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

Zarepakzad N., Artun E., DURGUT İ.

International Journal of Oil, Gas and Coal Technology, vol.27, no.3, pp.227-246, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27 Issue: 3
  • Publication Date: 2021
  • Doi Number: 10.1504/ijogct.2021.115801
  • Journal Name: International Journal of Oil, Gas and Coal Technology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Compendex, INSPEC
  • Page Numbers: pp.227-246
  • Keywords: reservoir simulation, chemical enhanced oil recovery, polymer flooding, artificial neural networks, ANNs, data-driven modelling, screening model, SCREENING CRITERIA, RECOVERY, FIELD, VISCOSITY
  • Middle East Technical University Affiliated: Yes

Abstract

Copyright © 2021 Inderscience Enterprises Ltd.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.