摘要
For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.
基金
supported by the National Natural Science Foundation of China under Grant 51722406,52074340,and 51874335
the Shandong Provincial Natural Science Foundation under Grant JQ201808
The Fundamental Research Funds for the Central Universities under Grant 18CX02097A
the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008
the Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002
the National Research Council of Science and Technology Major Project of China under Grant 2016ZX05025001-006
111 Project under Grant B08028
Sinopec Science and Technology Project under Grant P20050-1