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• W4220654329 abstract "Hydraulic fracturing technique increases shale gas well productivity significantly. Horizontal well fracture optimization has been studied by many researchers worldwide in the past decade. However, most of these researches relied on computationally expensive numerical simulation models for objectives evaluation during the optimization process. This affects the optimization efficiency significantly. To address this issue, surrogate model methods which adapt a simple approximate model are employed to lessen the computational burden. In this study therefore, a novel intelligent variable-fidelity radial basis function (VFRBF) surrogate-assisted model for multi-objective fracture stage placement optimization, namely VFRBF-FSO, is proposed to reduce the computational burden of the numerical simulation-based production optimization. In the VFBRF-FSO method, low-fidelity (LF) and high-fidelity (HF) samples were adopted simultaneously to establish the variable-fidelity (VF) surrogate model. To the best of our knowledge, this is the first time that variable-fidelity model is used for shale gas horizontal well fracture stage placement optimization. The uniqueness of this proposed method is that a scaling factor and an augment matrix are used to integrate the LF and HF samples to increase the accuracy of the surrogate model. Moreover, two cases with different wells and well types were studied to illustrate the effectiveness and accuracy of the VFRBF-FSO method. The optimization results showed that the VFRBF-FSO method performed comparably with the HF model-based method in terms of convergence and diversity. However, the VFRBF-FSO reduced the simulation runs on the two cases with different wells and fracture types to about five times that of the HF model." @default.
• W4220654329 created "2022-04-03" @default.
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• W4220654329 date "2022-11-01" @default.
• W4220654329 modified "2023-10-13" @default.
• W4220654329 title "Optimization design of horizontal well fracture stage placement in shale gas reservoirs based on an efficient variable-fidelity surrogate model and intelligent algorithm" @default.
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• W4220654329 doi "https://doi.org/10.1016/j.egyr.2022.02.228" @default.
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