recently, the team of Zhang Kai has made new progress in the field of reservoir history matching, and the related research has been published in the Journal of Petroleum Science and Engineering. The paper is titled " A distributed surrogate system assisted differential evolutionary algorithm for computationally expensive history matching problems".
Innovation: The solving of reservoir engineering problems such as automatic history matching often involves time-consuming reservoir numerical simulation. Based on ensemble learning technology, a distributed surrogate system is constructed to approximate the objective function of the optimization problem. This algorithm has good parallel scalability and is suitable for large-scale high-performance computing platforms. It is of great significance for reservoir engineering problems involving a large number of reservoir numerical simulations, such as automatic history matching, production optimization, well pattern or location optimization.
Abstract: Recently, surrogate assisted evolutionary algorithms (SAEAs) are widely studied and applied for history matching problems due to surrogate models can accelerate convergence. However, most of the SAEAs lose the ability of parallel sampling due to the introduction of surrogate models, in which, a small number of potential solutions are selected for evaluation in each iteration. Generally, history matching involves a large number of numerical simulations, and the role of parallel computing cannot be ignored. To address this issue, this paper proposes a distributed surrogate system-assisted differential evolution algorithm, termed DSS-DE. A distributed surrogate system (DSS) based on ensemble learning techniques is first developed, which builds a large number of basic learners before optimization, to effectively approximate different regions in the search space. Following that, performing multiple differential evolution (DE) optimizers with different mutation operators concurrently to sample a set of solutions to find as many as possible local or global optima of the data mismatch objective function. Moreover, based on the DSS prediction, a parallel infill strategy is designed to screen the potential promising solutions. Combined with the convolutional variational autoencoder (CVAE) based parameterization technique, a history matching workflow is developed. Empirical studies on two multimodal benchmark functions demonstrate that the proposed algorithm can obtain high-quality solutions on a limited computational budget. Furthermore, the proposed history matching workflow is validated on three synthetic waterflooding reservoir case studies with different geological characteristics. The test results show the effectiveness of the proposed algorithm for history matching problems.
The Journal of Petroleum Science and Engineering covers the fields of petroleum and natural gas exploration, production, and flow in its broadest possible sense. Topics include reservoir engineering; reservoir simulation; rock mechanics; petrophysics; pore-level phenomena; well logging, testing, and evaluation; mathematical modeling; enhanced oil and gas recovery; petroleum geology; compaction/diagenesis; petroleum economics; drilling and drilling fluids; thermodynamics and phase behavior; fluid mechanics; multi-phase flow in porous media; production engineering; formation evaluation; exploration methods; CO2 Sequestration in geological formations/sub-surface, etc. The latest impact factor of the journal is 4.346, and the average impact factor IF in the past 3 years is 3.646. This journal currently appears in the JCR Q1 or the Chinese Academy of Sciences ranking 2.
Paper link:
https://doi.org/10.1016/j.petrol.2021.110029
Citation:
Xiaopeng Ma, Kai Zhang, Liming Zhang, et al. A distributed surrogate system assisted differential evolutionary algorithm for computationally expensive history matching problems [J]. Journal of Petroleum Science and Engineering, 2022, 210: 110029.
