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Is This Thing On? A Well State Model for the People

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Oil and gas companies have thousands of wells in production. These wells frequently require action or maintenance.

Is This Thing On? A Well State Model for the People

  1. 1. Is This Thing On? A Well State Model for the People Tristan Arbus Data Scientist, Devon Energy Corporation tristan.arbus@dvn.com
  2. 2. - Frank Chimero, designer “People ignore design that ignores people.”
  3. 3. 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?
  4. 4. What is your goal?
  5. 5. Field Lifecycle Timeline Where production fits in to the overall life of a well
  6. 6. Rod Pumps Just one of many different forms of artificial lift
  7. 7. Looking at the Data A quick look at production parameters and identifying well state by eye
  8. 8. Well State Are we on or are we off? On OnOff Off 0 100 200
  9. 9. Whiteboarding
  10. 10. Existing Data Architecture How we obtain and use production data from the field
  11. 11. 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
  12. 12. 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
  13. 13. PI System Explorer Data quality Statistics and calculations Output as new attributes
  14. 14. DATA
  15. 15. Final Architecture Decision trees can be easily understood and implemented
  16. 16. Pi to Seeq to Databricks
  17. 17. Modeling Workflow
  18. 18. 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
  19. 19. Synthetic Minority Over-sampling Technique (SMOTE) Uses support vectors found using an SVM algorithm to create new samples
  20. 20. 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
  21. 21. Grid Search Performed a brute force, manual grid search tracking the performance of each parameter combination.
  22. 22. Small, Understandable Decision Tree
  23. 23. Converting Decision Trees to Code
  24. 24. Bigger, Less Palatable Tree
  25. 25. Imported Back Into PI
  26. 26. Models in Production
  27. 27. At Scale
  28. 28. Models Can Outperform the Controller
  29. 29. 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
  30. 30. Thank you!
  31. 31. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

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