Development of a convolutional neural network based geomechanical upscaling technique for heterogeneous geological reservoir

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外文摘要：Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological res-ervoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale re-alizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R2＞0.949),stress-strain behavior(R2＞0.925),and volumetric strain changes(R2＞0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxy-based upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of nu-merical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.

外文关键词：

UpscalingLithological heterogeneityConvolutional neural network(CNN)Anisotropic shear strengthNonlinear stress-strain behavior

作者：

Zhiwei Ma、Xiaoyan Ou、Bo Zhang

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作者单位：

Earth and Environmental Sciences Division,Los Alamos National Laboratory,Los Alamos,USA

Civil and Environmental Engineering,University of Alberta,Edmonton,Canada

基金：

Future Energy System at University of Alberta and NSERC Discovery GrantReservoir Geomechanics Research Group at University of AlbertaLos Alamos National Laboratory

项目编号：

RGPIN-2023-04084LA-UR-24-21924

出版年：

2024

DOI：

10.1016/j.jrmge.2024.02.009

岩石力学与岩土工程学报(英文版)

中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD

影响因子：0.404

ISSN：1674-7755

年,卷(期)：2024.16(6)