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, vol.27, no.3, pp.227-246, 2021 (SCI-Expanded, Scopus)
- 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.