INTERNAL

Rethinking Earth and Reservoir Modeling:

Is the Path Forward Black, White, or Gray?

Jeffrey M. Yarus, PhD

Halliburton Technology Fellow

2EXTERNAL

Early Career Preparedness - Academia to industryTeaching and research engagement that delivers lasting benefits across the academic cycle

iEnergy® University Hub

For further information: Xinli.Jia@Halliburton.com

3EXTERNAL

UGP EVOLVE

INTERNS

University Grants Program

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

4EXTERNAL

Early Career Preparedness - Academia to industryTeaching and research engagement that delivers lasting benefits across the academic cycle

http://www.ienergy.community/UniversityHub iEnergyUniHub@halliburton.com

Meaningful academic engagement looks beyond financial contributions…

5EXTERNAL

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

6EXTERNAL

STEPS — Distinguished Lecture Series

Topics relate to the STEPS annual research theme

Each lecture series has

A minimum of 5 lectures

Diverse speakers from both academia and industry

Each held at a different location across the globe

Lectures open to industry, students and academics

Attend at the venue

Tune into the live broadcast

Catch up on the recording on iEnergy®

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

8EXTERNAL

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

9EXTERNAL

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.

10EXTERNAL

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

11EXTERNAL

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

12EXTERNAL

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”

13EXTERNAL

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)?

14EXTERNAL

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

15EXTERNAL

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

16EXTERNAL

Earth Modeling Pain Points - Missing Log Values

Can be done as black or white modeling – depends on the level of transparency needed

17EXTERNAL

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)

18EXTERNAL

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$

19EXTERNAL

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

20EXTERNAL

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

21EXTERNAL

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!

22EXTERNAL

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

23EXTERNAL

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

25EXTERNAL

Thank You