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Rethinking Earth and Reservoir Modeling: Is the Path ... · The Earth Modeling Workflow moving...
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Rethinking Earth and Reservoir Modeling:
Is the Path Forward Black, White, or Gray?
Jeffrey M. Yarus, PhD
Halliburton Technology Fellow
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Early Career Preparedness - Academia to industryTeaching and research engagement that delivers lasting benefits across the academic cycle
iEnergy® University Hub
For further information: [email protected]
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INTERNS
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Flagship Programs
Virtual, asset-based learning
on the iEnergy cloudThematic research and lecture
series
Business critical projects,
mentored by experts in mixed
domain teams
Three-year renewable
software licenses
iEnergy® cloud
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Early Career Preparedness - Academia to industryTeaching and research engagement that delivers lasting benefits across the academic cycle
http://www.ienergy.community/UniversityHub [email protected]
Meaningful academic engagement looks beyond financial contributions…
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STEPS — Structure
Different research theme each year:
2016/17: Source-to-Sink
2017/18: Big Data in Exploration & Production
2018/19: Near Field Exploration & Production
Students are provided with
A project scope and guidance
At least 1 industry mentor
Real-world data
Access to industry-leading software
Mentorship
Training
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STEPS — Distinguished Lecture Series
Topics relate to the STEPS annual research theme
Each lecture series has
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Diverse speakers from both academia and industry
Each held at a different location across the globe
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Attend at the venue
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2nd Distinguished Lecture: Imperial College, 10/17/2019, Prof. Martin
Blunt. Details soon.
INTERNAL
Rethinking Earth and Reservoir Modeling:
Is the Path Forward Black, White, or Gray?
Jeffrey M. Yarus, PhD
Halliburton Technology Fellow
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Earth Modeling Today
Today, subsurface reservoir characterization or Earth Modeling is the construction
of a Digital Twin representing a reservoir or a stack of reservoirs used in the
process of economic assessment of mineral resources
Earth models are constructed such that they honor:
Input data
Structure
Stratigraphy
Physics / Chemistry
Spatial relationships
Today, there is a drive integrate earth modeling methodologies into High
Performance Computing environments and improve the technology through use
of data science and automation. z
Wolfcamp
Spraberry
CBP
Midland Basin
Key Attributes of Spatial
Modeling
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Problem Statement and Conclusions
#1 Why consider HPC, Data Science,
and Automation for Earth Modeling (EM)
or its geostatistical engines (Spatial
Modeling Kriging, simulation),?
#2 Are there any HPC, Data Science, or
Automation methods that could enhance
the geostatistical engines within EM?
#3 What parts proximal to Earth
Modeling are potentially good candidates
for HPC, ML, and Automation?
#1: Most aspects of EM can benefit
from HPC and Automation, but there is
very little benefit to replacing the
geostatistical engine with ML
#2: Spatial model fitting (variograms)
can benefit from ML and automation:
#3: Both Pre (data QC and analysis) and
post processing (preparation for
downstream assessment) can benefit
from spatial analytics and ML.
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Definitions
These definitions are broad and not meant to become complicated so…
Black Box Models: Highly non-linear by nature and are harder to explain in general. With black-box models, users can only observe the input-output relationship
White Box Models: A white model are the type of models which one can clearly explain how they behave, how they produce predictions and they identify the influencing variables.
Physical Models: Physical models are generally representations of the object being studied. They constructed using methods like finite difference, finite element, and finite volume. They are based on physical, chemical, and mechanical principles. They can be classified as White Box Models
Gray Box Models: Gray models are models that integrate all or portions of the above
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Earth Modeling, Machine Learning, and Automation
Current Earth Modeling practices are quantitatively sophisticated
Rooted in mathematical and statistical theory
Provide methods of data integration
Hard” and “soft” data
Long track record of success
Automation, ML, and HPC can address EM pain points in:
Pre-processing: ML on data Input
Automated Modeling: Assisted automation: Data QC Spatial Model Fitting Kriging Simulation
Post Processing: Assessment of multiple realizations for simulation, drilling, completion
Log calibration / correlation
Multiple realizations and scenarios
Trend capture, spatial modeling
Multiple realizations Simulator
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Earth Modeling Pain Points
Pre-Processing
Hard work happens prior to
modeling
»Petrophysics
Log interpretation
»Geophysics
Fault interpretation
»Structural Interpretation
»Stratigraphic Interpretation
Facies Definitions
Sh
ale
Sandst
one
Sh
SST
Limest
one
Dolomit
e
Marl
Car Sh
Badhol
e
Anhydri
te
Assisted machine learning ensemblemethod: gradient boosted trees andconvolution neural networks
Assisted Machine Learning fault
interpretation; “Fault Likelihood”
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Earth Modeling Pain Points
With the Permian
Training Dataset
With the
Australian
Training Dataset
Combined
~90%~80%
Permian
Well
H
Petrophysics
Assisted ML log interpretation
Inter and Intra-basin relationships(Black and/or White Modeling)
• Black or White? How critical is
the facies interpretation?
• Are they the primary
predictor (e.g. depositional
system)?
• Are they the secondary
predictor (e.g. to classify
petrophysical properties)?
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Earth Modeling Pain Points
Pre-Processing
Missing Log Values (Black/White Modeling)
Potential Issues:
»Listwise: Entire well dropped from analysis if missing values in any logs
»Pairwise: Only logs with missing values is dropped form analysis
“Null flag” value or moving average
»Used to prevent Listwise/Pairwise problems
»Not always viable
»May bias results Permian Basin Example
Wolfcamp A, B, C
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Earth Modeling Pain Points
Pre-Processing
Missing Log Values (black modeling)
Example:
»5 Patterns
Data present
Stacking of tools * Below Target Formation
Unavailable Log
Outliers
»Other Possible Patterns
Washout zones
Cased Holes
Tool malfunctions
Human/mechanical error
More…
2
3
4
5
1
Permian Basin Example
Wolfcamp A, B, C
Concept:
Impute or predict
missing values from
nearby wells where
data are present in
specified pattern
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Earth Modeling Pain Points - Missing Log Values
Can be done as black or white modeling – depends on the level of transparency needed
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Earth Modeling Pain Points – Automation
Modeling with CIP (Continuous Interface Piping)
Computational Speed
»Parallization and HPC
»Variogram Fitting (LMKR)
»Redundant processes
Assisted Automation
Semi-automation approach built in R:
An assisted process (or semi-automation) allows experts to intervene as necessary
• Simulation Method
• # of Realizations
• Isoprobability Maps
• CPDF Criteria
Kriging Interpolation
Outlier rules
High-Performance Computing
White Modeling
White Modeling
White/Black Modeling (e.g. classification of realizations)
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Earth Modeling Pain-Points
Modeling
Updating models
Very large models
Rebuilding models at different scales
Uncertainty – incr # of realizations
Model calibration from scale to scale
Tensor Cloud (White Box Modeling)
Grid-free, spatial statistics
HPC native
Single seamless scalable modeling
Provides downstream output
100+ realizations no added CPU$
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Harvesting Data Richness at Larger Scales
Integration of data and physics-driven modeling
Premise: recalibrate coarse model to a smaller scales to enable seamless scaling
Series of recalibration exercises at each scale
»On-the-fly
Scale dependent resolution and detail
»Limited to original resolution of the measurements
Basin Model
Block ModelField
Model
Appraisal/
Development
Model
Well
Model
Scale calibration and consistency
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Sorting out Realizations
Using Unsupervised Cluster Analysis (Black Modeling)
Post-Processing
Uncertainty assessment»Realizations
P10, P50, P90
»Each realization represented as
an array of cells from 1 to n
»Realizations = Variables
»Cells = Samples =
RealizationsOrganize realizations
into arrays
Two-way cluster analysis; variable
grouping and samples grouping
Graphical interactions to
represent grouped and associated
geological bodies of realizations
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Sorting out Realizations Using Unsupervised Cluster Analysis
Two-way cluster analysis; variable
grouping and samples grouping
Single Realization; all clusters
combined
Graphical interactions to
represent grouped and
associated geological
bodies of realizations
Decomposed realization for
each cluster. Each cluster
shares unique identical ijk cells,
regardless of the realization
KEY POINT: The cells from any realization are
the same in a given cluster!
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Analytics on Geobodies from RealizationsRealization 10
1
23
4
Realization 16 Realization 22
Geobody 1 Geobody 1
Realization 15Realization 13
Geobody 1 Geobody 1
Realization 10 Realization 15
Constellation Chart
Filtering, no Cluster AnalysisConnectedness: # of cells
that share at least one wall at
a given threshold
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Conclusions
The Earth Modeling Workflow moving forward will be Black, White, and Gray!
• #1: Geostatistical Modeling does not need to be replaced by ML
• White and Gray!
• #2: Fitting of spatial models can benefit from ML and automation
• Black and/or White
• #3: Pre and post-processing can benefit from spatial analytics and ML
• Black and/or White
“The purpose of scientific computing should be insight, not numbers”Richard Hamming, 1962
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Thank You