Qin Qian¹, Mingjing Lu^1,2,*, 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 >

Jingyi Wang¹, Jing Sun^1,*, Dehua Liu¹, Xiang Zhu¹

FDMP-Fluid Dynamics & Materials Processing, Vol.15, No.5, pp. 613-625, 2019, DOI:10.32604/fdmp.2019.08782

Abstract One of the important indicators of shale gas reservoir excavation is capacity evaluation, which directly affects whether large-scale shale gas reservoirs can be excavated. Capacity evaluation is the basis of system analysis and dynamic prediction. Therefore, it is particularly important to conduct capacity evaluation studies on shale gas horizontal wells. In order to accurately evaluate the horizontal well productivity of shale gas staged fracturing, this paper uses a new method to evaluate the productivity of Fuling shale gas. The new method is aimed at the dynamic difference of horizontal wells and effectively analyzes the massive More >