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 Geoenergy Science and Engineering. The paper is titled " A physics-guided autoregressive model for saturation sequence prediction ".
Innovation: Considering the limitations of most existing deep learning-based surrogate models in processing static and dynamic data simultaneously, and the lack of guidance from physical equations, a physics-guided autoregressive model is proposed to solve these problems. The surrogate model introduces the idea of explicit finite difference to make it work more in line with physical processes; the embedding of the mass balance equation makes its prediction accuracy significantly improved.
Abstract: A deep-learning-based surrogate model is developed and applied for predicting dynamic saturation sequences in oil-water two-phase reservoirs. Considering the limitations of most existing deep learning-based surrogate models in processing static and dynamic data simultaneously, and the lack of guidance from physical equations, a physics-guided autoregressive model is proposed to solve these problems. The surrogate model introduces the idea of explicit finite difference to make it work more in line with physical processes; the embedding of the mass balance equation makes its prediction accuracy significantly improved. To evaluate the performance of the proposed method, three representative models are comprehensively compared, namely the recurrent R-U-Net, the autoregressive model without the physical meaning embedded and the physics-guided autoregressive model. And experiments show that the physics-guided autoregressive model is capable of predicting accurate dynamic saturation maps and achieves very competitive results. Compared to the numerical simulation, the trained surrogate model is capable of predicting the saturation sequence efficiently, quickly, and accurately. We believe it has the potential to replace the forward process of numerical simulation when predicting saturation.
The Geoenergy Science and Engineering(formerly known as 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 5.168, 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.geoen.2022.211373
Citation:
Yanzhong Wang, Kai Zhang*, Xiaopeng Ma, Piyang Liu, Haochen Wang, Xin Guo, Chenglong Liu, Liming Zhang, Jun Yao. A physics-guided autoregressive model for saturation sequence prediction[J]. Geoenergy Science and Engineering, 2023, 221:211373.
