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

Authors: Negar Zarepakzad; Emre Artun; Ismail Durgut

Addresses: Middle East Technical University, Northern Cyprus Campus, RZ-32, Kalkanlı-Guzelyurt, TRNC, Mersin 10, 99738, Turkey ' Middle East Technical University, Northern Cyprus Campus, T-138, Kalkanlı-Guzelyurt, TRNC, Mersin 10, 99738, Turkey ' Middle East Technical University, Ankara Campus, R-13, Cankaya, Ankara, 06800, Turkey

Abstract: 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. [Received: April 15, 2020; Accepted: June 26, 2020]

Keywords: reservoir simulation; chemical enhanced oil recovery; polymer flooding; artificial neural networks; ANNs; data-driven modelling; screening model.

DOI: 10.1504/IJOGCT.2020.10034580

International Journal of Oil, Gas and Coal Technology, 2021 Vol.27 No.3, pp.227 - 246

Received: 15 Apr 2020
Accepted: 26 Jun 2020

Published online: 14 Jun 2021 *

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