Physics-informed deep learning for rock physical inversion and its uncertainty analysis

Abstract

Geological CO2 storage is aiming to inject the carbon dioxide into subsurface formations, and geophysical measurements are then commonly used to monitor the fluid long-term and safe storage for risk assessment during and after the injection. In this process, the rock physical inversion is an essential part for determining the reservoir parameters such as porosity or fluid saturation for potential storage calculation or monitoring fluid migration. We propose a deep learning approach to invert reservoir parameters based on rock properties or seismically inverted results. The rock physics equations are incorporated into the learning process, leading the neural networks as physics informed. We choose the generative adversarial networks to obtain ensemble predictions by varying the input latent vectors, from which the uncertainty analysis is performed. The proposed approach is applied to the Sleipner 2019 Benchmark Model for inverting reservoir porosity and CO2 saturation with rock properties in terms of velocities and bulk density as inputs. The supervised learning and physics-informed neural network are also applied for a comparison; however, both of them cannot access the prediction uncertainty that is important for risk reduction by decision makers.