Deep Conditional Generative Adversarial Network for History Matching with Complex Geologies

History matching is an important part of reservoir numerical simulation research and the basis for predicting oilfield development performance. At present, integrated smoother and ensemble Kalman filter methods are the mainstream methods of history matching. However, these data assimilation algorithms need to be based on Gaussian assumptions and cannot guarantee the geological authenticity of the modified model. Therefore, to achieve end-to-end processing of dynamic data to modify the reservoir model, while ensuring that the distribution of data does not need to be restricted by Gaussian distribution, this paper proposes the use of the deep conditional generative adversarial network (CGAN) method for automatic reservoir history matching for the first time. The mapping relationship between static geological reservoir parameters and dynamic production data is found through the training generative network, which reduces the uncertainty of the reservoir model obtained by automatic history matching and explains parallel offline processing. The numerical simulation results show that the prediction results on the 2D two-phase flow model are consistent with the results of the proposed deep conditional generative adversarial network method.