A Novel Automated and Probabilistic EOR Screening Method to Integrate Theoretical Screening Criteria and Real Field EOR Practices Using Machine Learning Algorithms

To make promising operational decisions for reservoir exploitation, oil and gas industry relies heavily on predicting the performance of various enhanced recovery processes. Decisions on recovery strategies should be taken in an early stage of field development planning. To select an appropriate recovery technique based on reservoir and fluid characteristics, EOR screening criteria are used as a reliable decision making approach. A dependable first order screening evaluation algorithm enables critical decision making on potential enhanced oil recovery strategies with the limited reservoir information.

In this study, we propose to solve the EOR screening problem by machine learning or pattern recognition methods, which are well-established in computer science literature. We perform a comprehensive study on the application of various machine learning methods such as Bayesian Classifier, K-nearest Neighbor Classifier, Minimum Mean Distance Classifier, and Artificial Neural Networks.

The proposed data-driven screening algorithm is a high-performance tool to select an appropriate EOR method such as steam injection, combustion, miscible injection of CO₂ and N₂, based on different reservoir and fluid properties such as permeability, depth, API, and viscosity. In this innovative approach, we integrate both theoretical screening principles such as Taber criteria and successful field EOR practices worldwide. Not only this algorithm proposes an appropriate EOR method for a specific reservoir condition but it also gives the probability of success or success rate corresponding to each EOR method. In addition, the proposed algorithm is able to address environmental, economical, geographical and technological limitations.

The proposed algorithm permits integration of different types of data, eliminates arbitrary approach in making decisions, and provides accuracy and fast computation. The suitability of the proposed method is demonstrated by different synthetic and real field EOR cases. This novel EOR screening method is capable of evaluating the effectiveness of different EOR scenarios given a specific reservoir condition. We showed that the proposed EOR screening algorithm is able to predict the appropriate EOR method correctly in more than 90% of cases. We also ranked the proposed screening algorithms based on their screening performance.