Abstract
The process of designing and selecting practical Enhanced Oil Recovery (EOR) methods have become increasingly important in the planning of field development as significant quantities of readily accessible oil have been extracted from reservoirs. Polymer flooding is one of the classical Chemical EOR (CEOR) techniques with many large-scale projects ongoing or in the early stages of implementation in various countries worldwide. Although Chemical EOR techniques have been in practice since several decades now, the issue of accurate estimation of incremental recovery under these schemes due to uncertainties associated with the unknown or assumed data still persists. This paper describes a novel data driven workflow using Machine Learning to predict the incremental oil recovery for a target reservoir under Chemical EOR schemes. This paper aims to study the effect and advantages of polymer flooding in improving ultimate hydrocarbon recovery by using Computerized Tomography (CT) scan images of flooded core samples.
Published Version
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