首页|A hybrid physics-informed data-driven neural network for CO2 storage in depleted shale reservoirs

A hybrid physics-informed data-driven neural network for CO2 storage in depleted shale reservoirs

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To reduce CO2 emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most cur-rent CO2 sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO2 storage processes usually involves laborious and time-consuming numerical simula-tions unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO2 storage in depleted shale reservoirs,supporting the establishment of a training data-base.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO2 storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO2 storage projects in depleted shale reservoirs.

Deep learningPhysics-informed data-driven neural networkDepleted shale reservoirsCO2 storageTransport mechanisms

Yan-Wei Wang、Zhen-Xue Dai、Gui-Sheng Wang、Li Chen、Yu-Zhou Xia、Yu-Hao Zhou

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College of Construction Engineering Jilin University,Changchun,130026,Jilin,China

Institute of Intelligent Simulation and Early Warning for Subsurface Environment Jilin University,Changchun,130026,Jilin,China

School of Energy Resources,China University of Geosciences,Beijing,100083,China

Department of Safety,Environmental Protection and Quality Management,Shengli Oilfield,Sinopec,Dongying,257000,Shandong China

Key Laboratory of Thermo-Fluid Science and Engineering of MOE,School of Energy and Power Engineering Xi'an Jiao tong University,Xi'an,710049,Shaanxi,China

College of Computer Science and Technology,China University of Petroleum(East China),Qingdao,266580,Shandong China

Department of Physics,University of Fribourg Fribourg 1700,Switzerland

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国家自然科学基金国家自然科学基金Program for Jilin University(JLU)Science and Technology Innovative Research Team

42202292421410112019TD-35

2024

10.1016/j.petsci.2023.08.032

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(1)
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