Qin Qian1, Mingjing Lu1,2,*, Anhai Zhong1, Feng Yang1, Wenjun He1, Min Li1
Energy Engineering, Vol.121, No.8, pp. 2167-2190, 2024, DOI:10.32604/ee.2024.049430
Abstract The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics, engineering quality, and well conditions. These relationships, nonlinear in nature, pose challenges for accurate description through physical models. While field data provides insights into real-world effects, its limited volume and quality restrict its utility. Complementing this, numerical simulation models offer effective support. To harness the strengths of both data-driven and model-driven approaches, this study established a shale oil production capacity prediction model based on a machine learning combination model. Leveraging fracturing development data from 236 wells… More >
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