A general physics-based data-driven framework for numerical simulation and history matching of reservoirs
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Abstract
This paper proposed a general physics-based data-driven framework for numerical mod-eling and history matching of reservoirs that achieves a good balance of flow physics and actual field data. Underground reservoir is easily discretized in this framework as a flow network composed of one-dimensional connection elements, each of which is defined by two flow characteristic parameters. Each one-dimensional connection element is divided into some grids, and the cross-sectional area and permeability of the grids on the same connection element are equal. The fully implicit scheme of flow equations and the Newton iteration nonlinear solver concurrently solve all unknown quantities. Then, using actual field data, the simultaneous perturbation stochastic approximation algorithm is used to invert flow characteristic parameters of each connection element, and the unequal constraint that the volume of connection elements should not exceed the total reservoir volume is added to control the data-driven process. To demonstrate the unequal constraint is physical, a test case of a waterflooding reservoir with a high permeability zone is given. A waterflooding reservoir example with five injectors and four producers is used to demonstrate that this framework outperforms earlier techniques, and another case with single-phase depletion development is used to demonstrate that this framework has a high generalization for flow models. In addition, this data-driven framework based on physics is expected to serve as a reference for other fields of science and engineering.
Cited as: Rao, X., Xu, Y., Liu, D., Liu, Y., Hu, Y. A general physics-based data-driven framework for numerical simulation and history matching of reservoirs. Advances in Geo-Energy Research, 2021, 5(4): 422-436, doi: 10.46690/ager.2021.04.07
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