Predicting the performance of different enhanced recovery schemes is of high importance due to a need for making feasible and practical operational decisions in reservoir exploitation. Technically, a proper recovery strategy has to be selected in early stage of field development planning. EOR screening criteria are widely employed in order to provide engineers with an appropriate recovery technique corresponding to reservoir and fluid properties. A dependable first order screening evaluation algorithm enables critical decision making on potential enhanced oil recovery strategies with the limited reservoir information. We present and test a novel screening techniques through the means of machine learning and pattern recognition techniques, which are well-established in computer science literature, in order to discriminate between various EOR projects with a special focus on CO2flooding. A comprehensive study is conducted utilizing various classification rules to solve EOR screening problem. Also, the effects of various features which are globally shared within different reservoirs are investigated as well. Well known feature selection methods like Data whitening, feature ranking and dimensionality reduction have been integrated to the process to improve the performance of the classifiers. In order to perform feature selection, some important fluid and reservoir characteristics such as permeability, depth, API, temperature, oil saturation and viscosity are taken into account. 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 aforementioned reservoir and fluid properties. 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.

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