© Repsol Technology Lab. © Repsol Technology Lab.

Reservoir Petrophysical Properties

estimation from drill cuttings using

advanced data analytics

Outline

Introduction

Objective

Proof of concept

Available data

Data Analytics

Outlook

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Repsol Reservoir Characterization

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Well Log Data Cuttings

Description

Performed in a limited number of wells

Not taken in development wells

Core Data

Opportunities

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Complementary analytical approach

Can we extract more

information from drilled

cuttings through data

analytics?

Fast and low cost subsurface characterization

Geological Conceptual Model improved:

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Multiphysics

Properties derived

from cuttings

Rock Physics

Properties

Case: Porosity = f ( )

To Develop predictive models based on multiphysics

cutting’s derived properties to estimate the porosity.

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Proof of Concept

Cores

Plugs

RCA/SCAL

Artificial

Cuttings

Multivariate Predictive

Model

𝜀 = 𝑅𝐶𝐴𝐿 −𝑅𝐶𝐴𝐿

XRF

RGB

RCA/SCAL

GR

Input Hard data

(Target)

Input Soft data

(Training)

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Standard Sandstones with different permeability. Cores

LOW MEDIUM HIGH

Three families made up of four different sandstones

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Standard Sandstones with different permeability. Artificial Cuttings

www.yourwebsite.com

Data Collection

02 03 04 05

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Data Analytics Workflow

01

Data

Preparation

Exploratory

Data Analysis

Model

Development

Model

Deployment

Porosity (%)

Pe

rme

ab

ilit

y (

md

)

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Target Data

The structure in the standard data

set is captured by unsupervised

learning algorithms

Porosity (%)

Family mean std kurtosis skeweness

Low 13.19 2.57 -1.27 0.01

Medium 20.67 3.53 -1.24 0.55

High 26.27 5.16 -1.42 0.48

All 20.04 6.63 -0.40 0.44

XRF Feature

RG

B F

ea

ture

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Training Data

The cutting’s extracted features have a

discriminative capacity of the three

main families in the standard data set

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Training Data. Feature Selection

0 no linear relationship between two variables

•1 strong positive linear relationship

•-1 strong negative linear relationship

19 selected XRF features from 52

11 selected RGB features from 46

All selected GR

Start with 42 predictive features

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Model Performance Evaluation

A convergence of model performance with six features

Cross-validation Method: Leave One Out

Error:

Basic Predictive Model

Preliminary Results

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Model Performance Evaluation

Basic Tailored Predictive Model

SVM Lasso K-nn Ridge

Family Low 28.12 34.72 34.93 28.95

Family Medium 22.78 26.17 17.79 27.57

Family High 27.77 13.1 14 13.62

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00E

rro

r (

%)

Model Performance Comparison

Preliminary Results

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Model Performance Evaluation

Porosity

distribution from

Plugs

Porosity

distribution

obtained from

cutting’s predictive

model

Preliminary Results

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

Model Performance Evaluation Preliminary Results

Sample Actual

Porosity (%)

Mean

Actual Porosity

SD

Tolerance

resulted from

the model

(MAPE = 17%)

Sandtone 1 9.87 ±0.524 ± 1.67

Sandtone 2 14.20 ± 0.359 ± 2.41

Sandtone 3 12.10 ± 0.067 ± 2.05

Sandtone 4 16.59 ± 0.119 ± 2.82

Sandtone 5 21.41 ± 0.036 ± 3.64

Sandtone 6 25.93 ± 0.106 ± 4.40

Sandtone 7 18.40 ± 0.059 ± 3.12

Sandtone 8 16.93 ±0.073 ± 2.87

Sandtone 9 33.72 ±0.654 ± 5.73

Sandtone 10 21.26 ±0.097 ±3.61

Sandtone 11 27.95 ±0.180 ±4.75

Sandtone 12 22.15 ±0.645 ±3.76

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

10 -2

10 -1

10 0

10 1

10 2

10 3

0

10

20

30

40

50

60

Permeability (mD)

Fre

qu

en

cy

Rock Physics measurement representability

• Scalars cannot describe

reservoir behavior even on

homogeneous samples.

• Probability Density Functions

provide more insightful

information for further

reservoir modeling. Plug sample

RCAL

1 Point

Core sample (6 feet homogeneous)

AutoScan system

+300 Points

Plug sample

Digital Rock Physics

+20 Points

Digital Reservoir Characterization with Cuttings

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

The multiphysics data (features), extracted from drill cuttings can be obtained in short times.

This proposed methodological approach could allow to obtain relevant petrophysical information on site.

It is not sought to emulate a laboratory value it is sought to capture tendencies and distribution of the property.

This approach can be applied to estimate another properties such as Permeability, Swi, elastic modulus, etc.

Thank you

Introduction Objective PoC Data Analytics Discussion/ Outlook Data

OUTPUT Basic

Predictive Model

Soft-data

INPUT

Porosity

Permeability

Irreducible Water

Lithotypes

Inorganic

Geochemistry

GR

Digital Imaging

(RGB)

Cuttings

Error/Accuracy

OUTPUT Advanced

Predictive Model

Soft-data

INPUT

Porosity

Permeability

Irreducible Water

Cuttings

Better Accuracy

Lithologies

Digital Rock

Physics (2D)

Petrofacies

Sonic Velocity

Hardness

Elastic Modulus

Probability

Density Functions

(Upscaling)

Rock Physics Rock Mechanics Geology