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 Petroleum Science. The paper is titled " Multi-surrogate framework with an adaptive selection mechanism for production optimization ".
Innovation: Data-driven alternative models, which use efficient evolutionary algorithms to find optimal development solutions, have been widely used to solve reservoir production optimization problems. However, existing research shows that the effectiveness of proxy models can vary depending on the complexity of the design problem. A proxy model that appears successful in one scenario may perform poorly in others. In addition, the optimization process often relies on a single evolutionary algorithm, which may produce different results in different situations. To address these limitations, this paper introduces a new approach called Multi-agent Framework with Adaptive Selection Mechanism (MSFASM) to solve the production optimization problem. MSFASM consists of two phases. Firstly, a dimensionality reduction generalized learning system (BLS) is used to adaptively select the evolutionary algorithm with the best performance in the current optimization period. In the second stage, the multi-objective non-dominated sorting Genetic Algorithm II (NSGA-II) is used as an optimizer to find a set of well-performing Pareto solutions on multiple agent models.
Abstract: Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems. However, existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem. A surrogate model that has demonstrated success in one scenario may not perform as well in others. In the absence of prior knowledge, finding a promising surrogate model that performs well for an unknown reservoir is challenging. Moreover, the optimization process often relies on a single evolutionary algorithm, which can yield varying results across different cases. To address these limitations, this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism (MSFASM) to tackle production optimization problems. MSFASM consists of two stages. In the first stage, a reduced-dimensional Broad Learning System (BLS) is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period. In the second stage, the multi-objective algorithm, non-dominated sorting genetic algorithm II (NSGA-II), is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models. A novel optimal point criterion is utilized in this stage to select the Pareto solutions, thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator. The two stages are combined using sequential transfer learning. From the two most important perspectives of an evolutionary algorithm and a surrogate model, the proposed method improves adaptability to optimization problems of various reservoir types. To verify the effectiveness of the proposed method, four 100-dimensional benchmark functions and two reservoir models are tested, and the results are compared with those obtained by six other surrogate-model-based methods. The results demonstrate that our approach can obtain the maximum net present value (NPV) of the target production optimization problems.
The Journal of Petroleum Science 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 5.6, and the average impact factor IF in the past 3 years is 5.47. This journal currently appears in the JCR Q1 or the Chinese Academy of Sciences ranking 2.
Paper link:
https://doi.org/10.1016/j.petsci.2023.08.028
Citation:
Jialin Wang, Liming Zhang*, Kai Zhang, Jian Wang, Jianping Zhou, Wenfeng Peng, Faliang Yin, Chao Zhong, Xia Yan, Piyang Liu, Huaqing Zhang, Yongfei Yang, Hai Sun. Multi-surrogate framework with an adaptive selection mechanism for production optimization [J]. Petroleum Science, 2023, ISSN 1995 – 8226
