Application of machine learning techniques for selecting the most suitable enhanced oil recovery method; challenges and opportunities

Abstract

A reliable method for selecting the most suitable Enhanced Oil Recovery (EOR) method is of a high necessity in reservoir engineering. Machine Learning (ML) techniques have been widely adopted as an effective and fast approach in primary stage of EOR screening. This paper presents a step-by-step procedure of applying ML in EOR screening with focusing on two main goals. Firstly, the problem of EOR screening is investigated from ML point of view. A large database consisting of more than 1000 experiences of worldwide EOR projects has been gathered from various sources. On the basis of this database, several ML models have been developed for predicting the suitable category of EOR methods for a candidate reservoir. These models include: Shallow and Deep Artificial Neural Networks (ANN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Dimension Reduction using Principal Component Analysis (PCA). Almost all models performed well in predicting non-observed data with an average accuracy ranging from 80 to 90%. However, RF and the deep ANN models delivered the best performance with an average accuracy of 90%. The results demonstrated that RF model performed much better in comparison with the DT model and improved the classification accuracy from 85 to 91%. In case of ANNs, it was observed that the deeper ANN developed here, improved the classification accuracy from 83 to 90% in comparison with the shallow ANN with one hidden layer. The weakest performance was delivered by NB classifier with an average accuracy of 49%. As the second purpose of this research, reliability of screening results from applying ML techniques is discussed in details. Because, this approach suffers from some serious limitations and challenges, which could unfavorably affect the screening results and never had been considered in previous works. In this work, potential challenges and limitations associated with this well-known approach are introduced from database issues to modeling problems. Accordingly, an important conclusion is that application of ML, although providing a very good insight into primary EOR screening, should not be relied upon as the only method for predictions. It is recommended that the obtained results from this approach should be cross-checked with other EOR screening approaches or with methods based on expert's knowledge.