Recently, the team of Zhang Kai has made new progress in the field of reservoir production optimization, and the related research has been published in the SPE Journal. The paper is titled " Adaptive Basis Function Selection Enhanced Multisurrogate-Assisted Evolutionary Algorithm for Production Optimization ".
Innovation: Traditional production optimization relies on a single agent model for search, which increases the probability of wrong search direction due to prediction bias. This paper presents an adaptive basis function selection enhanced multi-agent assisted evolution algorithm (ABMSEA) for production optimization. There are two main innovations in this method. First, by training and testing different types of basis functions, adaptive selection of the best predictive performance of the basis function. Secondly, the bootstrapping method is used to construct the integrated model, which is composed of several global proxy models based on the selected best basis function.
Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have become a popular approach for solving reservoir production optimization problems. The radial-basis-function network (RBFN) is a robust surrogate model technology suitable for reservoir development with numerous wells and a long production lifetime. There are several types of basis functions for constructing RBFN models. However, existing research shows that selecting the basis function with competitive performance for the current optimization problem is challenging without prior knowledge. In conventional surrogate-assisted evolutionary algorithms, the basis function is often predetermined, but its prediction accuracy for the problem at hand cannot be guaranteed. Furthermore, canonical SAEAs usually employ only one surrogate model for the entire optimization process. However, relying on a single surrogate model for optimization increases the probability of search direction misdirection due to prediction deviations. In this paper, a novel method named adaptive basis function selection enhanced multi-surrogate-assisted evolutionary algorithm (ABMSEA) is introduced for production optimization. This method mainly includes two innovations. First, by training and testing different types of basis functions, the one with the best prediction performance is adaptively selected. Second, the ensemble model is constructed using the bootstrap sampling method, comprising multiple global surrogate models based on the selected best basis function. To search for a set of solutions that perform well on multiple surrogates, we employ an efficient multi-objective optimization algorithm called NSGA-II. This algorithm utilizes the surrogates themselves as objective functions, aiming to find solutions that yield favorable results across multiple surrogates. The proposed method improves the efficiency of production optimization while enhancing global search capabilities. To evaluate the effectiveness of ABMSEA, we conduct tests on four 100-dimensional benchmark functions, a three-channel model, and an egg model. The obtained results are compared with those obtained from differential evolution and three other surrogate-model-based methods. The experimental results demonstrate that ABMSEA exhibits accurate selection of competitive basis functions for the current optimization period, while maintaining high optimization efficiency and avoiding local optima. Consequently, our method enables optimal well control, leading to the attainment of the highest net present value (NPV).
The SPE Journal 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 3.6, 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 3.
Paper link:
https://doi.org/10.2118/217432-PA
Citation:
Jialin Wang, Kai Zhang*, Liming Zhang, Jian Wang, Wenfeng Peng, Xia Yan, Haochen Wang, Huaqing Zhang, Yongfei Yang, Hai Sun, Piyang Liu, Haichuan Chen, Xiaokun Fang, Adaptive Basis Function Selection Enhanced Multisurrogate-Assisted Evolutionary Algorithm for Production Optimization[J]. SPE Journal, 2023, SPE217432
