Recently, the team of Zhang Kai has made new progress in the field of inversion modeling of high-dimensional uncertain geological parameters, and the related research has been published in the Water Resources Research. The paper is titled " Deep Conditional Generative Adversarial Network Combined With Data-Space Inversion for Estimation of High-Dimensional Uncertain Geological Parameters ".

Innovation: Numerical simulation is an effective means to characterize the flow of subsurface fluid and achieve efficient development of subsurface resources. Inverse modeling can calibrate the uncertain parameters of the numerical model and thus ensure simulation accuracy. However, most inverse modeling methods are based on iteratively adjusting the uncertain parameters, which requires performing a great deal of time-consuming numerical simulations. In the last decades, significant progress has been made in the field of machine learning, especially in the field of deep learning. In this study, we use the generative adversarial network (a key achievement of deep learning) in combination with the data-space inversion method to establish a novel inverse modeling workflow.

Abstract: 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.

The Water Resources Research publishes original research articles and commentaries on hydrology, water resources, and the social sciences of water that provide a broad understanding of the role of water in Earth’s system. The latest impact factor of the journal is 5.4, and the average impact factor IF in the past 3 years is 5.6. This journal currently appears in the JCR Q1 or the Chinese Academy of Sciences ranking 1.

Paper link:

https://doi.org/10.1029/2022WR032553

Citation:

Fu, W., Zhang, K., Ma, X., et al. Deep conditional generative adversarial network combined with data-space inversion for estimation of high-dimensional uncertain geological parameters[J] Water Resources Research, 2023, 59(3): e2022WR032553.