Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network  

作　　者：LI Yanchun JIA Deli WANG Suling QU Ruyi QIAO Meixia LIU He 

机构地区：[1]College of Mechanical Science and Engineering,Northeast Petroleum University,Daqing 163318,China [2]PetroChina Research Institute of Petroleum Exploration&Development,Beijing 100083,China [3]National Key Laboratory of Green Exploitation of Continental Shale Oil with Multi-Resource Collaboration,Daqing 163712,China

出　　处：《Petroleum Exploration and Development》2024年第5期1287-1300,共14页石油勘探与开发（英文版）

基　　金：Supported by the National Natural Science Foundation of China Basic Science Center Project(72088101);National Natural Sciences Fund Projects(52074345;52274036)。

摘　　要：This paper proposes a novel intelligent method for defining and solving the reservoir performance prediction problem within a manifold space,fully considering geological uncertainty and the characteristics of reservoirs performance under time-varying well control conditions,creating a surrogate model for reservoir performance prediction based on Conditional Evolutionary Generative Adversarial Networks(CE-GAN).The CE-GAN leverages conditional evolution in the feature space to direct the evolution of the generative network in previously uncontrollable directions,and transforms the problem of reservoir performance prediction into an image evolution problem based on permeability distribution,initial reservoir performance and time-varying well control,thereby enabling fast and accurate reservoir performance prediction under time-varying well control conditions.The experimental results in basic(egg model)and actual water-flooding reservoirs show that the model predictions align well with numerical simulations.In the basic reservoir model validation,the median relative residuals for pressure and oil saturation are 0.5%and 9.0%,respectively.In the actual reservoir model validation,the median relative residuals for both pressure and oil saturation are 4.0%.Regarding time efficiency,the surrogate model after training achieves approximately 160-fold and 280-fold increases in computational speed for the basic and actual reservoir models,respectively,compared with traditional numerical simulations.The reservoir performance prediction surrogate model based on the CE-GAN can effectively enhance the efficiency of production optimization.

关 键 词：two-phase flow phase distribution deep neural network vision transformer pore-morphology-based simulator large dataset 

分 类 号：TE331[石油与天然气工程—油气田开发工程] TP183[自动化与计算机技术—控制理论与控制工程]