A comparative analysis and rapid performance prediction of polymer flooding process by coupling reservoir simulation with neural networks
by Negar Zarepakzad; Emre Artun; Ismail Durgut
International Journal of Oil, Gas and Coal Technology (IJOGCT), Vol. 27, No. 3, 2021

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]

Online publication date: Wed, 23-Jun-2021

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