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 Journal of Petroleum Science and Engineering. The paper is titled "Surrogate-reformulation-assisted multitasking knowledge transfer for production optimization".
Innovation: Evolutionary algorithms usually call for lots of numerical simulations, and the solution speed is difficult to meet the needs of practical engineering problems. Meanwhile, multiple-surrogate-model-assisted evolutionary algorithms usually get multiple samples independently, and the quality of the obtained sample points needs to be further improved. For a target production optimization problem, the method proposed in this research constructs global and local surrogate models simultaneously, and uses multitasking optimization to get the optimal solution of the two surrogate models at the same time, which can effectively improve the sample quality and optimization efficiency. In addition, an adaptive transfer mechanism based on similarity is proposed to avoid negative transfer and improve efficiency and stability.
Abstract: Data-driven surrogate models, which are trained by samples to replace time-consuming numerical simulations, have been widely used to solve production optimization problems in recent years. It is a challenging and meaningful subject to research advanced surrogate-model-based methods that can obtain superior optimization performance within a limited time budget. The key is to enhance the quality of each training sample, i.e., the contribution of each sample to the overall optimization performance improvement, because the acquisition of each sample requires a numerical simulation that generally costs tens of minutes or even several hours. To obtain samples with enhanced quality, a novel approach named surrogate-reformulation-assisted multitasking knowledge transfer (SRAMT) for production optimization is proposed in this research. Multiple surrogate models, which can imitate the landscape of the initial production optimization problem, are constructed with diverse samples as reformulations of the target problem. These models reflect different landscape information of the same problem and thus can be regarded as multiple associated optimization instances, which just provide a solid foundation for the subsequent process. Then, an advanced optimization method, namely multitasking optimization (MTO), is leveraged to find optimal solutions for these surrogates. MTO can handle several optimization instances simultaneously and boost the performance of each one by transferring useful knowledge among tasks. Besides, in the absence of prior knowledge about the target production optimization task, as in most situations, an approach is proposed to determine the frequency of knowledge transfer adaptively based on the similarity between surrogates to improve efficiency and stability. To verify the effect, four 100-dimensional benchmark functions and two reservoir models are tested on the method and the results are in comparison with those obtained by differential evolution (DE) algorithm and three other surrogate-model-based methods. The results show that the proposed method can achieve optimal well controls which can get the highest net present value (NPV) for target production optimization problems and have superior convergence speed.
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.2021.109486
Citation:
Zhong C, Zhang K, Xue X, et al. Surrogate-reformulation-assisted multitasking knowledge transfer for production optimization[J]. Journal of Petroleum Science and Engineering, 2022, 208: 109486.
