Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability

Wang，Nanzhe1,2; Chang，Haibin3; Kong，Xiang Zhao2; Zhang，Dongxiao4,5

2023-07-01

10.1016/j.renene.2023.04.088

Renewable Energy   影响因子和分区

0960-1481

1879-0682

To maximize the economic benefits of geothermal energy production, it is essential to optimize geothermal reservoir management strategies, in which geologic uncertainty should be considered. In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for the well control optimization of geothermal reservoirs. In this framework, we construct a hybrid convolution–recurrent neural network surrogate, which combines the convolution neural network (CNN) and long short-term memory (LSTM) recurrent network. The convolution structure can extract spatial information of reservoir property fields and the recurrent structure can approximate sequence-to-sequence mapping. The trained model can predict time-varying production responses (rate, temperature, etc.) for cases with different permeability fields and well control sequences. In this closed-loop optimization framework, production optimization, based on the differential evolution (DE) algorithm, and data assimilation, based on the iterative ensemble smoother (IES), are performed alternately to achieve a real-time well control optimization and to estimate reservoir properties (e.g. permeability) as the production proceeds. In addition, the averaged objective function over the ensemble of geologic parameter estimates is adopted to consider geologic uncertainty in the optimization process. Geothermal reservoir production cases are examined to evaluate the performance of the proposed closed-loop optimization framework. Our results show that the proposed framework can achieve efficient and effective real-time optimization and data assimilation in the geothermal reservoir production process.

Closed-loop optimization Data assimilation Deep learning Geothermal energy Surrogate

SCI ; EI

英语

通讯

China Scholarship Council scholarship[202106010163] ; Shenzhen Key Laboratory of Natural Gas Hydrates[ZDSYS20200421111201738]

Science & Technology - Other Topics ; Energy & Fuels

Green & Sustainable Science & Technology ; Energy & Fuels

WOS:001003879700001

PERGAMON-ELSEVIER SCIENCE LTD

20231914066247

Convolution ; Evolutionary algorithms ; Geothermal fields ; Long short-term memory ; Optimization ; Reservoir management ; Uncertainty analysis

Geothermal Phenomena:481.3.1 ; Petroleum Deposits : Development Operations:512.1.2 ; Geothermal Energy:615.1 ; Information Theory and Signal Processing:716.1 ; Optimization Techniques:921.5 ; Numerical Methods:921.6 ; Probability Theory:922.1

ENGINEERING

2-s2.0-85158046797

Scopus

1.BIC-ESAT,ERE,and SKLTCS,College of Engineering,Peking University,Beijing,100871,China
2.Geothermal Energy and Geofluids Group,Institute of Geophysics,ETH Zurich,Zurich,Sonneggstrasse 5,8092,Switzerland
3.School of Energy and Mining Engineering,China University of Mining and Technology (Beijing),Beijing,100083,China
4.Eastern Institute for Advanced Study,Eastern Institute of Technology,NingboZhejiang,315200,China
5.National Center for Applied Mathematics Shenzhen (NCAMS),Southern University of Science and Technology,ShenzhenGuangdong,518000,China

Wang，Nanzhe,Chang，Haibin,Kong，Xiang Zhao,et al. Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability[J]. Renewable Energy,2023,211:379-394.

Wang，Nanzhe,Chang，Haibin,Kong，Xiang Zhao,&Zhang，Dongxiao.(2023).Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability.Renewable Energy,211,379-394.

Wang，Nanzhe,et al."Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability".Renewable Energy 211(2023):379-394.