Selection and Evaluation of Enhanced Oil Recovery Method Using Artificial Neural Network

Abstract

ABSTRACT Enhanced Oil Recovery (EOR) has gained great attention as a result of higher oil prices and increasing oil demands. Extensive researches have been conducted to develop various EOR methods, evaluate their applicability and optimize operation conditions. One of the principal areas is to develop an effective tool for selection of a suitable EOR method according to oil field characteristics. The main objective of the studies is to screen various EOR methods based on field characteristics and evaluate their technical/economic applicability in an efficient way instead of predicting the field performances of all possible competing strategies and comparing their economics. In this paper, we present an Artificial Neural Network (ANN) approach to enable the petroleum engineer to select an appropriate EOR method with the given reservoir properties. The ANN developed in this study is a four-layered feed-forward Back Propagation (BP) network consisting of one input and output layer with two hidden layers. The input layer is composed of the key reservoir parameters (reservoir depth, temperature, porosity, permeability, initial oil saturation, oil gravity, and in-situ oil viscosity) while the output layer is composed of the five EOR methods to be evaluated (steam, CO2 miscible, hydrocarbon miscible, in-situ combustion, polymer flooding). The number of hidden layers and neurons are optimized during the training by repeated trial and error. After trained successfully, the ANN is tested and applied to other fields which are not used for the training. The noise test is also conducted to evaluate applicability of the model against the error included in the input. A series of the test results show that the ANN developed in this study can be used to select the most appropriate EOR process according to reservoir rock and fluid characteristics in a time and cost effective way.