Application of latent variable evolution for channel reservoir characterization using generative adversarial networks and particle swarm optimization

Abstract

When characterizing channel reservoirs, ensemble-based methods may compromise channel features due to the conflict between the Gaussian distribution assumption of the methods and bimodality of channel reservoirs. To overcome this challenge, one effective approach involves leveraging generative models. Specifically, a generative model trained with channel reservoir models has ability to generate bimodally distributed values from latent vectors whose elements are intentionally designed to conform a Gaussian distribution. Therefore, it resolves the conflict, allowing the ensemble-based methods to update the latent vectors.However, generating initial latent vectors randomly poses an additional challenge and can lead to a suboptimal quality of the initial ensemble. They can significantly influence the unreliability of characterizations, primarily because their impact is strongly tied to the quality of the initial models in the ensemble-based methods.To address this challenge, we propose a novel scheme for channel reservoir characterization using Latent Variable Evolution. In this study, we adopt Generative Adversarial Networks (GAN) and Particle Swarm Optimization (PSO). PSO explores the latent space of GAN trained with channel reservoirs in order to find ones that generate models resembling a true reservoir. Validation on simple and complex reservoir cases demonstrates our proposed method can efficiently provide reliable channel reservoir characterization regardless of the quality of the initial latent vectors. These results are compared to Ensemble Smoother with Multiple Data Assimilation with GAN.