Deep Conditional Generative Adversarial Network Combined With Data-Space Inversion for Estimation of High-Dimensional Uncertain Geological Parameters

Inverse modeling can provide a reliable geological model for subsurface flow numerical simulation, which is a challenging issue that requires calibration of the uncertain parameters of the geological model to establish an acceptable match between simulation data and observation data. The general inverse modeling method needs to iteratively adjust the uncertain parameters, which is a difficult and time-consuming high-dimensional sampling problem. To address this problem, we propose a deep-learning-based inverse modeling method called pix2pixGAN-DSI. In this method, the deep-learning-based image-to-image generative adversarial network (pix2pixGAN) is constructed to directly predict the posterior parameter fields from the posterior dynamic responses obtained by the data-space inversion (DSI) method. This inverse modeling method does not need to iteratively adjust the uncertain parameters, which improves computational efficiency. The effectiveness of the proposed method is verified through a Gaussian model case and two non-Gaussian channelized model cases. Through the analysis of posterior realizations, matching and forecast of production data, and uncertainty quantification, the results show that the proposed method can obtain reasonable estimates without iteration and parameterization.