An approach of using machine learning classification for screening of enhanced oil recovery techniques

Screening of an appropriate enhanced oil recovery (EOR) technique is important for the maximum reservoir recovery and economics of an oil exploration and production project. The conventional approaches for selecting EOR are often time-consuming and may fail to produce satisfactory results for the complex reservoirs. This paper focuses on implementing of selected five supervised machine learning (ML) techniques for accurate screening of EOR methods for complex reservoirs. Two of them are never used before for EOR screening. A global database of 358 successful EOR projects is collected of which 176 are used in the present study to train Neural Network (NN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes, and Random Forest (RF) classifier and tested their efficiencies. The result shows that the RF classification technique predicts the most suitable EOR with an accuracy value of 0.91 which is the highest accuracy among all other techniques. The study also ranks the reservoir and fluid parameters depending on their influences on the EOR screening. Viscosity is found to be the most impacting factor in the selection of an EOR technique with a feature importance of 34.6%.

Automated summary

Screening of an appropriate Enhanced Oil Recovery (EOR) technique is crucial for maximizing oil reservoir recovery and economics. This study utilizes five supervised machine learning techniques to accurately screen EOR methods for complex reservoirs, including two techniques never used before. By analyzing a database of 358 successful EOR projects, the Random Forest classification technique demonstrates the highest accuracy value of 0.91. The study also ranks reservoir and fluid parameters based on their influences on EOR screening, with viscosity being the most impactful factor at 34.6% feature importance.