Full text loading...
-
Deep Learning a Poro-Elastic Rock Physics Model for Pressure and Saturation Discrimination
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, First EAGE Digitalization Conference and Exhibition, Nov 2020, Volume 2020, p.1 - 5
Abstract
Utilizing time lapse seismic for determining pore pressure and saturation effects is relevant for hydrocarbon production as well as natural gas and CO2 storage. Its quantitative interpretation enables a detailed understanding of 4D evolution of fluid/gas migration. We focus on the rock physics model to invert for rock physical parameters. A training dataset is generated with a forward modeling operator, with parameters adapted from a 65 m deep unconsolidated high porosity reservoir from the Svelvik field laboratory, Norway. Two independent rock physical formulations are considered and multiple deep fully connected neural networks conditioned and trained to invert for different rock physics parameters. The network can rapidly derive rock physical parameters such as pressure, saturation, and porosity from seismic attributes, thus acting as an inversion tool. Subsets of the input parameters can be preset based on prior knowledge of a site. Utilizing neural networks in discriminating pressure and saturation allows real time field site conformance verification during seismic campaigns targeting 4D effects in an operations scenario.