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Is This Thing On? A Well State Model for the People
Is This Thing On?
A Well State Model for the People
Tristan Arbus
Data Scientist, Devon Energy Corporation
tristan.arbus@dvn.com
- Frank Chimero, designer
“People ignore design
that ignores people.”
Agenda
Production
What is production, where does it fit into the
lifecycle of a well, and why do we care about it?
Well State
What are we trying to solve and how can we do so
while promoting adoption?
Pros, Cons, and the Future
What’s good? What’s not? How can we make
improvements?
What is your goal?
Field Lifecycle Timeline
Where production fits in to the overall life of a well
Rod Pumps
Just one of many
different forms of
artificial lift
Looking at the Data
A quick look at production parameters and identifying well state by eye
Well State
Are we on or are we off?
On OnOff Off
0
100
200
Whiteboarding
Existing Data Architecture
How we obtain and use production data from the field
Supervised Learning
The rod pump run status controller provides a supervised learning dataset not readily available with
other forms of production
A proof of concept with rod pump production would motivate the manual creation of a supervised
learning dataset for other lift types
Data Quality Issues
The data isn’t always as nice as we want it to be
Problem
▪ “Ratty” data
▪ Missing data
▪ Variable data frequency
▪ Issues exist on a parameter-by-
parameter and well-by-well level
Solution
▪ Smoothing
▪ Data quality filters
▪ Interpolation
▪ One model for each parameter
PI System Explorer
Data quality
Statistics and
calculations
Output as new attributes
DATA
Final Architecture
Decision trees can be easily understood and implemented
Pi to Seeq to Databricks
Modeling Workflow
Manual Train Test Split
Allows engineers to select a group of wells that represent the entire field.
“If the model is accurate on this set of wells, it will be accurate everywhere.”
Wells
chosen by
engineers
Synthetic Minority Over-sampling Technique
(SMOTE)
Uses support vectors found using an SVM algorithm to create new samples
Hyperparameter Tuning
While keeping all but one parameter constant, you can both determine good starting points for a grid
search and prevent potential overfitting
Differential Pressure Total Gas Rate
Grid Search
Performed a brute force, manual grid
search tracking the performance of each
parameter combination.
Small, Understandable Decision Tree
Converting Decision Trees to Code
Bigger, Less Palatable Tree
Imported Back Into PI
Models in Production
At Scale
Models Can Outperform the Controller
Room to Get Better
Data Quality
▪ Training would benefit from improvements in data collection
Feature Engineering
▪ While basic statistics were used, perhaps features could be
engineered to be more descriptive
Model Type
▪ Gradient boosting and other model types can outperform
decision trees, but would require additional architecture and
buy-in
Model Longevity, Maintenance, and Tracking
▪ Don’t worry, I’m well aware that deploying a decision tree as
if/then statements won’t be the best long term option
Thank you!
Feedback
Your feedback is important to us.
Don’t forget to rate and
review the sessions.

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