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© 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