Using an Ensembled Artificial Intelligence Approach for EOR Methods Screening in Oil Fields

Decision-making to choose the best Enhanced Oil Recovery (EOR) method(s) among different variety of models, is a vital step in the oil reservoir development process. Selecting the proper EOR method has a key role in the technical and economic success of enormous oil industry projects. Screening criteria are used for selecting the best EOR method(s) for an oil reservoir with specific rock and fluid properties. The main input parameters that affect the screening process include; Reservoir capacity, fluid transmissibility and permeability, depth, net thickness, temperature, and oil gravity (API). Considering mentioned parameters have uncertainty, specifying a suitable EOR method for reservoir development is a radical challenge. It can be used combination of fuzzy logic systems (knowledge base) and artificial neural networks (data-driven) as a suitable tool and solution for expressing uncertainty and screening methods. In this study, data from the history of different reservoirs worldwide is used to define fuzzy variables and determine fuzzy rules between input and target variables, and finally, a fuzzy model and a single-layer neural network with 20 neurons are presented. ANN model provides 92% accuracy in the prediction of the target method. Consequently, we proposed the ensemble model for the selection of the EOR screening tool.