• Goal: to bring consistency to facies logs thus enhancing the workflows, integration of data, and quality of reservoir modeling • Premise: Facies logs are typically not tuned optimally to the hierarchical geomodeling workflows

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Electrofacies, A Guided Machine Learning For The Practice of Geomodeling
David Garner
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8Electrofacies Modeling

9. Electrofacies, A Guided Machine Learning
For the Practice of Geomodelling
David Garner
9.11.2020
World Class Training Solutions
www.petro-teach.com

10. 1010
Agenda Topics
• Geomodeling context
• Facies and Facies Definitions
• Facies Classification and Motivation to Improve
• Electrofacies Concepts and Application Illustrations
− Unsupervised, supervised, parametric, non-parametric
• Selected Case Examples highlighting different issues
− Heidrun Field – shoreface -Fangst
− Winborne Field – Devonian carbonate reef - Leduc
− Ells River – shoreface - McMurray
− Corner Field – point bar - McMurray
• Geomodeling Impacts
• Summary Remarks
©David Garner

11. 11
Goal:
• Goal: to bring consistency to facies logs thus enhancing the
workflows, integration of data and quality of reservoir modeling
• Premise: Facies logs are typically not tuned optimally to the
hierarchical geomodeling workflows
©David Garner

12. 1212
Facies
VE=7.5
8o dip
Analogues
Classifications
Geomodels
Fluvial
Point Bar
Data
Integration
Garner, et al. 2014; Martinius, et al., 2017
Geo-Modeling Involves Concepts to Models
Statistical & Physical behaviour
Sedimentology
to stratigraphy
©David Garner

13. 13
Rescaling workflows (context)
Geomodelers work with multi-scale data (i.e., different support volumes)
Logs
Blocked wells
Geomodel
Flow model
Seismic model
Core plugs
Models are generated at different scales
Slide modified from

14. 1414
Facies as conditioning data
Facies are used
•To represent and honour reservoir heterogeneities,
Facies+Petrophysics:
−Reservoir architecture described by the sequence of litho-facies
−Variability of the rock properties within each given facies
•As a categorical variable for conditioning geomodels – as “Truth”
•As statistically stationary domains of properties
1D facies is a significant data type influencing the entire
subsurface workflow, from Static Facies to Dynamic
©David Garner

15. 1515
Static Facies to Dynamic
Benefits of Rock Types in the Flow Simulator
Ability to apply parameters by individual rock type:
 Capillary pressure curves, Pc, water trends
 Relative Permeability, Kr, Curve shapes
 Relative Permeability, Kr, End points
 Thermal Properties, e.g. conductivity, diffusivity
 Geomechanical properties, e.g. Young’s, Poisson’s, dilation
Issue …Scaling Facies Models into Rock types …preserving heterogeneity!
©David Garner

16. 1616
Definitions
Practitioners from different disciplines describe the facies variable from their viewpoints:
 Facies: a general term used to reflect any and all of the rock definitions present.
 Litho-facies: describe the characteristics of a real rock. Typically described during
visual inspection of the actual sample.
 Bio-facies and Ichno-facies: describe biological content of actual samples through
visual identification which supports differentiation of depositional environments
 Depositional Facies: a generic term used to account for the depositional environment
and lithofacies assemblages.
 Electro-facies: a term describing log-derived lithologies or rock types using statistical
methods
 Petro-facies: commonly used to describe log-derived lithologies or reservoir rock types
 Rock Types: used as an engineering term for flow studies
©David Garner

17. 1717
Classification of Lithofacies/Electro-Facies
Various approaches
• Combine rock fabric, pore space and petrophysics (e.g. Lucia, F. J., 1995)
• Detailed description of depositional and diagenetic processes from core or image data
• Electrofacies using multivariate statistics using wireline and core or image description
(e.g. Nivlet, et al. 2001; Ye and Rabiller, 2000)
• Rules Based Petrophysical Classification (Petrofacies); e.g. use of cutoffs or polygons
Account for quality of pores & engineering impact
Account for spatial location
All of these approaches require a commitment of time and effort to clean and
understand the database
©David Garner

18. 1818
Advantages of Electrofacies Technology
The under-used application of electrofacies modeling provides a
robust framework of methods:
1. Improved integration and consistency in the application of well data
(conditioning) in the full geomodeling workflow
2. Strengthens geology-to-flow principles
3. Decreased observational bias in facies classification
4. Decreased facies classification uncertainty
5. Faster run time and enabler of first-pass geomodels
6. Reduced need for coring from a geomodeling perspective
©David Garner

19. 19
Barriers to Adoption of Electrofacies
• Workflow steps are not widely established in industry practice or
promoted by s/w vendors.
• A lack of best practice guidance and training.
• Misuse or sub-optimal application
• Lack of dissemination of software to G&G staff beyond
petrophysicists holds back the technology
Solution: Treating the electrofacies practice as an open team
driven interpretation, a guided machine learning process
©David Garner

20. 20
Electrofacies General Theory Illustration
Identify which “facies” class to assign to well log data without facies description:
Inputs: facies description and log/image/seismic curves
Find an orientation along which the two classes have the greatest separation
and the least inflation
After Davis, 1986 Assign unknown samples
Garner, D.L., Woo, A., and Broughton, P., 2009, Applications of 1D Electro-Facies Modeling (Abstract),
presented at the CSPG CSEG CWLS Convention, May
©David Garner

21. 2121
Assignment Functions (classification)
• Linear: Modes have similar size
• Quadratic: Modes have different sizes
• Non-parametric: No size/shape assumption
Centroids
Mahalanobis
distances Assignment probabilities can be
controlled (by posterior weights) to
adjust output facies proportionsCentroids = Center of mass
©David Garner

22. 22
Un-Supervised Discriminant Analysis
Mode mapping or “bump hunting” (kernel estimation)
 Find high densities of points
grouped together
• Can be interpreted as petrofacies
 Define Seed points
• Shown in color in cross plots
 These samples can be used as
the training set for discriminant
analysis
GRRhob
Neut
Neut
Rhob
GR
©David Garner

23. 23
Un-Supervised Method
Classification of samples
Assign all samples in relation
to a function of 3 variables
GR, RHOB, Neutron
GR
Rhob
Rhob
Neut
Neut
©David Garner

24. 24
Un-Supervised
Method
In this example…
Electro-facies have a better visual
relationship to the effective porosity
curve than to the Vshale curve
Porosity = f(Density,GR)
Efacies = f(Density,GR,Neutron)
Electro-facies models can be a
practical way to proceed
Vshale
©David Garner

25. 25
Assumptions (from discriminant analysis as guide to data issues)
1. The observations in each class were randomly chosen.
− Observed facies are not random samples, are spatially biased as is the natural
variability of depositional successions.
2. The probability of an unknown observation belonging to each class is equal
− Facies proportions are not equal in nature.
3. Variables are normally distributed within each class.
− By-facies distributions of log variables have various shapes.
4. The variance-covariance matrices of the classes are equal in size.
− The spread of properties for a given facies may be narrow or wide depending on
lithological characteristics.
5. None of the observations used to calculate the function were misclassified.
− Facies and logs have many imprecisions leading to erroneous petrophysical
statistics. Depth shifts, interpretive scale, bed boundary overlap, log normalization,
interpretive ambiguities are sources of potential “errors” in training sets.
©David Garner

26. 2626
• Electrofacies Classification
− unsupervised approach – facies data is not provided
− supervised approach – facies data is provided
• Multivariate probability function described:
− Parametrically: linear and quadratic
− Non-parametrically : Bayesian and kernel
GR
RHOB
X
Electrofacies Modeling Approaches
p(x | Fj) Data
NFi
FpFxp
FpFxp
xFp NF
j
jj
ii
i ,...,1,
)()|(
)()|(
)|(
1





x
X
F1 F2
Nonparametric
Parametric
“Probability to assign an
observation x to the class Fi”
©David Garner

27. 27
Case 1: Carbonate Reef Example using Electrofacies
Fore-Reef
Lagoon
Reef and
Grainstone
7 described facies
Model facies trends impacted petrophysics, e.g. vertical permeability in high energy depositional areas
Kv
Fore-Reef
Reef and
GrainstoneLagoonField Study 1999-2000
Facies groups imply
different petrophysics.
Model zones and trends
related to deposition
©David Garner

28. 28
Case 2: Heidrun Field (The Magical Goat)
• Norwegian Sea Dataset
• 4 Stratigraphic units – Fangst
Formation
• Focus on the lowest zone
17 wells
• 4 Facies
• Training, N=1729
• Trained, N=2732
Training Data Clean up:
• Cleaned Outliers from distributions
• Bi-modal depo-facies split and re-assigned
Garner, Srivastava, Yarus, 2015. Presented at EAGE Petroleum Geostatistics, Biarritz
©David Garner

29. 29
Cleaning the Training set
Heidrun Field
Initial – 2nd Facies
is bi-modal
Cleaned – 2nd facies -
lower quality mode moved
to 1st facies
Depositional Facies interpretations may combine contrasting lithologies in
facies associations to represent a depositional process or location.
17 wells, 4 facies
©David Garner

30. 3030
Classification Results
Heidrun Field
Training Trained Full Log Coverage
Facies N=1729 N=1729 N=2732
1 17.7% 15.6% 16.5%
2 26.6% 26.4% 26.3%
3 11.9% 9.5% 8.2%
4 43.8% 48.4% 49.0%
Direct Bayesian Approach (by Mohan Srivastava)
©David Garner

31. 3131
Electrofacies (Heidrun Field)
GR Rhob Pr{F1} Pr{F2} Pr{F3} Pr{F4}Raw TrainNeutron
Efacies
Pr{max}
CBA A
Garner, Srivastava, Yarus, 2015. Presented at EAGE Petroleum Geostatistics, Biarritz

32. 3232
Core Facies
Description
• Grouped Data exhibits
unreasonable overlap
• e.g. Diagenetic (black points)
2008
lumped
cleaned
FACIES GR PHIE RHOB NPSS
2 10-60 24-38 1900-2200 16-52
3 10-70 22-36 1950-2250 12-50
4 10-70 20-36 1950-2250 14-50
6 60-110 2-18 2100-2350 30-56
7 40-90 10-28 2100-2350 28-48
9 10-50 0-8 2500-2700 0-30
Case 3: Ells River
Presented at
Geoconvention, 2009

33. 3333
Electro-Facies Supervised Classification
& Assignment
2008
GR
Density
Bayesian Classifier with a Gaussian kernel and posterior weights to adjust output
facies proportions
Using
4 curves
Ells River
©David Garner

34. 3434
Electro-facies Results Example
The study compared results to core photos and image logs
2007 2008
Based on geological concepts the proportions were adjusted to ensure the succession captured the variability
of a lower shoreface (mixed clastics) to upper shoreface (sand dominated) transition.

35. 3535
Checking the Models
F2 F4 F6 F7 F9
Phie Rhob GR NPSS
Stringers9
Sandy Mud7
Mud6
Sand w/ mud drapes4
Fine Sand2
©David Garner

36. 3636
Comments
It was possible to influence the result by changing the following parameters
(detailed descriptions follow):
1) Wells used to guide the prediction
2) Log-curve sets used for the discrimination and classification
3) Facies data input, i.e. amount of cleaning, lumping
4) Assignment probability of each facies, i.e. posterior weights
5) Linear, quadratic or non-parametric calculation method of the discriminant
function Use non-parametric for supervised models
6) Choice of kernel operator, e.g. Nearest Neighbour, Epanechnikov, Gaussian
©David Garner

37. 37
Example Facies Model
Seq-3
Seq-2
McM
Wab
GOC
DevUnc
8
ElectroFacies Results
SandSand
InterbedInterbed SSSS
MudMud
Muddy IB SSMuddy IB SS
StringersStringers
Garner, 2009; Rubin, et al., 2009 (Gussow conference)
Ells River
©David Garner

38. 3838
Case 3 Conclusions (1/2)
A petrophysically consistent and robust Electro-facies model was developed
for populating 3D models
The number of core facies were reduced based on similarity in log response
Core Description was used to classify Electro-facies in wells using a non-
parametric discriminant analysis
To account for vertical trends, analysis was separated into layers for
assignments
The proportions of the input training samples were used as a guide for the
output proportions during the process
Geological Concepts were used to validate and further adjust proportions
©David Garner

39. 3939
Case 3 Conclusions (2/2)
Fluid effects on logs may cause erroneous classifications
The optimum number of curves is between three and six
 Using too many curves does not improve the discriminant function
Select curves that are reliable and thought to be representative of the
rock types
The input from the geologists was important for deciding on final
electro-facies predictions (Geological Concepts)
Feedback from team members after use of Efacies can be
beneficial for updating models
©David Garner

40. 4040
Example 4: Facies Visual and Electrofacies
Visual Interpretation Electrofacies
Point Bar
Deposit
Electrofacies are consistent with the log scale variations
These visual facies were interpreted coarsely
Corner Field
Presented at
WHOC2014
New Orleans
Garner, et al. 2014; WHOC14-139©David Garner

41. 41
Point Bar
example
Figure from Martinius,
et al., 2017
Marine and Petroleum
Geology, Vol. 82
Visually interpreted depo-facies inputs are not consistent with logs. Electrofacies results
provide log-scale resolution and consistency necessary for geomodeling processes©David Garner

42. 42
Blind Tests: Judgement considerations
• Concept facies are mainly defined by percentages of
mud visually, i.e vshale range. Some geometric
issues apply.
• Classification Errors tend to be a reclassification
to an adjacent quality facies
• Breccia is a textural designation with a broad range
of qualities. The mode is closest to middle quality
I.H.S. with tails in all facies.
• Electrofacies are similar to a lithofacies or petrofacies
Breccia
Sandstone
SIHS 1
SIHS 2
Mixed IHS
MIHS
Mudstone
©David Garner

43. 43
Step-wise Results Reporting
Curves
RhoB
GR
NPSS
DT
PEF
DTS
6 5 4 3
Not covered
PCA screening
Step-wise reporting:
• Rank variables
• Wilks’ Lambda
• Avg Canonical
Correlations
• Eigen values
Not covered
Discussion of log curves
Strengths/weaknesses
Normalization required
Vintages and quality
Advanced analysis:
Transition statistics
Shannon’s entropy©David Garner

44. 44
Validation well (excluded from training)
4
4
Input
Visual
Interpretation
6 5 4 3
Number of Curves NPSS
RHOB
Electrofacies
An excluded well used as a blind test for validation purposes is shown. Non-parametric models
are compared using 3-6 curves which show consistency and log scale variability©David Garner

45. 45
Blind Tests (New Wells)
Visual
Facies
Pr{max}Pr{max}
Efacies
Visual
Facies
Visual
Facies
Blind wells show low probability efacies assignments tend to be a reclassification to adjacent
quality facies, aiding consistency for heterogeneity modeling
©David Garner

46. 46
Electrofacies Impacts: Improved parameters
Permeability Models Water saturation trends
 Captures capillary effect Percolation effects
©David Garner

47. Summary
Motivation to Improve Facies – additional points
• Electrofacies: Generating reliable inputs to geomodeling results in
• Quality: Description misinterpretations, bed boundaries, depth errors are
corrected
• Coverage: Facies in uncored intervals and uncored wells.
• Consistency: Petrophysical consistency in electrofacies model fidelity
• Electrofacies application to analogue fields ensures a reliability with
asset comparisons
• Accelerates subsurface project cycle times
• Rapid assimilation of delineation results Fast-track Decision Analysis
Static Models represent heterogeneities: Define important ones
©David Garner

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Electrofacies Modeling