A comparative analysis and rapid performance prediction of polymer flooding process by coupling reservoir simulation with neural networks

Date

2021-01-01

Author

Zarepakzad, Negar
Artun, Emre
Durgut, İsmail

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

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Accelerated technological progresses offer massive amounts of data, prompting decision making for any asset to be more complicated and challenging than before. Data-driven modelling has gained popularity among petroleum engineering professionals by turning big data into valuable insights that introduces fast and reliable decision making. In this study, a viscosifying polymer flooding performance-forecasting tool is developed using an artificial neural network-based data-driven model. A wide variety of reservoir and operational scenarios are generated to inclusively cover possible conditions of the process. Each scenario goes through no injection, water-only flooding, polymer followed by waterflooding and polymer-only flooding schemes. Neural network models were trained with three representative performance indicators derived from simulator outputs; efficiency, water-cut and recovery factor. Practicality of the tool in assessing probabilistic and deterministic predictions is demonstrated with a real polymer-flooding case of Daqing Oil Field.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108869180&origin=inward
https://hdl.handle.net/11511/91368

Journal

International Journal of Oil, Gas and Coal Technology

DOI

https://doi.org/10.1504/ijogct.2021.115801

Collections

Department of Petroleum and Natural Gas Engineering, Article

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Citation Formats

N. Zarepakzad, E. Artun, and İ. Durgut, “A comparative analysis and rapid performance prediction of polymer flooding process by coupling reservoir simulation with neural networks,” International Journal of Oil, Gas and Coal Technology, pp. 227–246, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108869180&origin=inward.