How machine learning forecasts can both confirm and complement traditional Rate Transient Analysis (RTA) methods in unconventional fields. www.novilabs.com

1. SPE Workshop
New Perspectives in Well Performance Analysis
and Production Forecasting
SPE Workshop | 4-6 Apr 2022
Galveston, Texas, USA

2. 1
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
USING MACHINE LEARNING FOR WELL PERFORMANCE ANALYSIS & FORECASTING
Scott McEntyre, Managing Director, Novi Labs
“All models are wrong, but some are useful”
- George Box, Statistician
Today will discuss the following:
1. Ensemble Tree Machine Learning Methods
2. Examples of ML Insight in Oil & Gas
3. How ML Confirms & Complements RTA/PTA

3. 2
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
BASIC MACHINE LEARNING (ML) ALGORITHMS: DECISION TREES ARE A POWER TOOL
y=m1
x1
+m2
x2
+b
Multivariate Linear Regression Decision Tree-Based Neural Networks
● Often what people mean when they
say “MVA”
● Easily interpretable via slopes
● Only captures linear relationships
● Cannot capture multiple
dependence relationships
○ Wells with tight spacing AND
large completion designs ...
● Algorithm splits/groups wells by
input variables to predict output
variables
● Handles variable interaction well
● Strong explain-ability
● Similar to analog type curve or
analog field approaches
● Robust to messy data
● Strong with smaller datasets
● Powerful! Much of buzzy public
AI uses neural nets
● Handles variable interaction and
complex relationships well
● Requires large datasets
● Difficult to interpret/explain
● Sensitive to input data quality
https://en.wikipedia.org/wiki/Neural_network#/

4. 3
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
USING TREE BASED ML FOR INDEPENDENT, EMPIRICAL ANALYSIS: HAYNESVILLE
1000’ Spacing - 102 Undrilled Wells
750’ Spacing - 131 Undrilled Wells
Inventory Configuration &
Completion Designs
2500 lb/ft - 2500 gal/ft
3500 lb/ft - 3500 gal/ft
4000 lb/ft - 4000 gal/ft
EUR/ft
NPV/Scenario
Production Drivers
ML Model Outputs for Scenario and Well-level Optimization:

5. 4
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
ML EXAMPLE OUTPUTS: PRODUCTION DRIVERS IN DELAWARE BASIN
For every forecasted well for each stream and at every IP day interval, a ML
model can show which variables drove the forecast, and to what degree.

6. 5
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
USING MACHINE LEARNING TO UNDERSTAND WELL PERFORMANCE: BAKKEN BASIN
Performance Drivers change with time
to explain impact of features on well
performance.
Intuitive presentation of complex
interactions between co-dependent
features.
Enables matching of development
strategy to specific acreage and
economic objectives.
Potential to combine Performance
Drivers for comparison with some
RTA/PTA outputs.
Example of Production Drivers in Bakken Basin

7. 6
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
ML AUTOREGRESSIVE MODELS TO AUTOMATE PDP FORECASTS
Before a well comes online,
ML models use geology,
completions, and spacing to
make a prediction
After a well starts flowing,
ML models can utilize prior
production to improve the
post-drill predictions
Farther into the production
profile, the uncertainty has
decreased even further
Forecast
QA Criteria
Manual
Forecast by
Exception
Catalog of
PDP Forecasts
Example Workflow for PDP
Forecast Automation:
Base Production
Well
Surveillance
Reserves, NAV

8. 7
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
COMPARISON OF ML STRENGTHS AND LIMITATIONS TO RTA/PTA
RTA/PTA Analysis:
+ Most direct technical measurement of
well performance
+ Continuous surveillance over well life
- Capital deployed before analysis
- Requires accurate PVT inputs,
production data, and pressure
- RE time required for each well
Reservoir & Fracture Numerical Simulation:
+ Familiar physics-based methods
+ Predicts geo-mechanical and reservoir drive mechanisms
- Requires accurate input data
- Outputs can be difficult to directly validate.
Machine Learning:
+ Excellent predictions before drilling
+ Explainable Multi-variate Analysis
+ Empirical and naive to physics
+ Fast and inexpensive to run multiple
iterations/scenarios
- Limited within feature space
- Features of interest needed in training,
test, and forecast data sets
- Can be influenced by bias in dataset.

9. 8
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
HOW ML COMPLEMENTS AND ENHANCES RTA/PTA
ML as an Independent Prediction Combining RTA/PTA and ML
● ML methods empirically “honor the data”
● Degree of ML and RTA output agreement
or disagrees indicates confidence in
expected results
● ML methods offer fast iteration and
scale-up field-development level
● RTA/PTA data outputs can be highly
informative as features in ML methods
(A√K, SRV, Kh, OOIP)
● ML Production Forecasts using RTA/PTA
features combine all available data in
familiar investment decision formats
● Using ML methods for bulk of forecasts
and RTA/PTA methods to “forecast by
exception” maximizes valuable RE
resources

10. 10
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
NOVI
DATA
ENGINE
NOVI MODEL
ENGINE
NOVI FORECAST
ENGINE
DECISION
ACQUIRE
OPTIMIZE
INVEST
DATA
NOVI PUBLIC NOVI CLOUD PLATFORM
NOVI DATA
NETWORK
CUSTOMER DATA
https://novilabs.com/

11. 11
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
MAKING MACHINE LEARNING OUTPUT INTUITIVE :: ANALOG WELLS
Location of planned well
Each blue square is a well in
the ML model thought was
analogous to the selected
planned well. Size of square
equals level of similarity.
For every forecasted well for each stream, a ML model shows exactly which
analog wells drove the forecast, and to what degree.

12. 12
4–6 April 2022
Galveston, Texas, USA
SPE Workshop: New Perspectives in Well Performance
Analysis and Production Forecasting
OVERVIEW OF MACHINE LEARNING APPLICATION TO OIL AND GAS
High
Prop
All
Close
spac.
Far
spac.
High
ppg
Low
ppg
PSI
>.65
All
High
Sw
Low
Sw
High
ppg
Low
ppg
Fm 1 Fm 2
All
High
ppg
Low
ppg
High
ppg
Low
ppg
Target
Variable
Feature A Feature
B Feature A Feature B
Data Feature Space Algorithm Slices Data
to Minimize Error
Create Decision Trees for
Combinations of Features
● Training and Test data sets
established
● Target variable is to be
predicted (usually production)
● Features can include:
○ Completion size
○ Geology
○ Well Spacing
○ Engineered Variables
○ RTA/PTA output data
● Iterations find values in features
that lead to local means with
minimal error to data
● Purely empirical relationships
identified between features and
Target
● Algorithm is naive to any
common physics-based
relationships
Target
Variable
● Algorithm finds which trees are
most useful to predict target in
variable by minimizing error
● Decision Trees can identify
multiple dependence
relationships
● Implicitly identifies importance
of features
● This approach can “show it’s
work” to explain output.

13. You can download the complete presentation
from our website
Novilabs.com/resource-library/