An artificial-neural-network-based surrogate modeling workflow for reactive transport modeling

作     者：Yupeng Li Peng Lu Guoyin Zhang Yupeng Li;Peng Lu;Guoyin Zhang

作者机构：Beijing Research Center of EXPEC Advanced Research CenterAramco AsiaBeijingChina EXPEC Advanced Research CenterSaudi AramcoDhahranSaudi Arabia China University of PetroleumQingdaoChina 

出 版 物：《Petroleum Research》 (石油研究（英文）)

年 卷 期：2022年第7卷第1期

页      面：13-20页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Indiana University  IU 

主　　题：Reactive transport modeling Surrogate model Machine learning Dolomitization Carbonate reservoir 

摘      要：Process-based reactive transport modeling(RTM)integrates thermodynamic and kinetically controlled fluid-rock interactions with fluid flow through porous media in the subsurface and surface *** is usually conducted through numerical programs based on the first principle of physical ***,the calculation for complex chemical reactions in most available programs is an iterative process,where each iteration is in general computationally intensive.A workflow of neural networkbased surrogate model as a proxy for process-based reactive transport simulation is established in this *** workflow includes(1)base case RTM design,(2)development of training experiments,(3)surrogate model construction based on machine learning,(4)surrogate model validation,and(5)prediction with the calibrated *** training experiments for surrogate modeling are generated and run prior to the predictions using *** results show that the predictions from the surrogate model agree well with those from processes-based RTM but with a significantly reduced computational *** well-trained surrogate model is especially useful when a large number of realizations are required,such as the sensitivity analysis or model calibration,which can significantly reduce the computational time compared to that required by *** benefits are(1)it automatizes the experimental design during the sensitivity analysis to get sufficient numbers and coverage of the training cases;(2)it parallelizes the calculations of RTM training cases during the sensitivity analysis to reduce the simulation time;(3)it uses the neural network algorithm to rank the sensitivity of the parameters and to search the optimal solution for model calibration.