Final-Ahmed Alzahabi-Shale Gas Plays Screening Spet-4 - Copy
description
Transcript of Final-Ahmed Alzahabi-Shale Gas Plays Screening Spet-4 - Copy
-
Shale Gas Plays Screening Criteria
A Sweet Spot Evaluation Methodology
A. Algarhy, M. Soliman, R. Bateman, and G. Asquith
Prepared to submitted to Fracturing Impacts and Technologies Conference
Texas Tech University, Lubbock, TX, USA
Sept. 2014
Ahmed Alzahabi, PhD Candidate
Bob L. Herd Department of Petroleum Engineering
Well Placement and Fracturing Optimization Research Team, TTU
1
-
Agenda
Introduction
Objectives
Shale Success Factor
Building database for major shale plays
Shale Expert System( Toolbox, Benchmark)
Application of Shale Expert System
Conclusions & Recommendations 2
-
Introduction
No. Shale play
1 Barnett
2 Ohio
3 Antrim
4 New Albany
5 Lewis
6 Fayetteville
7 Haynesville
8 Eagle Ford
9 Marcellus
10 Woodford
11 Bakken
12 Horn River
> 70 shale-gas-plays 3
-
Objectives
Develop a candidate evaluation algorithm.
Develop an algorithm that considers geomechanical, petrophysical and
geochemical parameters of a newly discovered shale.
Provide a guiding database for major productive shale plays in North
America and list all possible potential
Develop guidelines to identify the sweet spots in unconventional resources.
5
-
Building Success Factor
AlgorithmStatistics
Database Structure
Shale Success Factor
[0-100 %]
Candidate
Evaluation
6
-
Data Structure
1. Shale Plays Spider Plot
2. Completion Strategies
3. Mineralogy Comparison
4. Mechanical Properties
5. Shale Plays Characteristics
6. Shale Gas Production Indicators
7. Sweet Spot Identifier
9
-
Data Structure, Common shale plays spider plot
0102030405060708090
100TOC
RO
Total Porosity
Net ThicknessAdsorbed Gas
GasContent
Depth
Spider Plot
Barnett
Ohio
Antrim
New Albany
Lewis
Fayettevillle
Haynesville
Eagle Ford
Woodford
Bakken
Horn River
10
-
Data Structure Completion Strategies:
No. Shale play Average Frac Stage Count. Average later length, ft.
1 Barnett 10-20 3500
2 Ohio n/a n/a
3 Antrim n/a n/a
4 New Albany n/a n/a
5 Lewis n/a n/a
6 Fayetteville 5 4000
7 Haynesville 10 4000-7000
8 Eagle Ford 10 2500
9 Marcellus 8 2900
10 Woodford n/a n/a
11 Bakken 14 9250
12 Horn River 11 4500
11
-
Data Structure Mineralogy Comparison of shale gas plays:
No. Shale play Quartz,% Feldspar,% Clay,% Pyrite,% Carbonate,% Kerogen, %
1 Barnett 35-50 6-7 10-50 5-9 0-30 4.0
2 Ohio n/a n/a 15-57 n/a 7-80 n/a
3 Antrim 40-60% n/a n/a n/a 0-5% n/a
4 New Albany 28-47 % 2.1-5.1 11-23 3-9 0.5-2.5 n/a
5 Lewis 56 n/a 25 n/a n/a n/a
6 Fayetteville 45-50 n/a 5-25 n/a 5-10 n/a
7 Haynesville 23-35 0-3 20-39 n/a 20-53 4-8
8 Eagle Ford 11-50 n/a 20 n/a 46-78 4-11
9 Marcellus 10-60 0-4 10-35 5-13 3-50 5.1
10 Woodford 48-74 3-10 7-25 0-10 0-5 7-16
11 Bakken 40-90 15-25 2-18 5-40 8-16
12 Horn River 9-60 0-3 28-78 4-10 0-9 n/a
12
Wt %
-
Data Structure Mechanical Properties of Shale Gas Plays:
No. Shale play E
1 Barnett 3.5 E+06 0.2
2 Ohio n/a n/a
3 Antrim n/a n/a
4 New Albany n/a n/a
5 Lewis n/a n/a
6 Fayetteville 2.75 E+06 0.22
7 Haynesville 2.00 E+06 0.27
8 Eagle Ford 1.00:4.00 E+06 019:0.27
9 Marcellus 2.00 E+06 0.26
10 Woodford 5.00 E+06 0.18
11 Bakken 6.00 E+06 0.22
Horn River 3.64 E+06 0.23
13
-
Data Structure Shale Plays Characteristics
Some parts from Curtis 2002.
parameters
Shales TOC RO Total Porosity Net Thickness Adsorbed Gas Gas Content Depth
Permeability,
ndGeological Age
1 Barnett 4.50 2.00 4.50 350.00 25 325 6500 25-450 Mississipian
2 Ohio 2.35 0.85 4.70 65.00 50 80 3000 n/a Devonian3 Antrim 5.50 0.50 9.00 95.00 70 70 1400 n/a Upper Devonian
4 New Albany 12.50 0.60 12.00 75.00 50 60 1250 n/a Devonian and Mississippian
5 Lewis 0.45-1.59 1.74 4.25 250.00 72.5 29.5 4500 n/a Devonian and Mississippian
6 Fayettevillle 6.75 3.00 5.00 110.00 60 140 4000 n/a Mississippian
7 Haynesville 3 2.2 7.3 225 18 215 12000 10-650 Upper Jurassic
8 Eagle Ford 4.5 1.5 9.7 250 35 150 11500 1100-2500 Upper Cretaceous
9 Marcellus 3.25 1.25 4.5 350 50 80 6250 n/a Devonion
10 Woodford 7 1.4 6 150 n/a 250 8500 145-206Late Devonian -Early
Mississippian)
11 Bakken 10 0.9 5 100 n/a n/a 10000 n/a Uppper Devonion
12 Horn River 3 2.5 3 450 34 n/a 8800 150-450 n/a
14
-
15
No. Shale play Configuration of horizontal wells Completion Style Frac Design
1 Barnett 10-12 % fracturing fluid as a pad 75-85% as
a sand laden slurry
2 Ohio
3 Antrim
4 New Albany
5 Lewis
6 Fayetteville
7 Haynesville
8 Eagle Ford
9 Marcellus
10 Woodford
11 Bakken Single Lateral, Multilateral Barefoot open hole
Non-isolated uncemented preperforated liner
Frac ports/ball activated sleeves
Plug and perf
Slick water/ gel
Plug and perf
Frac ports/ ball activated sleeves
100 mesh, 40/70,30/50, 20/40, 16/20, 12/18,
12 Horn River Plug and perf Slick water, 15 stages, 200 tonnes/ stage, 17.
6 Mbbls/stage
Completion Strategy for each shale play
-
16
parameters
Shales Decline Historic Production area
1 Barnett Wise County, Texas2 Ohio Pike County, Kentucky 3 Antrim Otsego, County, Michigan
4 New Albany Harrison County, Indiana
5 LewisHyperbolic
(5.6%)
San Juan & Rio Arriba Counties,
New Mexico.
6 Fayettevillle
7 Haynesville
8 Eagle Ford
9 Marcellus
10 Woodford
11 Bakken
12 Horn River
Shale Gas Reservoirs of Utah Steven Schamel Sept. 2005
ProductionPotential for each shale play
-
Data Structure Average Shale Characteristics
Based on 10,000 shales (Yaalon, 1962), after Asquith Class
Clay Minerals(mostly Illite ) 59%Quartz and Chert 20%
Feldspar 8%Carbonate 7%Iron Oxides 3%
Organic Material 1%Others 2%
18
-
Data Structure Assessing Shale Plays Potential ReservesSweet Spot Identifier
Parameters Conditions
Brittleness > 45% (Rickman & Mullen criterion)
Young modulus + 3.5 10 ^6 psi (SPE125525)
TOC +1 wt.%
Poisson ratio 1.3% RO
Kerogen Type Type I &II better gas yield than type III
Mineralogy + 40 % Quartz-Calcite/ less Clay (Less clay/low Smectite
-
Algorithm Used; How it works ?
Identifies relationships in a dataset in a form of Spider plot.
Generates a series of clusters based on those relationships.
The clusters group points on the spider plot and illustrate the relationships that the
algorithm identifies.
Calculates how well the cluster groups.
Tries to redefine the groupings to create clusters that better represent the data
The algorithm iterates through this process until it cannot improve the results more
by redefining the clusters.
26
-
Shale Expert System
27
-
28
Shale Gas Reservoirs of Utah Steven Schamel Sept. 2005
-
29
-
30
-
World Shale Plays Potential
33http://www.gidynamics.nl/products/gas-processing/Unconventional-Gas
-
Recommendations
The current model is still in the early stages
Production data for large shale fields need to be considered in the future w
ork.
Decline curve parameters of each major play should be a part of data base.
Trends and patterns should be obtained for the 12 majors shale plays
Clustering similar regions within the same shale is a possible sweet spot
identifying tool, adding it to the expert system37
-
Conclusions
A new shale plays benchmark has been created
The output of this study has an important value to evaluate any shale play and to
suggest future development strategies.
The algorithm check maturity of newly discovered shale play.
The algorithm works as a guide for identifying Sweet Spot, identification operat
ionally approved method help increase the potentiality of existing shale natural
gas accumulations recovery.
36
-
Thank youQuestions ?
38