Efficient well placement optimization based on theory-guided convolutional neural network

Wang，Nanzhe1; Chang，Haibin1; Zhang，Dongxiao2,3; Xue，Liang4; Chen，Yuntian3

2022

10.1016/j.petrol.2021.109545

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING   影响因子和分区

0920-4105

1873-4715

Well placement optimization is important in reservoir management, but it is challenging to implement due to the high-dimensional solution space and large number of reservoir simulations required. Surrogate models may assist to alleviate the computational burden by efficiently approximating full-order models. Although deep learning has been proven to be effective for surrogate modeling, most deep learning surrogates are purely data-driven, and underlying physical principles or theories of subsurface flows are not considered. In this work, a theory-guided convolutional neural network (TgCNN) framework is extended as a surrogate for subsurface flows with position-varying sink/source terms (well locations), which is further utilized for well placement optimization. In TgCNN, the physical constraints are incorporated to guide the training process of the surrogate by adding the residual of governing equations (and boundary/initial conditions) into the loss function. Guided by theory, the TgCNN surrogate can achieve better accuracy and generalizability, even when trained with limited data. The trained TgCNN surrogate can be further used for well placement optimization by combining it with the genetic algorithm (GA). The TgCNN surrogate also achieves satisfactory extrapolation performance for scenarios with different well numbers, and thus joint optimization of well number and placement can also be implemented with the TgCNN surrogate. The performance of the proposed optimization strategy is compared with the optimization framework that uses the simulator directly, and the results verify the accuracy of the TgCNN surrogate-based GA. Moreover, using the TgCNN surrogate can improve the efficiency of optimization significantly compared with running the simulators repeatedly. The effect of geologic uncertainty for the optimization is also investigated, and the results demonstrate that the optimization results may deviate from the optimal well placements as the degree of uncertainty increases.

Genetic algorithm Optimization Surrogate modeling Theory-guided convolutional neural network Uncertainty Well placement

SCI ; EI

英语

通讯

Shenzhen Key Laboratory of Natural Gas Hydrates[ZDSYS20200421111201738]

Energy & Fuels ; Engineering

Energy & Fuels ; Engineering, Petroleum

WOS:000710878400059

ELSEVIER

20214010970806

Computation theory ; Convolution ; Convolutional neural networks ; Deep learning ; Reservoir management ; Uncertainty analysis

Ergonomics and Human Factors Engineering:461.4 ; Petroleum Deposits : Development Operations:512.1.2 ; Information Theory and Signal Processing:716.1 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Probability Theory:922.1

GEOSCIENCES

2-s2.0-85115923753

Scopus

1.BIC-ESAT,ERE,and SKLTCS,College of Engineering,Peking University,Beijing,100871,China
2.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Intelligent Energy Laboratory,Peng Cheng Laboratory,Shenzhen,518000,China
4.State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum,Beijing,102249,China

Wang，Nanzhe,Chang，Haibin,Zhang，Dongxiao,et al. Efficient well placement optimization based on theory-guided convolutional neural network[J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING,2022,208.

Wang，Nanzhe,Chang，Haibin,Zhang，Dongxiao,Xue，Liang,&Chen，Yuntian.(2022).Efficient well placement optimization based on theory-guided convolutional neural network.JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING,208.

Wang，Nanzhe,et al."Efficient well placement optimization based on theory-guided convolutional neural network".JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 208(2022).