Publications Details

Enhanced Geothermal Site Characterization using Generative Adversarial Network and Ensemble Method

Bao, Jichao; Lee, Jonghyun; Yoon, Hongkyu

Characterizing the subsurface properties such as permeability and thermal conductivity is important for stimulation planning and heat production in enhanced geothermal systems (EGS). Data assimilation methods, such as the Kalman-type methods, are widely used for characterization by assimilating observed dynamic data such as pressure and temperature. However, these approaches only perform well when the parameters follow a Gaussian distribution. The geothermal sites are usually highly heterogeneous with non-Gaussian distributed complex structures such as faults and fractures, which are difficult to characterize. Over the past few years, emerging deep generative models and their impressive applications in different tasks have provided a solution to produce images with complicated features. In this work, we use the Wasserstein Generative Adversarial Network (WGAN), a deep generative model, to generate fractured images from the low-dimensional and Gaussian distributed latent space. The ensemble method, a Kalman-type data assimilation method, is then applied to the latent variables to characterize the permeability fields of a fractured geothermal site using temperature data. A synthetic two-dimensional example is presented to show the performance of our approach.