The detailed descriptions provided in this review serve as a comprehensive reference of AI optimization techniques for further studies and research in this area.Īdaptive neuro-fuzzy inference system ANN:īack propagation artificial neural networks CCDE:Ĭooperative coevolutionary differential evolution CMA-ES:Ĭovariance matrix adaptation evolution strategy CR: Furthermore, hybridization and/or combination of various AI techniques can be successfully applied to solve important optimization problems and obtain better solutions.
The review highlights the exceptional performance of AI methods in optimization of various objective functions essential for industrial decision making including minimum miscibility pressure, oil production rate, and volume of \(\hbox \) sequestration. Additionally, we examine these types of algorithms with respect to their applications in petroleum engineering. For this purpose, we classify AI methods into four main categories including evolutionary algorithms, swarm intelligence, fuzzy logic, and artificial neural networks. This survey offers a detailed literature review based on different types of AI algorithms, their application areas in the petroleum industry, publication year, and geographical regions of their development. Interactive, intuitive and easy-to-use interface enables quick and efficient design and preparation of simulation models for all CMG simulators.Ĭreate tuned fluid property descriptions for CMG simulators and black oil fluid property data for third-party reservoir simulation software.In recent years, artificial intelligence (AI) has been widely applied to optimization problems in the petroleum exploration and production industry.
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