A. Saeedi * , K.V. Camarda, J.T. Liang Chemical & Petroleum Engineering Department

1. SPE 101028 Using Neural Networks for Candidate Selection and Well Performance Prediction in Water-Shutoff Treatments Using Polymer Gels A. Saeedi*, K.V. Camarda, J.T. Liang Chemical & Petroleum Engineering Department * Now with Chevron Corp.

2. Water-shutoff Treatments • World wide, > 75 billion barrels/yr of water produced from oil and gas fields. • Averaging > 3 barrels of water for each barrel of oil produced. • Estimated disposal costs ~ 40 billion US dollars/yr. Financial & Environmental Incentives

3. Polymer Gels for Water-shutoff Treatments • Used extensively in field applications to suppress excess water production. • Candidate selection critical to the success of gel treatments. • Anecdotal guidelines for candidate-well selection resulting in inconsistent outcomes.

4. Objective of Study • Develop a methodology using neural networks to identify candidate wells based on predicted outcomes for polymer gel treatments. • 22 wells treated with polymer gels in Arbuckle formation in Kansas were used to develop the neural networks.

5. A. Arbuckle Structure Map B. Arbuckle Cum. Oil Production by County (MMBO) Franseen, 2003

6. Franseen, 2003

7. Arbuckle Formation in Kansas • Main oil producer in Kansas (~2.2 billion barrels). • Fractured-controlled karstic reservoirs with porosity and permeability influenced by basement structural patterns and subaerial exposures. • Strong bottom aquifer with high WOR. • Open-hole completion at top 1/3 of pay zone to avoid water coning. • Reservoir poorly characterized.

8. Gel Treatments in Arbuckle • Cr(III)-HPAM gels very successful in treating high water-cut well. • >250 wells treated with Cr(III)-HPAM gels. • Candidate selection based mainly on vendor’s past experience (reservoir poorly characterized).

9. Neural Networks for Candidate Selection • Candidate selection based on predicted treatment outcomes using pre-treatment data is a pattern-recognition problem. • Neural network is a powerful tool for solving pattern recognition problems.

10. Artificial Neuron After Mohaghegh (2000)

11. Input Layer Hidden Layer Output Layer Weight Weight Three-layer Neural Network

12. Neural Network Development • Data divided into two sets. • Training set used to adjust connecting weights. • Verification set used to evaluate the trained network. • Verification set not seen by network during training.

13. Supervised vs. Unsupervised Neural Networks Unsupervised Neural Networks • Training set consists of input patterns only. • No feedback is provided. Supervised Neural Networks • Training data consist of many pairs of input an output patterns. • Feedbacks are provided during training.

14. Feedforward-Backpropagation Neural Networks (Supervised Training) Backpropagation Input #1 Weight Input #2 Weight Output #1 Input #3 Output #2 Input #4 Input #5 Feedforward Input #6 • An iterative process to minimize error.

15. Input Output Neural Network Feedforward Backpropagation Adjusted Weights Training Step

16. Example Training Set

17. Input Trained Neural Network Actual Output Predicted Output Accuracy Evaluated Verification Step

18. Example Verification Set * Cumulative oil production one month after treatment **Error Fraction = |(Actual Value – Predicted Value)|/Actual Value

19. Input Trained Neural Network Outcome Prediction Predictive Step

20. Example Outcome prediction Neural Network

21. Neural Network 1* * 18 Training Sample Wells + 4 Verification Sample wells

22. Neural Network 1

23. Neural Network 1 *EF = |(Actual Value – Predicted Value)|/Actual Value **AEF: Average EF

24. Neural Network 2* * 16 Training Sample Wells + 4 Verification Sample wells

25. Neural Network 2

26. Neural Network 2

27. Conclusions • With only pre-treatment data as input parameters, the neural networks in this study can accurately predict cumulative oil production one and three months after gel treatment. • Neural networks allow the candidate selection to be based on the accurate predictions of treatment outcomes using only pre-treatment data. • This method is far superior to the anecdotal guidelines based solely on vendor experience.

28. Thank you!