.


68

Figure 7.8 also illustrates a decrease in strength gain, compared to ANF-1, with

composites ANF-2 and ANF-3 at 23.7 MPa and 22.7 MPa respectively. Again, this

strength decrease is due to nanofiber clustering with higher dosages of nanomaterial.

This result affirms the belief that at high loading of nanofibers, there is difficulty in

dispersing the material uniformly throughout the composite material due to Van der

Waals forces.

Figure 7.8: UCS of cement samples (batch two)

7.1.4 Cement slurry rheological behavior

Figure 7.9 shows the rheology for the prepared cement specimens. There is

low disparity of the rheological performance between cement batches and the flow

curves almost resemble a straight line. The yield point, plastic viscosity, and linear

regression value for each cement batch is shown in Table 7.3. The intercept of the

linear regression line corresponds to the yield point while the slope represents the

plastic viscosity. Essentially, the results indicate there are no significant alterations in

rheology when well dispersed ANFs are added in the cement matrix. Additionally,

Table 7.3 also indicates that as the concentration of ANF increases the gel strength

also slightly increases. This is true for both the 5 s and 10 min gel strength tests. This

17.8

25.623.7

22.7

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Ref ANF-1 ANF-2 ANF-3

Com

pre

ssiv

e S

tren

gth

(M

pa)

Composite Identifier


69

is likely due to the increased solids ratio of ANF which possess Van der Waals

attraction.

Figure 7.9: Rheological flow curves for cement specimens

Table 7.3: Rheological properties for cement specimens

Cement batch

identifier

Yield

Point (Pa)

Plastic Viscosity

(Pa·s)

r2

value

5 sec. Gel

strength

(Pa)

10 min. Gel

Strength (Pa)

CFR02 5.1 0.0262 0.99 2.04 3.17

CFR02PDANF01 1.6 0.0269 0.99 4.09 5.02

CFR02PDANF02 3.5 0.0317 0.99 4.38 5.23

CFR02PDANF03 4.6 0.0344 0.98 4.63 5.50

CFR02PDANF04 4.7 0.0345 0.98 4.84 5.76

7.1.5 Cement free fluid and sedimentation behavior

Table 7.4 and Table 7.5 display the results for the free fluid and sedimentation

tests. As can be seen in Table 7.4, the amount of free fluid continues to decrease as the

amount of ANF is increased. A previous study was conducted utilizing TGA to

evaluate the existence of surface coatings on ANF (Saunders et al. 2015). Based upon

this study it was concluded that ANF possesses a high-water adsorption capacity. This

0

5

10

15

20

25

0 100 200 300 400 500 600

Shea

r S

tres

s (P

a)

Shear rate (s-1)

CFR02

CFR02PDANF01

CFR02PDANF02

CFR02PDANF03

CFR02PDANF04

Linear (CFR02)

Linear

(CFR02PDANF01)Linear



(CFR02PDANF04)


70

affinity to water reduces the amount of water in the cement hydration process. This

phenomenon explains the reduction of free water as the amount of ANF is increased.

For each ANF cement formulation, the amount of free water is below the maximum

amount of “5.9%” when using class “H” cement. There is also not a significant

increase in sedimentation between different alumina nanofiber concentrations

according to Table 7.5. This is an indication that highly dispersed alumina nanofibers

are highly stable.

Table 7.4: Free fluid tests for cement specimens

Cement batch identifier % Volume Fraction of Free Fluid

CFR02 2.83

CFR02PDANF01 2.19

CFR02PDANF02 1.89

CFR02PDANF03 1.76

CFR02PDANF04 1.68

API Limit < 5.9

Table 7.5: Sedimentation tests for cement specimens

Cement batch

identifier

Segment

1 (Base)

Segment 2 %

change in

density ∆ƿ

Segment 3 %

change in

density ∆ƿ

Segment 4 %

change in

density ∆ƿ

CFR02 - 0.15 0.33 0.50

CFR02PDANF01 - 0.26 0.39 1.11

CFR02PDANF02 - 0.46 0.59 1.12

CFR02PDANF03 - 0.51 0.59 1.11

CFR02PDANF04 - 0.54 0.52 1.14

7.1.6 Cement thickening time

According to Table 7.6, there is minimal discrepancies in the thickening time

between the various cement samples. As the amount of ANF is increased the

thickening time slightly reduces. This slight reduction is caused by the loss of water in

the slurry due to the strongly bound water on ANFs. The largest discrepancy in

thickening time is between the CFR02 and CFR02PDANF04 cement formulation (10

minutes). These two cement formulations also have the largest discrepancy in the


71

amount of free fluid according to Table 7.4, which was previously mentioned. The

combination of these two results affirm the notion that loss of water from the slurry

accelerates the setting time.

Table 7.6: Cement thickening time tests

Cement batch identifier Thickening Time (Hrs:Min)

CFR02 2:18

CFR02PDANF01 2:16

CFR02PDANF02 2:14

CFR02PDANF03 2:10

CFR02PDANF04 2:08

7.1.7 Cement permeability measurements under cyclic confining pressures

Figure 7.10, Figure 7.11, Figure 7.12, and Figure 7.13 displays the

permeability measurements during cyclic loading and unloading at each confining

pressure value for all cement formulations. The experiments were performed under

cyclic confining pressure beginning at 6.89 MPa, increased to 20.68 MPa in 3.45 MPa

increments, and finally decreased in the same manner for two cycles. All cement

formulations undertake the most irreversible change to the pore structure during the

first pressure cycle. This irreversible change indicates the closing of microcracks due

to increasing the confining pressure. All cement formulations also showed the ability

to endure confining pressure fluctuations since there was no sudden increase in

permeability which would indicate internal cement failure. Although subtle, there are

minor changes in permeability between cement formulations. The Ref cement sample

had an average permeability of 1.83 micro Darcy (μD), ANF-1 had an average

permeability of 1.44 μD, ANF-2 had an average permeability of 1.92 μD, and ANF-3

had an average permeability of 2.25 μD. The increase of permeability between the Ref

cement paste and the ANF-1 cement paste is due to the characteristics of ANFs. ANFs

possess high aqueous adsorption capacity. This allows cement particles to hydrate

around ANFs, which creates a denser microstructure filling nano-pores and bridging

nano-cracks. ANFs essentially provide nuclei for cement phases to promote DOH. The

inclusion of ANFs also increases the amount of C-S-H within the cement composite,

this will be further discussed in the XRD analysis section. Thus, the amount of binder

is increased which reduces the permeability of the cement and densifies the structure.


72

However, after ANF-1 the permeability of composites steadily increases with the

increase of ANFs. This increase of permeability is due to nanofilament agglomeration

and clustering due to increased loading. This leads to irregularities in the

microstructure during the hydration process which increases the permeability. These

irregularities in the microstructure can easily cause nano or microcracks in the

composite which can be enhanced by the simulated downhole curing temperature.

(Mahmoud et al. 2019) experienced a similar phenomenon stating that after increasing

the nanoclay content past 3%, the permeability of cement increased.

Figure 7.10: Permeability measurement of Ref cement composite under cyclic

confining pressure


73

Figure 7.11: Permeability measurement of ANF-1 cement composite under cyclic

confining pressure


74


confining pressure


75


confining pressure

7.1.8 Cement elastic properties measurements under cyclic confining

pressures

The dynamic elastic properties, MOE, and Poisson’s ratio were also measured

under the same cyclic confining pressure schedule as the permeability tests. The MOE

data is shown in Figure 7.14. According to the results, it is evident all cement

formulations either experienced insignificant or no change in MOE. Specimens are

experiencing a low inelastic deformation (the closing of microcracks), as the MOE

values are mostly able to return to its original value after pressure cycling. These

values are similar to (Spaulding et al. 2015), who reported MOE values of class “H”

cement between 5.5 GPa to 11.1 GPa with varying cement class “H” formulations.

The Ref cement composite displays the highest elastic modulus, which does not

contain any ANF while the ANF-1 displays the lowest elastic modulus from all other

cement formulations. It is interesting to note that ANF-2 and ANF-3 display similar


76

values as the Ref material. A possible explanation is that the cements are experiencing

nanofiber clustering which reduce the mechanical properties, thereby lowering

elasticity. This is essential because cements with lower MOE are more resistant to

common mechanical stresses associated in well operations.

Figure 7.14: MOE results for each cement composite at the corresponding confining

pressure increment for both pressure cycles

It is also worth noting that as confining pressure increases, the 𝑉𝑝 and 𝑉𝑠 waves

also increase. Essentially the confining pressure is causing microfractures to close,

thereby increasing the velocity which causes the specimens to exhibit plastic behavior.

This explains the increase of MOE for each cement composition. In order to

effectively illustrate this phenomenon all MOE values at the corresponding pressure

increment were averaged and plotted as shown in Figure 7.15. The values were

averaged because the MOE at the corresponding pressure increment differed

minimally across pressure cycles. The standard deviations (SDx) along with the

percentage increase between each averaged pressure increment is also displayed in

Figure 7.15.

According to Figure 7.15, the change in MOE across all pressure fluctuations

is the lowest for the ANF-1 sample at 1.23%. Followed by the Ref sample at 1.43%,


77

ANF-2 at 2.93%, and ANF-3 at 3.27%. This is indication that there is more pore

volume in the Ref, ANF-2, and ANF-3 cement formulations than the ANF-1

formulation. These results coincide with the permeability results discussed earlier. The

ANF-1 sample essentially contains the least amount of pore spaces with an almost

constant MOE throughout pressure cycling.

(a)


78

(b)

(c)

(d)

Figure 7.15: Average values of Young’s Modulus, the percentage increase, and

standard deviations of cement composites under cyclic pressure increments: (a) Ref

sample (b) ANF-1 (c) ANF-2 (d) ANF-3


79

Figure 7.16 displays the Poisson’s ratio values under the same cyclic loading

schedule. At each confining pressure increment ANF-1 possesses the highest

Poisson’s ratio compared to the other cement formulations. Again, the Ref material

does not contain ANF which explains the low Poisson’s ratio. ANF-2 and ANF-3

however possess slightly lower values of Poisson’s ratio than ANF-1. This is possibly

due to nanofiber clustering, which lowers the mechanical properties. It is

advantageous to increase the Poisson’s ratio because this reduces the compressibility

of the cement allowing better wellbore integrity and cycle fatigue behavior. Again, the

Poisson’s ratio values differed minimally across corresponding pressure cycles, so the

values were averaged.

Figure 7.16: Poisson’s Ratio results for each cement composite at the corresponding

confining pressure increment for both pressure cycles

The standard deviations (SDx) along with the percentage change for each

averaged pressure increment is also displayed in Figure 7.17. Unlike MOE, Poisson’s

ratio does not display any significant trend as the values slightly increase and decrease

between pressure fluctuations. Therefore, it is observed the Poisson’s ratio is mostly

unaffected by the pressure fluctuations.


80

(a)

(b)


81

(c)

(d)

Figure 7.17: Average values of Poisson’s ratio, the percentage change, and standard

deviations of cement composites under corresponding cyclic pressure increments: (a)

Ref sample (b) ANF-1 (c) ANF-2 (d) ANF-3


82

7.1.9 XRD analysis

In order to investigate the phase identification and crystalline phase changes of

cement composites cured with ANFs, XRD analysis was conducted with varying

dosages of ANFs. Figure 7.18 displays the diffraction patterns of hydrated cement

composites listed in Table 7.2. The XRD patterns reveal that all cement composite

formulations experience the same diffraction patterns and trends. Although, there are

slightly noticeable differences in the peak intensities. The anhydrous hydration

products (Brownmillerite (Ca2FeAlO5), Portlandite (Ca(OH)2), Quartz (SiO2), Calcite

(Ca(CO3)), and Larnite (Ca2(SiO4))) are crystalline, therefore their detection by X-ray

diffraction (XRD) was possible. However, majority of the hydration products was

indistinguishable by XRD due to the ill-defined hydrated calcium silicate hydrate gel.

This phase is usually referred to as C-S-H which is partially crystalline or amorphous

and is structurally related to tobermorite and jennite (with approximate stoichiometry

of C5S6H5 and C9S6H11 respectively). Due to the peak overlapping, it is difficult to

conduct a quantitative analysis based upon the peak intensities. However, the weight

percentage of hydration products for each cement composite was obtained as shown in

Table 7.7. Table 7.7 was calculated using the semi-quantitative analysis through

Rietveld refinements.


83

Figure 7.18: Diffraction patterns of ANF-cement composites

Table 7.7: Weight percentage of hydration products in cement composites

Cement Identifier Portlandite Quartz Calcite Brownmillerite Larnite C-S-H

Ref. 32.8 2.4 1.9 10.3 16.6 35.9

ANF-1 29.7 3.3 0.8 11.2 16.3 38.7

ANF-2 28.9 3.1 0.9 11.5 17.9 37.7

ANF-3 29.7 4.2 0.9 10.4 16.7 38.0

Most notable in Table 7.7 is the amount of C-S-H in each cement formulation.

The C-S-H phase is the most vital phase due to its binding ability which is responsible

for majority of strength gain in hydrated cement specimens. According to Table 7.7

the largest increase in the C-S-H phase occurred between the Ref and ANF-1 sample.

Additionally, the total weight percentage of hydration products ANF-2 and ANF-3

slightly varied from ANF-1. There are two different phenomena that could explain the

increase in the C-S-H phase from the Ref sample to the other ANF composite

formulations. (1) There is a nucleation effect of ANFs in the cement composite which

accelerate the formation of hydrate phases. In other words, there is a seeding effect


84

that is commonly associated with the use of nanomaterials that effectively accelerates

hydration. Essentially the seeding effect provides additional surface area which offers

more nucleation sites for C-S-H formation. (Muzenski, Flores-Vivian, and Sobolev

2019) utilized ANFs in ultra-high strength cement-based composites in which ANFs

were found to act as seeds to promote the formation of hydration products along the

fibers. It was concluded that C-S-H formed around nanofibers establishing a “shish

kebab” effect. According to the results in this research, the seeding affect phenomena

is likely affirmed. (2) The additional presence of Al2O3 may also promote the

formation of tobermorite. (Meller, Kyritsis, and Hall 2009) conducted experiments on

API Class “G” oil well cements were the addition of alumina promoted the formation

of tobermorite, thereby, improving the engineering properties of the cementitious

composite material. The Al3+ ions support the anomalous 1.1 nm tobermorite at high

curing conditions which encourages its formation. Likewise, a similar phenomenon is

possibly occurring with the addition of ANF promoting the increase of C-S-H owing

to the Al3+ ions. Since there is such a small additional amount of ANF introduced in

the cement composite all the hydration products had a relatively small difference

between ANF-1, ANF-2, and ANF-3.

It is also worth noting in situations where sulfate resistance is required, the

quantities of tricalcium aluminate are typically reduced or almost eliminated due to its

susceptibility of sulfate attack. According to the Ref sample of Figure 7.18, the

exclusion of ettringite from the diffraction pattern ascribes to low amounts of

tricalcium aluminate. Thus, it seems reasonable that the inclusion of ANF could be

deleterious to the cement sheath. However, once ANF is included in the cement

composition for samples ANF-1, ANF-2, and ANF-3, there is almost no change in the

diffraction pattern. Therefore, the results suggest a low dosage of ANF does not

conduce sulfate attacks. Additionally, ANFs are considered to be anti-corrosive

materials (Noordin and Liew 2010). Thus, it is observed an insignificant amount of

alumina is reintroduced into the cement composition negating the formation of

ettringite. Although, longer curing durations or increased ANF dosages may deem

different results.


85

7.1.10 TGA analysis

The DOH was calculated by measuring the total mass of CBW in each of the

cement formulations. Figure 7.19 displays the weight loss with the mass at 140°C

normalized at 100%. There is also a zoomed in plot of Figure 7.19 to provide greater

details of the weight losses. The dramatic increase in weight loss between 440°C and

520°C is attributed to the decomposition of Portlandite (Ca(OH)2) (Pane and Hansen

2005) for all samples.

Figure 7.19: TGA results from 140 to 1100°C with the mass at 140°C as the base

(100%)

The weight of CBW is quantified in Table 7.8 as (wb), which indicates there is

a slight increase in the weight loss for ANF cement composites compared to the

reference sample. The total DOH for each cement formulation is also calculated and

presented in Table 7.8. The DOH was calculated by dividing the weight of CBW, in

the region between 140 and 1100°C, by 0.23 which is the assumed gram per unit gram

of cement when fully hydrated (Young and Hansen 1986). It should be mentioned the


86

value of 0.23 is typical for type I Portland cement, although it has also been used in

previous studies to calculate the degree of hydration for class “H” oil well cement

(Zhang et al. 2010). Considering its intended use, there is a possibility of slight

systematic calculational error. Additionally, because of the various cement phases that

act at different rates, the degree of hydration may relate either to individual clinker

phases or the entire cement composition rendering a definitive solution problematic.

Therefore, the degree of hydration is considered an overall and approximate

measurement of the reacted mass fraction of cement irrespective of the phase

(Mounanga et al. 2004). The results show that more water reacts with cement when

ANFs are present. These experiments, together with XRD, prove the fact that ANF

indeed slightly increases the DOH due primarily to the nucleation effect of ANF

which accelerates the formation of hydration products.

Table 7.8: wb and DOH analyzed per gram of cement paste with different ANF weight fractions

Analysis Ref ANF-1 ANF-2 ANF-3

wb 11.76 12.01 11.92 11.87

DOH 51.1 52.2 51.8 51.6

wb: Weight of CBW per g cement; DOH: Degree of Hydration

To illustrate this phenomenon, once cement particles are combined with water,

hydration products begin to form in the matrix as shown in Figure 7.20. Considering

ANFs are added and embedded into the cement matrix (Figure 7.21), additional

hydration products form (seeding effect) and the fibers can bridge nanopores thereby

distributing various stresses.


87

Figure 7.20: Schematic representation of cement hydration

Figure 7.21: Schematic representation of cement hydration with ANF

7.1.11 ANF construction cost

Depending upon the type of wellbore drilled and cemented, the operational

cost will vary. However, in this project, it is considered the well is drilled in the

Permian Basin. A typical wellbore trajectory is shown in Figure 7.22. Typically, the


88

wellbore is cemented with specialized light weight cement (such as foam cement) until

the end of curve (EOC). After the end of curve, the remaining section (lateral section)

is cemented with higher density cement. A cross-sectional view of the cemented

lateral section is presented in Figure 7.23. The cost of ANF is US$1.17/g, which is

considerably lower than other nanomaterials such as CNT which can cost upwards of

US$750/g. Considering the hole diameter is 215.9 mm and the casing diameter is

177.8 mm, the cost to cement the lateral section with the ANF-1 formulation in Table

7.2 will cost US$23,780. Considering the improved wellbore integrity ANF can

provide, this is a beneficial long-term investment.

Figure 7.22: Schematic of horizontal wellbore trajectory


89

Figure 7.23: Cross-sectional view of the cemented lateral section

7.2 Cement hybridization scheme of ANFs and micro-synthetic PP fibers: Phase two

7.2.1 Morphology of SiO2 nanoparticles on PP fibers

The SEM images of both the untreated and sol-gel treated PP fibers are

displayed in Figure 7.24 and Figure 7.25. The surface of the untreated fibers (Figure

7.24) is noticeably smoother than the surface of the treated fibers (Figure 7.25). Due to

the poor wettability of the untreated PP fibers and differing surface energy from the

cement matrix, the interfacial interaction between the untreated PP fibers and the

cement composite results in a poor fiber/matrix bond. However, the increased

roughness of the treated fibers improves the fiber/matrix bonding resulting in

improved mechanical properties. The increased roughness is attributed to the presence

of nano-silica on the surface of the treated fibers. Essentially, nano-silica behaves as a

primer on the treated fibers whereby, the silanol groups on the nanoparticles surface

allows points of nucleation for the precipitation of hydration compounds (Di Maida et

al. 2015; Claramunt et al. 2019). Nano-silica possesses high pozzolanic activity,

improves cement impermeability and mechanical properties, and reduces cement

thickening time (El-Gamal, Hashem, and Amin 2017). Consequently, nano-silica is a

common additive in both oil and gas well cementing and civil engineering industries.


90

Figure 7.24: SEM images of untreated PP fibers

Figure 7.25: SEM images of sol-gel treated PP fibers

EDX measurements were conducted on both fibers (shown in Figure 7.26 and

Figure 7.27) to assess the elemental differences. The treated PP fibers exhibit a silicon


91

(Si) peak (Figure 7.27), which is absent in the untreated PP fibers (Figure 7.26). It

should be mentioned that the (Au/Pd) peaks are results of the fiber coating process,

and the large carbon (C) peak is from the carbon tape used to conduct the analysis.

Thus, the EDX spectra further proves the effective modification of PP fibers obtained

by dispersing SiO2 nanoparticles on the fibers surface. The improved adhesion

characteristics are imperative to capitalize on the properties of the PP fibers embedded

in the cement composite.

Figure 7.26: EDX spectra of untreated PP fibers


92

Figure 7.27: EDX spectra of sol-gel treated PP fibers

7.2.2 UCS of ANFs and PP fibers

The uniaxial compressive strength of the oil well cement composites varied

from 21.38 to 25.51 MPa as shown in Figure 7.28.

Figure 7.28: UCS of ANFs and PP fibers

21.62

25.10

24.12

22.61

21.38

22.91

25.51

24.27 24.2624.71 24.74 24.56 24.83

20.00

22.00

24.00

26.00

28.00

1 2 3 4 5 6 7 8 9 10 11 12 13Com

pre

ssiv

e S

tren

gth

(M

pa)

Run Number


93

The values in Figure 7.28 were then used to construct the three-dimensional

response surface plot (Figure 7.29) and the two-dimensional contour plot (Figure 7.30)

for the UCS.

Figure 7.29: UCS 3-D response surface plot

0.09

0.19

0.29

0.40

0.50

0.03

0.07

0.12

0.16

0.20

18.00

20.50

23.00

25.50

28.00

Com

pre

ssiv

e S

treng

th (

MP

a)

ANF (% BWOC)PP fibers (% BWOC)


94

Figure 7.30: UCS 2-D contour plot

According to Figure 7.29, as the concentration of PP fibers increase there is

also an increase in the compressive strength. However, the increase in compressive

strength is more pronounced when increasing the ANF concentration. In fact, as the

results insinuate, ANF is responsible for obtaining ultimate compressive strength with

minimum fiber hybridization contribution from PP fibers. Various researchers have

documented minimal compressive strength improvement in cement composites when

using untreated PP fibers. (Song, Hwang, and Sheu 2005), (Ede and Ige 2014), and

(Qin et al. 2019) reported that untreated PP fibers increased the compressive strength

of cementitious materials by a maximum of 1.26%, 5.8%, and 9%, respectively as

compared to the plain (nonfibrous) control counterpart. Although subtle, the strength

increase is attributed to proper distribution of PP fibers in the cement composite. PP

fibers also provide a bridging and reinforcing effect, which resists cracking in the

cement matrix. The adhesion between the cementitious material and PP fibers enables

stress to transfer from the crack tip to the crack areas of the upper and lower cement

composite surface regions. The stress inside the cement composite becomes more

uniform as the degree of stress concentration is mitigated.

0.03 0.07 0.12 0.16 0.20

0.09

0.19

0.29

0.40

0.50Compressive Strength (MPa)

ANF (% BWOC)

PP

fib

ers

(% B

WO

C)

22

23

23

24

24

25

5


95

In the current research, SiO2 nanoparticles deposited on the PP fibers surface

enhanced the adhesion between the fiber/matrix interface, which increased the overall

compressive strength. However, treated PP fibers provided minimal contribution to

ultimate compressive strength. The minimal contribution is primarily a result of PP

fibers resistance against stresses after cracks have appeared (post-cracking) as opposed

to before cracks appear. Conversely, ANFs provide excellent reinforcement

characteristics before cracks appear. ANFs act as a nucleation site for the development

of hydration products, mainly calcium silicate hydrate (C-S-H). Alumina (Al2O3)

promotes the formation of 1.1 nm tobermorite, which enhances the engineering

properties of the cementitious composite material. The degree of hydration (DOH)

also increases due to the nucleation effect, more water is able to react with the cement

improving the mechanical properties. ANFs also provide a “bridging effect” between

nanopores that transfers various stresses. The interactive relationship of both PP fibers

and ANFs on the compressive strength can be further analyzed by the empirically

derived quadratic model shown below:

𝐶𝑜𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑣𝑒 𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ (𝑀𝑃𝑎)

= 19.56017 + 69.75644 ∗ 𝐴𝑁𝐹 + 5.55376 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠 − 71.51951

∗ 𝐴𝑁𝐹 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠 − 182.75020 ∗ 𝐴𝑁𝐹2 + 2.73123 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠2

The results in Figure 7.30 also suggests ANFs is primarily responsible for

achieving ultimate compressive strength. The distorted parabolic contours indicate

fewer interactions between the independent variables (Nassar, Thom, and Parry 2016).

7.2.3 Tensile strength of ANFs and PP fibers

The tensile strength of the oil well cement composites varied from 2.15 to 2.88

MPa as shown in Figure 7.31.


96

Figure 7.31: Tensile strength of ANFs and PP fibers



for the tensile strength.

Figure 7.32: Tensile strength 3-D response surface plot

2.28

2.732.34 2.23 2.15

2.32

2.88

2.40 2.46 2.52 2.52 2.48 2.54

0.00

1.00

2.00

3.00

4.00

1 2 3 4 5 6 7 8 9 10 11 12 13

Ten

sile

Str

ength

(M

Pa)

Run Number

0.09

0.19

0.29

0.40

0.50

0.03

0.07

0.12

0.16

0.20

2.00

2.25

2.50

2.75

3.00

Ten

sile

Str

ength

(M

Pa)

ANF (% BWOC)PP fibers (% BWOC)


97

Figure 7.33: Tensile strength 2-D contour plot

Upon observation, it is evident that Figure 7.32 and Figure 7.33 strongly

resembles Figure 7.29 and Figure 7.30, respectively. The resemblance is not

coincidental as the compressive strength is typically 10 to 12 times greater than the

tensile strength. The direct proportionality between the compressive and tensile

strength of cement composites has also been reported by various researchers

(Bourgoyne Jr et al. 1991; Dass et al. 1993; Al-Awad 1997). Conventional cement

composites are naturally brittle, and the cement sheath typically fails due to inadequate

tensile strength (Jafariesfad, et al. 2017). In fact, tensile strength, elasticity, and

ductility is believed, by various researchers, to be more important to long-term cement

sheath durability than compressive strength (Thiercelin et al. 1998; Bosma et al. 1999;

Di Lullo and Rae 2000; Ravi, Bosma, and Gastebled 2002; Nelson and Guillot 2006).

Therefore, it is imperative to simultaneously enhance the mentioned mechanical

properties. Once the cement sample experiences splitting due to the tensile stress, the

stress is transferred from the matrix to the PP fibers. The transfer of stress is

accomplished due to the ability of PP fibers to bridge across the split sections of the

matrix. However, as with the compressive strength results, the PP fibers provide

0.03 0.07 0.12 0.16 0.20

0.09

0.19

0.29

0.40

0.50Tensile Strength (MPa)

ANF (% BWOC)

PP

fib

ers

(% B

WO

C)

2.3

2.3

2.4

2.4

2.5

2.6

2.7

5


98

minimal contribution to the ultimate tensile strength. Again, ANF is responsible for

achieving ultimate tensile strength without fiber hybridization from PP fibers. Since

the compressive and tensile strengths possess high proportionality, the previously

mentioned mechanisms for ultimate compressive strength improvement also applies

for the tensile strength. The derived RSM model for the tensile strength is displayed in

the equation below:

𝑇𝑒𝑛𝑠𝑖𝑙𝑒 𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ (𝑀𝑃𝑎)

= 2.17540 + 8.04906 ∗ 𝐴𝑁𝐹 − 0.595660 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠 − 8.01256

∗ 𝐴𝑁𝐹 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠 − 20.77900 ∗ 𝐴𝑁𝐹2 + 1.42684 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠2

Similar to Figure 7.30, Figure 7.33 displays ANF as the primary contributor to

ultimate tensile strength with distorted parabolic contours indicating a low interaction

between PP fibers and ANF. Although the PP fiber/matrix bond has been improved

through the sol-gel treatment method, there are several reasons they do not contribute

to ultimate strength. As previously stated, PP fibers are more effective during post

cracking. PP fibers are especially effective at holding broken cement fragments

together and preventing the generation of larger cracks (Pająk 2016). There is also a

substantial strength difference between PP fibers and ANF as displayed in Table 3.1

and Table 3.2. The tensile strength of ANF is two orders of magnitude greater than PP

fibers. The substantial strength difference in the fibers provides plausible explanation

for the results obtained. Considering cracks initiate from the nanoscale level, modified

PP fibers are rendered ineffective at the microscale level once ANF failure has

occurred. (Mohammed, Khed, and Liew 2018) also conducted research whereby the

flexural strength of engineered cementitious composites was increased due to the

hybridization of PVA fibers and tirewire fibers. However, PVA fibers were considered

the main contributor towards the strength enhancement due to its higher tensile

strength over tirewire fibers. Likewise, in this research, similar results have been

ascertained.


99

7.2.4 MOE of ANFs and PP fibers

In the previous strength properties section, Figure 7.28 and Figure 7.31 each

had the optimum strength value achieved at run number 7 (comprised of only ANFs

and no PP fibers). Figure 7.34, displaying the MOE varying from 9.45 to 10.00 GPa,

shows differing results. The optimum MOE values are located at runs 9-13, which are

the five center runs. These runs contain both PP fibers and ANF indicating that there is

a possible synergistic effect among the fibers enhancing the MOE. It is paramount to

reduce the MOE of the cement sheath; this increases the elasticity of the cement,

which reduces the susceptibility of cement failure due to pressure and temperature

fluctuations.

Figure 7.34: MOE of ANFs and PP fibers



for the MOE.

9.869.72 9.79 9.86

10.009.86

9.58

9.79

9.51 9.45 9.45 9.51 9.45

8.50

9.00

9.50

10.00

10.50

1 2 3 4 5 6 7 8 9 10 11 12 13

MO

E (

GP

a)

Run Number


100

Figure 7.35: MOE 3-D response surface plot

Figure 7.36: MOE 2-D contour plot

According to Figure 7.35 there is indeed a synergistic effect of achieving a

minimum MOE as both fibers provide contribution. Although subtle, Figure 7.36

0.09

0.19

0.29

0.40

0.50

0.03

0.07

0.12

0.16

0.20

9.40

9.50

9.60

9.70

9.80

9.90

10.00

MO

E (

GP

a)

ANF (% BWOC)

PP fibers (% BWOC)

0.03 0.07 0.12 0.16 0.20

0.09

0.19

0.29

0.40

0.50MOE (GPa)

ANF (% BWOC)

PP

fib

ers

(% B

WO

C)

9.5

9.6

9.7

9.7

9.8

9.8 9.8

5


101

shows that PP fibers provide a synergistic effect in achieving the second-order surface

minimum MOE with ANF as the primary contributor. The elliptical contours indicate

high interactions between the fibers. The derived RSM model for the MOE is

displayed in the equation below.

𝑀𝑂𝐸 (𝐺𝑃𝑎) = 10.20490 − 8.82394 ∗ 𝐴𝑁𝐹 − 1.55530 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠 + 2.96761

∗ 𝐴𝑁𝐹 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠 + 32.50389 ∗ 𝐴𝑁𝐹2 + 2.44110 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠2

Lowering the MOE of the cement sheath is achieved by adding flexible

materials. PP fibers and ANF are both considered flexible additives in oil well cement

composites (Song, et al. 2018; McElroy, Emadi, and Unruh 2020). As seen with the

tensile strength of the fibers, the MOE of ANF is two orders of magnitude greater than

PP fibers according to Table 3.1 and Table 3.2. However, the discrepancy in MOE of

the fibers does not negate the effectiveness of PP fibers. When measuring the elastic

properties of the cement samples, the applied confining pressure induces a low

inelastic deformation (the closing of microcracks), which causes the specimen to

exhibit plastic behavior. Unlike the test procedures conducted when measuring the

strength properties, the ultimate strength was not surpassed, which does not induce

cement failure. Essentially, cement samples are still within the elastic region meaning

that ANFs and PP fibers are still effectively bonded to the cement matrix. The

elasticity is simultaneously enhanced on the nano and microscale levels from the

contribution of both fibers. Hence, PP fibers and ANF both provide a synergistic effect

in reducing the MOE of the cement sheath.

7.2.5 Poisson’s Ratio of ANFs and PP fibers

The Poisson’s ratio of the oil well cement composites varied from 0.172 to

0.207 as shown in Figure 7.37. Similar to the MOE, the optimum Poisson’s ratio

values are located at runs 9-13. Again, these runs contain both PP fibers and ANFs

indicating a possible synergistic effect among the fibers in enhancing the Poisson’s

ratio.


102

Figure 7.37: Poisson's Ratio of ANFs and PP fibers


response surface plot Figure 7.38 and the two-dimensional contour plot Figure 7.39

for Poisson’s ratio.

Figure 7.38: Poisson's Ratio 3-D response surface plot

0.1780.198

0.1810.196

0.1720.197 0.202 0.194 0.204 0.206 0.207 0.204 0.204

0.000

0.050

0.100

0.150

0.200

0.250

1 2 3 4 5 6 7 8 9 10 11 12 13

Pois

son's

Rati

o

Run Number

0.09

0.19

0.29

0.40

0.50

0.03

0.07

0.12

0.16

0.20

0.170

0.180

0.190

0.200

0.210

Pois

son

's R

atio

ANF (% BWOC)

PP fibers (% BWOC)


103

Figure 7.39: Poisson's Ratio 2-D contour plot

According to Figure 7.38 there is indeed a synergistic effect of achieving a

maximum Poisson’s ratio as both fibers provide contribution. Again, Figure 7.39

suggests that PP fibers provide a synergistic effect in achieving the second-order

surface maximum Poisson’s ratio with ANF as the primary contributor. The elliptical

contours indicate high interactions between the fibers. The derived RSM model for the

Poisson’s ratio is displayed in the equation below.

𝑃𝑜𝑖𝑠𝑠𝑜𝑛′𝑠 𝑅𝑎𝑡𝑖𝑜

= 0.162626 + 0.495890 ∗ 𝐴𝑁𝐹 + 0.058034 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠

− 0.071736 ∗ 𝐴𝑁𝐹 ∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠 − 1.59123 ∗ 𝐴𝑁𝐹2 − 0.095492

∗ 𝑃𝑃 𝑓𝑖𝑏𝑒𝑟𝑠2

The measurements of MOE and Poisson’s ratio were conducted by measuring

the Vs and Vp waves simultaneously. The ultimate strength is not surpassed meaning

both ANF and PP fibers are effectively bonded to the cement matrix during the

measurement. Since samples are still within the elastic region, the elasticity is

increased on the nano and microscale levels. Hence, PP fibers and ANF both provide a

synergistic effect in increasing the Poisson’s ratio of the cement sheath.

0.03 0.07 0.12 0.16 0.20

0.09

0.19

0.29

0.40

0.50Poisson's Ratio

ANF (% BWOC)

PP

fib

ers

(% B

WO

C)

0.185

0.190.195

0.2

0.2

0.205

5


104

7.2.6 ANOVA and RSM model analysis

The previously discussed results have been statistically analyzed. All the

empirically derived equations were effectively modeled using quadratic formulations.

The ANOVA analysis was conducted to assess the relationship between the

independent and dependent variables. The RSM models were also validated to assess

the efficacy of the regression equations as predictive models. In order to avoid

redundancy in this section, only the Poisson’s ratio will be used as an illustrative

model. Table 7.9 displays the ANOVA results.

Table 7.9: ANOVA model analysis

Source Sum of Squares df Mean Square F-value p-value Significance

Model 1.54E-03 5 3.08E-04 51.52 <0.0001 Yes

A-ANF 6.68E-04 1 6.68E-04 111.74 <0.0001 Yes

B-PP fibers 1.46E-05 1 1.46E-05 2.44 0.1623 No

AB 6.25E-06 1 6.25E-06 1.05 0.3405 No

A2 8.62E-04 1 8.62E-04 144.20 <0.0001 Yes

B2 1.15E-04 1 1.15E-04 19.31 0.0032 Yes

Residual 4.18E-05 7 5.98E-06

Lack of Fit 3.38E-05 3 1.13E-05 5.64 0.0640 No

Pure Error 8.00E-06 4 2.00E-06

Cor Total 1.58E-03 12

The F-value and p-value obtained from the ANOVA results have a critical

influence on the evaluation of the selected models. The regression model is deemed

statistically significant if the calculated “F” value is greater than the critical value of

the “F” distribution. The “F” distribution table is used to calculate the critical value,

where F-tab = F0.05 (5,7) = 3.97. The model calculated F-value of 51.52 is greater than

3.97, hence the F-value implies that the model is significant. Additionally, p-values

less than or equal to 0.05 (rejection of the null hypothesis) indicates that the model

terms have a significant effect on the response with at least a 95% confidence interval

(i.e. α=0.05, 1-α=0.95). p-values greater than 0.05 (failure to reject the null

hypothesis) indicates that the model terms are insignificant. A-ANF, A2, and B2 were

significant model terms while B-PP fibers and AB were insignificant. A-ANF and A2

have the highest influence on the response since they contain the lowest p-values.


105

Most notable from Table 7.9 is the p-value of the model, which also indicates that the

quadratic model is indeed significant. Furthermore, the p-value for lack of fit was

greater than 0.05, which indicates insignificance. This implies that the model is

reliable and useful as a predictive model. Table 7.10 displays the summary of the

regression model.

Table 7.10: Summary of Regression Model

Std. Dev. 2.44E-03 R2 0.974

Mean 1.96E-01 Adjusted R2 0.955

C.V. % 1.25 Predicted R2 0.835

Adeq Precision 20.202

The reproducibility of the model was assessed by calculating the coefficient of

variance (C.V. %). The C.V.% is calculated from the ratio of standard error of the

estimate and the mean observed response value; 1.25% is the result of the calculation.

Typically, a ratio of less than 10% indicates reasonable reproducibility of the model.

The prediction model also exhibits non-biasness as the difference between the adjusted

R2 and the predicted R2 is less than 0.2. Additionally, the Adeq Precision, which

measures the signal to noise ratio, was greater than four, which is desirable. Thus, the

model can effectively be used to navigate the design space.

Figure 7.40 displays the normal plot of residuals. Multiple regression assumes

the residual values (difference between observed and predicted values) are normally

distributed. The data points are closely aligned along the inclined straight line

(representing normal distribution), which indicates the experimental data is normally

distributed.


106

Figure 7.40: Normal plot of residuals

Figure 7.41 displays the model predicted (inclined straight line) vs. actual

values (data points), which displays adequate model precision.

Externally Studentized Residuals

No

rmal

% P

rob

abil

ity

Normal Plot of Residuals

-4.00 -2.00 0.00 2.00 4.00 6.00

1

5

10

20

30

50

70

80

90

95

99


107

Figure 7.41: Model predicted vs. actual values

Figure 7.42 displays the standardized residuals vs. the model predicted line.

The experimental data was uniformly distributed and did not follow a specific pattern,

thereby exhibiting homoscedasticity (constant variance). This validates the

significance of the model and indicates there is no systematic error within the system.

Actual

Pre

dic

ted

Predicted vs. Actual

0.170

0.180

0.190

0.200

0.210

0.170 0.180 0.190 0.200 0.210

3


108

Figure 7.42: Standardized residuals vs. model predicted line

Figure 7.43 displays the perturbation plot for the developed response model.

The perturbation plot helps to compare the effects of both factors at a particular point

in the design space; with “A” representing ANF and “B” representing PP fibers. The

reference point is set at the midpoint (coded “0”) and the response is plotted by

changing one factor over its range while holding the other factor constant. A curvature

or steep slope in a factor shows that the response is sensitive to that factor as a

relatively flat line shows insensitivity. The results support the previous observations,

which indicate ANF has a larger influence on the response than PP fibers. It is worth

noting that after the optimum value is reached for each fiber, there is a decrease in the

mechanical properties as the fiber dosage is increased. This is due to nanofiber

clustering, which decreases cement composite mechanical properties. Essentially, the

results of the ANOVA and RSM model analysis have been statistically validated to

suggest that the developed formulations have high predictive accuracy. The

compressive, tensile and MOE models also possess similar results.

3

Predicted

Ex

tern

ally

Stu

den

tize

d R

esid

ual

s

Residuals vs. Predicted

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

0.170 0.180 0.190 0.200 0.210

4.56117

-4.56117

0


109

Figure 7.43: Perturbation plot

7.2.7 Multi-objective optimization of the responses and experimental

validation

Multi-objective optimization was performed to determine the optimum

concentrations of ANFs and PP fibers to synergistically enhance the mechanical

properties of the cement composite. This was accomplished through the use of the

desirability function (DF), which was proposed by (Harrington 1965) and improved by

(Derringer and Suich 1980). The general approach is to convert each response, 𝑦𝑖,

using the input variables, into an individual desirability function, 𝑑𝑖, that varies

between zero and one (values near zero signify non desirability while values near one

signify desirability). The equations below are used for maximizing the response as is

the case for the compressive strength, tensile strength, and Poisson’s Ratio.

-1.000 -0.500 0.000 0.500 1.000

0.170

0.180

0.190

0.200

0.210

A

ABB

Perturbation

Deviation from Reference Point (Coded Units)

Po

isso

n's

Rat

io


110

𝑑𝑖 = (𝑦𝑖 − 𝐿

𝑈 − 𝐿)

𝑤𝑖

, 𝐿 ≤ 𝑦𝑖 ≤ 𝑈

𝑑𝑖 = 0, 𝑦𝑖 < 𝐿

𝑑𝑖 = 1, 𝑦𝑖 > 𝑈

The equations below are used for minimizing the response as is the case for the

MOE.

𝑑𝑖 = (𝑈 − 𝑦𝑖

𝑈 − 𝐿)

𝑤𝑖

, 𝐿 ≤ 𝑦𝑖 ≤ 𝑈

𝑑𝑖 = 0, 𝑦𝑖 > 𝑈

𝑑𝑖 = 1, 𝑦𝑖 < 𝐿

𝑤𝑖 is the weight, “𝑈” is the upper limit, and “𝐿” is the lower limit of the

response. With each response having the same level of importance, each 𝑑𝑖 is

combined using the geometric mean to calculate the overall DF. The equation for the

DF is shown below.

𝐷𝐹 = (𝑑1 ∙ 𝑑2 ∙ 𝑑3 ∙ 𝑑4)1/4

Through the multi-objective optimization process, fiber dosages of 0.15% ANF

and 0.09% PP fibers BWOC resulted in the highest overall DF of 0.900. Table 7.11

shows the 𝑑𝑖 for each response used to calculate the overall DF. The produced ramp

diagrams are also shown in Figure 7.44.


111

Table 7.11: Multi-objective optimization and experimental validation

Mechanical

property

tests

ANF (%

BWOC)

PP fibers

(%

BWOC)

DF Optimization

Value

Experimental

Value Variation

Compressive

Strength

(MPa)

0.15 0.09 0.997 25.50 25.27 0.91%

Tensile

Strength

(MPa)

0.15 0.09 0.846 2.77 2.83 2.14%

MOE (GPa) 0.15 0.09 0.833 9.54 9.68 1.46%

Poisson's

Ratio 0.15 0.09 0.935 0.205 0.204 0.49%

Figure 7.44: Ramp diagrams for optimization

Additionally, an experimental validation procedure was conducted to affirm

the accuracy of the optimized values. Each response was experimentally tested using

the multi-objective optimized fiber dosages. As can be seen in Table 7.11, the

experimental values varied from the predicted values by less than 3%, indicating

adequate agreement between the measurements.


112


7.3.1 Data set description

The data set consists of 195 UCS values which were used to train, validate, and

test the reliability of the developed ANN model. The measured input features, for each

UCS test, are comprised of the following:

1. the amount of nano-SiO2, nano-SiO2 in % BWOC

2. the amount of nano-Al2O3, nano-Al2O3 in % BWOC

3. the amount of nano-TiO2, nano-TiO2 in % BWOC

4. the cement density, ρ-cement in g/cm3

5. the simulated wellbore curing temperature, T in °C

Each of the five input features greatly influences the UCS of the cement

specimens. The UCS is the output parameter to predict measured in MPa.

7.3.2 Data set analysis

Before passing the data through the ANN model, the characteristics of the data

set is analyzed. The summary statistics (e.g. minimum, maximum, mean, median, and

standard deviations) for each input feature and the output parameter is presented in

Table 7.12.

Table 7.12: Summary statistics of data set

Variable Minimum Maximum Mean Median Standard

deviation

Nano-SiO2 (% BWOC) 0.00 0.30 0.07 0.00 0.10

Nano-Al2O3 (% BWOC) 0.00 0.30 0.07 0.00 0.10

Nano-TiO2 (% BWOC) 0.00 0.30 0.06 0.00 0.10

ρ-cement (g/cm3) 1.87 1.97 1.91 1.92 0.04

T (°C) 60.00 93.33 76.70 76.67 11.10

UCS (MPa) 20.09 38.13 28.62 28.34 3.59

Pearson’s correlation coefficient was computed between each input feature as

shown in Table 7.13. The results indicate a weak negative linear correlation between

the nanoparticles. This is expected as only one nanoparticle additive was tested at a

time. For instance, the increase in concentration of nano-SiO2 means a decrease in

concentration of nano-Al2O3 resulting in a negative correlation coefficient. Table 7.13


113

also shows a correlation coefficient of nearly zero when comparing the density of

cement and temperature to all input features.

Table 7.13: Pearson's correlation coefficient (r) between input features Nano-Al2O3 Nano-TiO2 ρ-cem T

Nano-SiO2 -0.42 -0.40 0.01 -0.04

Nano-Al2O3 - -0.38 0.03 0.02

Nano-TiO2 - - -0.03 0.03

ρ-cement - - - -0.02

Pearson’s correlation coefficient was also computed between each input

feature and the output parameter as shown in Table 7.14. The density of cement and

the curing temperature exhibit a positive linear relationship with the UCS. This is

expected as the UCS typically increases with an increase in the density of the cement

or the curing temperature. The entire data set is provided in the appendix (Table 10.1).

Table 7.14: Pearson's correlation coefficient (r) between input features and the

output parameter

Nano-SiO2 Nano-Al2O3 Nano-TiO2 ρ-cement T

UCS 0.18 0.02 -0.28 0.70 0.43

7.3.3 Data set preprocessing

Typically, the raw data set is normalized into a suitable range to enhance the

stability of the training process, achieve a higher degree of accuracy, and to improve

the overall performance. Data normalization can also increase the speed of

convergence. Usually, normalization expressions are linear or logarithmic functions. A

simple linear normalization function (show in the equation below) was used in this

study to scale the input data to the range of 0.1-0.9:

Xi,norm= 0.1 + 0.8 × Xi−Xmin

Xmax−Xmin

Xi,norm is the calculated normalized value, Xi is the respective input value,

Xmin is the minimum of the input values, and Xmax is the maximum of the input

values. Essentially, the calculated normalized values lie within the region of the

sigmoid activation function where the output is most sensitive to variations in the

input values.


114

7.3.4 Assessment of pre-dispersed nanoparticles

It is necessary to ensure an effective dispersion of nanoparticles before their

inclusions into the cement matrix. An effective dispersion of nanoparticles helps

ensure improved performance in composite materials. Figure 7.45 displays the nano-

SiO2 pre-dispersed solution, Figure 7.46 displays the nano-Al2O3 pre-dispersed

solution, and Figure 7.47 displays the nano-TiO2 pre-dispersed solution.

Figure 7.45: Nano-SiO2 pre-dispersed solution


115

Figure 7.46: Nano-Al2O3 pre-dispersed solution


116

Figure 7.47: Nano-TiO2 pre-dispersed solution

Upon observation of Figure 7.45 and Figure 7.46, both nanoparticles seem to

be effectively dispersed throughout the solution. No apparent sights of agglomeration

are visible which should help improve the UCS of the cement specimens. However,

Figure 7.47 shows clumps of nano-TiO2 particles dispersed throughout the solution. It

is evident nano-TiO2 particles exhibit the tendency to agglomerate. The tendency of

nano-TiO2 particles to agglomerate, even after ultrasonication, is attributed to their

high surface area and high interfacial energies (Lee 2012; Ma et al. 2015). Thus,

researchers typically use lower concentrations of nano-TiO2 to improve the

mechanical properties of cement. For instance, (Salman, Eweed, and Hameed 2016)

increased the compressive and flexural strength of ordinary cement mortar with low

concentrations of nano-TiO2 particles. High concentrations of nano-TiO2 particles


117

resulted in a decrease in mechanical properties due to nanoparticle agglomeration; this

creates weak zones throughout the cement matrix. Likewise, in this research, lower

concentrations of nano-TiO2 particles improved the UCS of the cement material while

higher concentrations were detrimental. Similar results were also ascertained with

nano-SiO2 and nano-Al2O3 particles. Although, nano-TiO2 particles significantly

underperformed in improving the UCS of cement samples when compared to the other

nanoparticles. The results of these experiments can be viewed in the appendix (Table

10.1).

7.3.5 ANN model selection

In this section, the number of nodes in the hidden layer were varied to

investigate the different network architectures. This was done to select the optimum

network as the number of nodes in the hidden layer determines the accuracy of the

network. It is worth noting that increasing the number of nodes in the hidden layer

increases the complexity of the model. Increasing the model complexity can improve

the model’s predictive accuracy. However, a neural network with too many nodes may

overfit the data. This can cause poor generalization on data not used for training.

Conversely, using too few hidden nodes can result in underfitting the model, leading

to inaccurate predictions. Therefore, a trial and error approach was implemented to

determine the optimum number of nodes in the hidden layer.

The number of nodes in the hidden layer varied between 1 to 20 in increments

of 1. The performance of each model was investigated by calculating the MSE of the

network. The results are shown in Figure 7.48.


118

Figure 7.48: The performance of each network with differing number of hidden nodes

According to Figure 7.48, twelve nodes in the hidden layer results in the lowest

MSE (1.819 (MPa2)). Thus, further investigation on the ability of the neural network

to predict cement sample’s UCS were executed with twelve nodes in the hidden layer

(Figure 7.49).


119

Figure 7.49: Final network architecture for optimum performance

7.3.6 ANN prediction of UCS

In order to avoid overfitting, the validation-based early stopping procedure was

implemented. This procedure uses the training data set to update the network’s

weights and biases during training. Also, during training, the error on the validation

data set is monitored. The validation error typically decreases during the initial phase

of training, as does the training error. However, when the network begins to overfit the

data, the error on the validation set typically begins to increase. Finally, the testing

error is also recorded which is used during the application phase.

Figure 7.50 displays training, validation, and testing record MSE values

against the number of training epochs. According to the results, the network was

trained for a total of 13 epochs. When the validation error fails to decrease for six

iterations, the training stops, and the best performance is taken from the epoch with

the lowest validation error. Thus, the model parameters were saved and used at epoch

seven.


120

Figure 7.50: MSE training record of training, validation, and testing data

The predicted UCS values calculated by the ANN model against the target

values are displayed in Figure 7.51. The training, validation, testing, and entire data

set models, all show values of the square root of the coefficient of determination to be

0.930, 0.921, 0.931, and 0.927, respectively. A value of one is the ideal condition for

the square root of the coefficient of determination. However, the findings by Smith

(Smith 1986) have substantiated that satisfactory results can be achieved if the square

root of the coefficient of determination is greater than 0.8. Figure 7.51 shows all

values of the square root of the coefficient of determination are greater than 0.8.


121

Figure 7.51: Square root of the coefficient of determination for the ANN model

Satisfactory results can also be achieved if the difference between the predicted

and target values are kept to a minimum (Smith 1986). Table 7.15 displays the

statistical performance measures of the training, validation, testing, and entire data set

models.

Table 7.15: Statistical performance measures

Indicator Training Validation Testing All

MSE (MPa2) 1.754 1.971 1.972 1.819

R 0.930 0.921 0.931 0.927

MAPE (%) 3.864 4.147 4.069 3.936


122

According to Table 7.15, the MSE, “R”, and MAPE are close to zero, one, and

less than five percent, respectively for all testing stages. Most importantly, the test

data set indicates that the constructed ANN model has high prediction accuracy. The

model has the ability to generalize and provide accurate predictions on unseen data.


123

8. CONCLUSIONS AND RECOMMENDATIONS

In this section, the conclusions for all three phases of this project are

summarized. Recommendations for future work in this project are also presented.


1. The pre-dispersed ANF solution possesses better dispersibility than the ball milled

solution due to varying dispersive methods. The results were confirmed by UV-vis

analysis and TEM images.

2. The compressive and tensile strengths are increased with 0.1% ANF BWOC due to

the bridging effect, the increase in C-S-H due to the seeding effect, and ANFs

rough exterior which increases the fiber/matrix interaction. The additional

presence of Al2O3 may also promote the formation of tobermorite.

3. Dosages of ANFs higher than 0.1% BWOC results in a decrease of mechanical

properties due to nanofiber clustering. Entangled clumps of nanomaterial form in

the cement nanopores that create weak zones in the cementitious matrix.

4. ANFs do not significantly affect the rheological properties, free fluid,

sedimentation, and thickening time for all dosages of nanofibers used in the first

batch of cement formulations. This is considering the use of dispersant is

employed.

5. All batch two cement formulations were able to withstand confining pressure

cycling considering there was no dramatic increase in permeability. Although, the

ANF-1 formulation possessed the lowest permeability at 1.44 μD. This is

essentially due to the high aqueous adsorption capacity to water, allowing the

formation of C-S-H around ANFs creating a denser microstructure with lower

permeability than the Ref sample. The permeability, however, gradually increases

with higher dosages of ANFs causing the formation of nano and microcracks due

to irregularities in the pore structures.

6. All batch two cement formulations experienced low inelastic deformation during

confining pressure cycling. ANF-1 possessed the lowest MOE among all the

formulations and the lowest discrepancy between the 𝑉𝑝 and 𝑉𝑠 waves during

pressure cycling. This is further indication that the ANF-1 formulation contained

the least amount of pore spaces after hydration. ANF-1 also possesses the highest

Poisson’s ratio among all cement formulations. The low MOE and high Poisson’s

Ratio essentially indicated better ductility and thus a higher probability of resisting

deformation due to casing expansion/contraction.

7. Batch two cement formulations experienced the same XRD pattern with only

variations in the diffraction peaks. ANF-1 possessed the highest amount of C-S-H

due primarily to the nucleation effect (seeding effect) which provides additional

surface area for nucleation sites of C-S-H formation.

8. In the second batch of cement formulations, the DOH was the highest for the

ANF-1 formulation. This is due to the seeding effect which effectively enhances


124

hydration. This increase in hydration essentially improves the mechanical

performance and microstructural properties of the cement composites.

9. ANF is a relatively inexpensive material, with substantial potential to be utilized in

the oil well cement industry. Essentially, ANF can help to avoid secondary cement

jobs and improve the overall wellbore integrity.

8.2 Cement hybridization scheme of ANFs and micro-synthetic PP fibers: Phase two

1. SiO2 nanoparticles were successfully deposited on the surface of micro-synthetic

PP fibers to improve the fiber/matrix bond.

2. After curing samples at 82.2°C with 20.68 MPa for 24 hours, the ultimate

compressive and tensile strengths were attributed to the reinforcement of ANF.

The ultimate strength contribution from PP fibers is absent due to the strength

difference in the fibers and the inability of PP fibers to resist stresses before cracks

appear as opposed to post-cracking.

3. The ultimate MOE and Poisson’s Ratio were synergistically improved on the nano

and microscale levels. The improvement is attributed to the flexibilities of both

fibers and because the cement sample is still within the elastic region.

4. The CCD method for two factors was effectively utilized to model each response.

All responses resulted in quadratic formulations with model p-values less than

0.05 indicating model significance with at least a 95% confidence interval

according to the ANOVA table. The lack of fit was insignificant with reliable

precision according to the regression model summary. The RSM models were also

validated through residual plots and an adequate agreement was established

between the predicted and actual data.

5. The multi-objective optimization of the responses resulted in the highest overall

DF of 0.900 encompassing 0.15% ANF and 0.09% PP fibers (BWOC). The

experimental validation also suggests adequate model precision with less than 3%

variation.


1. Nano-SiO2 and nano-Al2O3 displayed excellent dispersibility throughout the

aqueous solution. However, nano-TiO2 readily agglomerates which, at high

concentrations, is detrimental to the UCS of cement.

2. 195 cement samples were tested and used as examples. 70% was used for training,

15% was used for validation, 15% was used to test the model, and a maximum of

10,000 epochs were used.

3. Twelve nodes in the hidden layer resulted in the lowest MSE which produced a

network having one input layer with five nodes, one hidden layer with twelve

nodes, and one output layer with one node. The model parameters were saved and

used after seven epochs during training, at which point the validation error began

to increase leading to overfitting.


125

4. The statistical performance measures showed all MSE values close to zero, “R”

values close to one, and MAPE values less than five percent for all testing stages.

This indicates that the constructed ANN model possesses high predictive accuracy.

5. Thus, ANN models can replace, or be used in combination with, destructive UCS

tests which can significantly save the petroleum industry time, resources, and

capital.

8.4 Future work

1. The applicability of using ANFs in high temperature (greater than 110°C) and high

pressure (greater than 68.95 MPa) conditions in oil well cement composites should

be assessed.

2. The compatibility of other additives such as accelerators, retarders, and defoamers

should be assessed in conjunction with ANFs in oil well cement composites.

3. More tests should be conducted to expand the data set range for strength

predictions using machine learning.

4. More advanced modeling techniques are needed in order to understand the

hydration process, in its entirety, of cement embedded with nanomaterials.

5. Studies should be conducted in order to assess the long-term effects of oil well

cement embedded with nanomaterials.


126

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10. APPENDIX

Table 10.1: UCS tests: Phase three

Nano-silica

(% BWOC)

Nano-alumina

(% BWOC)

Nano-Titanium

dioxide (% BWOC)

Density

(g/cm3)

Temperature

(°C)

UCS

(Mpa)

0.10 0.00 0.00 1.87 60.00 24.77

0.10 0.00 0.00 1.89 60.00 26.86

0.10 0.00 0.00 1.94 60.00 31.50

0.10 0.00 0.00 1.97 60.00 30.47

0.20 0.00 0.00 1.87 60.00 24.95

0.20 0.00 0.00 1.92 60.00 26.70

0.30 0.00 0.00 1.87 60.00 22.23

0.30 0.00 0.00 1.92 60.00 27.11

0.30 0.00 0.00 1.94 60.00 28.22

0.30 0.00 0.00 1.97 60.00 29.52

0.10 0.00 0.00 1.87 65.56 23.75

0.10 0.00 0.00 1.89 65.56 26.65

0.10 0.00 0.00 1.92 65.56 30.09

0.10 0.00 0.00 1.97 65.56 32.82

0.20 0.00 0.00 1.87 65.56 24.68

0.20 0.00 0.00 1.92 65.56 30.42

0.20 0.00 0.00 1.94 65.56 31.90

0.20 0.00 0.00 1.97 65.56 33.45

0.30 0.00 0.00 1.87 65.56 26.28

0.30 0.00 0.00 1.92 65.56 30.65

0.30 0.00 0.00 1.97 65.56 31.09

0.10 0.00 0.00 1.87 71.11 25.87

0.10 0.00 0.00 1.89 71.11 29.79

0.10 0.00 0.00 1.94 71.11 33.28

0.10 0.00 0.00 1.97 71.11 33.02

0.20 0.00 0.00 1.87 71.11 26.46

0.20 0.00 0.00 1.92 71.11 29.29

0.20 0.00 0.00 1.94 71.11 33.32

0.20 0.00 0.00 1.97 71.11 32.71

0.30 0.00 0.00 1.87 71.11 25.37

0.30 0.00 0.00 1.92 71.11 31.66

0.30 0.00 0.00 1.97 71.11 33.88

0.10 0.00 0.00 1.87 76.67 25.10

0.10 0.00 0.00 1.92 76.67 31.11

0.10 0.00 0.00 1.97 76.67 32.75

0.20 0.00 0.00 1.87 76.67 26.12

0.20 0.00 0.00 1.89 76.67 28.15


141

Nano-silica

(% BWOC)

Nano-alumina

(% BWOC)

Nano-Titanium

dioxide (% BWOC)

Density

(g/cm3)

Temperature

(°C)

UCS

(Mpa)

0.20 0.00 0.00 1.92 76.67 27.39

0.20 0.00 0.00 1.97 76.67 32.18

0.30 0.00 0.00 1.87 76.67 25.68

0.30 0.00 0.00 1.89 76.67 25.90

0.30 0.00 0.00 1.94 76.67 26.50

0.10 0.00 0.00 1.87 82.22 26.21

0.10 0.00 0.00 1.89 82.22 29.52

0.10 0.00 0.00 1.94 82.22 33.69

0.10 0.00 0.00 1.97 82.22 35.71

0.20 0.00 0.00 1.87 82.22 25.37

0.20 0.00 0.00 1.92 82.22 32.96

0.20 0.00 0.00 1.97 82.22 34.83

0.30 0.00 0.00 1.87 82.22 26.51

0.30 0.00 0.00 1.89 82.22 25.61

0.10 0.00 0.00 1.87 87.78 27.82

0.10 0.00 0.00 1.92 87.78 32.91

0.10 0.00 0.00 1.97 87.78 35.91

0.20 0.00 0.00 1.87 87.78 26.80

0.20 0.00 0.00 1.89 87.78 31.53

0.20 0.00 0.00 1.97 87.78 36.54

0.30 0.00 0.00 1.89 87.78 26.39

0.30 0.00 0.00 1.97 87.78 33.11

0.10 0.00 0.00 1.87 93.33 28.04

0.10 0.00 0.00 1.89 93.33 32.02

0.10 0.00 0.00 1.97 93.33 37.18

0.20 0.00 0.00 1.87 93.33 27.63

0.20 0.00 0.00 1.92 93.33 33.86

0.20 0.00 0.00 1.97 93.33 38.13

0.30 0.00 0.00 1.87 93.33 29.89

0.30 0.00 0.00 1.92 93.33 33.54

0.30 0.00 0.00 1.94 93.33 32.71

0.30 0.00 0.00 1.97 93.33 34.39

0.00 0.10 0.00 1.87 60.00 20.09

0.00 0.10 0.00 1.89 60.00 20.62

0.00 0.10 0.00 1.97 60.00 28.46

0.00 0.20 0.00 1.87 60.00 23.06

0.00 0.20 0.00 1.92 60.00 25.60

0.00 0.20 0.00 1.94 60.00 27.04

0.00 0.20 0.00 1.97 60.00 30.39

0.00 0.30 0.00 1.89 60.00 23.01


142

Nano-silica

(% BWOC)

Nano-alumina

(% BWOC)

Nano-Titanium

dioxide (% BWOC)

Density

(g/cm3)

Temperature

(°C)

UCS

(Mpa)

0.00 0.30 0.00 1.92 60.00 26.17

0.00 0.30 0.00 1.97 60.00 29.22

0.00 0.10 0.00 1.87 65.56 24.69

0.00 0.10 0.00 1.89 65.56 26.12

0.00 0.10 0.00 1.94 65.56 26.83

0.00 0.20 0.00 1.89 65.56 25.15

0.00 0.20 0.00 1.97 65.56 31.43

0.00 0.30 0.00 1.89 65.56 27.30

0.00 0.30 0.00 1.94 65.56 28.50

0.00 0.10 0.00 1.87 71.11 24.93

0.00 0.10 0.00 1.89 71.11 24.15

0.00 0.10 0.00 1.97 71.11 28.91

0.00 0.20 0.00 1.87 71.11 26.61

0.00 0.20 0.00 1.89 71.11 26.12

0.00 0.20 0.00 1.94 71.11 29.99

0.00 0.20 0.00 1.97 71.11 29.73

0.00 0.30 0.00 1.89 71.11 28.07

0.00 0.30 0.00 1.92 71.11 29.01

0.00 0.30 0.00 1.97 71.11 30.19

0.00 0.10 0.00 1.87 76.67 25.43

0.00 0.10 0.00 1.92 76.67 29.01

0.00 0.10 0.00 1.97 76.67 30.35

0.00 0.20 0.00 1.89 76.67 28.61

0.00 0.20 0.00 1.92 76.67 29.18

0.00 0.20 0.00 1.97 76.67 31.84

0.00 0.30 0.00 1.87 76.67 25.34

0.00 0.30 0.00 1.89 76.67 28.14

0.00 0.30 0.00 1.94 76.67 32.20

0.00 0.10 0.00 1.87 82.22 25.59

0.00 0.10 0.00 1.89 82.22 24.78

0.00 0.10 0.00 1.97 82.22 31.96

0.00 0.20 0.00 1.87 82.22 25.83

0.00 0.20 0.00 1.89 82.22 27.17

0.00 0.20 0.00 1.92 82.22 28.41

0.00 0.20 0.00 1.97 82.22 32.45

0.00 0.30 0.00 1.89 82.22 28.34

0.00 0.30 0.00 1.94 82.22 28.57

0.00 0.30 0.00 1.97 82.22 30.53

0.00 0.10 0.00 1.87 87.78 26.71

0.00 0.10 0.00 1.92 87.78 30.26


143

Nano-silica

(% BWOC)

Nano-alumina

(% BWOC)

Nano-Titanium

dioxide (% BWOC)

Density

(g/cm3)

Temperature

(°C)

UCS

(Mpa)

0.00 0.10 0.00 1.94 87.78 32.49

0.00 0.20 0.00 1.87 87.78 29.10

0.00 0.20 0.00 1.89 87.78 28.27

0.00 0.20 0.00 1.94 87.78 34.72

0.00 0.30 0.00 1.87 87.78 27.44

0.00 0.30 0.00 1.89 87.78 26.73

0.00 0.30 0.00 1.97 87.78 35.62

0.00 0.10 0.00 1.87 93.33 29.45

0.00 0.10 0.00 1.89 93.33 31.77

0.00 0.10 0.00 1.94 93.33 33.16

0.00 0.10 0.00 1.97 93.33 32.49

0.00 0.20 0.00 1.87 93.33 29.29

0.00 0.20 0.00 1.94 93.33 34.56

0.00 0.20 0.00 1.97 93.33 36.82

0.00 0.30 0.00 1.87 93.33 26.23

0.00 0.30 0.00 1.92 93.33 29.04

0.00 0.30 0.00 1.97 93.33 35.11

0.00 0.00 0.10 1.87 60.00 23.08

0.00 0.00 0.10 1.89 60.00 25.33

0.00 0.00 0.10 1.97 60.00 30.08

0.00 0.00 0.20 1.87 60.00 22.71

0.00 0.00 0.20 1.92 60.00 28.78

0.00 0.00 0.20 1.94 60.00 29.85

0.00 0.00 0.20 1.97 60.00 29.07

0.00 0.00 0.30 1.87 60.00 22.53

0.00 0.00 0.30 1.94 60.00 26.03

0.00 0.00 0.10 1.87 65.56 26.77

0.00 0.00 0.10 1.89 65.56 25.15

0.00 0.00 0.20 1.89 65.56 21.04

0.00 0.00 0.20 1.94 65.56 27.68

0.00 0.00 0.30 1.94 65.56 29.73

0.00 0.00 0.10 1.89 71.11 28.34

0.00 0.00 0.10 1.92 71.11 27.39

0.00 0.00 0.10 1.97 71.11 29.68

0.00 0.00 0.20 1.87 71.11 22.48

0.00 0.00 0.20 1.89 71.11 26.61

0.00 0.00 0.20 1.94 71.11 28.32

0.00 0.00 0.20 1.97 71.11 30.47

0.00 0.00 0.30 1.87 71.11 21.91

0.00 0.00 0.30 1.89 71.11 21.20


144

Nano-silica

(% BWOC)

Nano-alumina

(% BWOC)

Nano-Titanium

dioxide (% BWOC)

Density

(g/cm3)

Temperature

(°C)

UCS

(Mpa)

0.00 0.00 0.30 1.92 71.11 24.45

0.00 0.00 0.30 1.97 71.11 24.17

0.00 0.00 0.10 1.87 76.67 27.03

0.00 0.00 0.10 1.92 76.67 26.30

0.00 0.00 0.10 1.94 76.67 26.06

0.00 0.00 0.20 1.89 76.67 25.15

0.00 0.00 0.20 1.94 76.67 30.04

0.00 0.00 0.20 1.97 76.67 29.08

0.00 0.00 0.30 1.87 76.67 21.10

0.00 0.00 0.30 1.89 76.67 24.20

0.00 0.00 0.30 1.92 76.67 23.95

0.00 0.00 0.30 1.97 76.67 27.72

0.00 0.00 0.10 1.87 82.22 26.83

0.00 0.00 0.10 1.89 82.22 25.43

0.00 0.00 0.10 1.97 82.22 32.10

0.00 0.00 0.20 1.87 82.22 24.08

0.00 0.00 0.20 1.92 82.22 30.87

0.00 0.00 0.20 1.97 82.22 30.79

0.00 0.00 0.30 1.87 82.22 26.41

0.00 0.00 0.30 1.92 82.22 25.75

0.00 0.00 0.30 1.94 82.22 32.34

0.00 0.00 0.10 1.87 87.78 27.59

0.00 0.00 0.10 1.94 87.78 28.10

0.00 0.00 0.20 1.89 87.78 27.16

0.00 0.00 0.20 1.92 87.78 29.47

0.00 0.00 0.20 1.97 87.78 31.54

0.00 0.00 0.30 1.87 87.78 24.23

0.00 0.00 0.30 1.89 87.78 26.27

0.00 0.00 0.10 1.87 93.33 30.16

0.00 0.00 0.10 1.89 93.33 31.03

0.00 0.00 0.10 1.94 93.33 31.76

0.00 0.00 0.10 1.97 93.33 32.89

0.00 0.00 0.20 1.87 93.33 29.67

0.00 0.00 0.20 1.92 93.33 30.69

0.00 0.00 0.20 1.97 93.33 33.93

0.00 0.00 0.30 1.87 93.33 25.52

0.00 0.00 0.30 1.89 93.33 27.08

0.00 0.00 0.30 1.97 93.33 33.65

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