Optimization of Production and Injection of Geothermal Fields: A Machine Learning Approach

Date

2021-10-01

Author

başer, ali
KÜÇÜK, SERHAT
saraçoğlu, önder
şentürk, erdinç
Akın, Serhat

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Optimizing field injection and production requires a calibrated numerical model, typically consisting of thousands of grid blocks. Optimization carried out using such a model usually takes a very long time. If the numerical model is replaced by an accurate proxy model, run-time can be significantly reduced. A numerical model has been developed using TOUGH2 to optimize the production of Kızıldere geothermal field. A proxy model developed in Python is used to optimize the production and injection of the field. The results are compared with the TOUGH2 model. The proxy model results are consistent with the field model. This approach significantly reduces time and effort.

Subject Keywords

Optimization, Tough2, Proxy, Machine Learning, Kızıldere

URI

https://pangea.stanford.edu/ERE/db/WGC/mobile/mSchedule.php
https://hdl.handle.net/11511/95985
https://pangea.stanford.edu/ERE/db/WGC/papers/WGC/2020/24034.pdf

Conference Name

World Geothermal Congress 2020+1

Collections

Department of Petroleum and Natural Gas Engineering, Conference / Seminar

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Citation Formats

a. başer, S. KÜÇÜK, ö. saraçoğlu, e. şentürk, and S. Akın, “Optimization of Production and Injection of Geothermal Fields: A Machine Learning Approach,” presented at the World Geothermal Congress 2020+1, Reykjavik, İzlanda, 2021, Accessed: 00, 2022. [Online]. Available: https://pangea.stanford.edu/ERE/db/WGC/mobile/mSchedule.php.