We present a framework that enables estimation of low-dimensional reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential analysis approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties, as well as generate samples from the posterior distribution. We discuss methods of learning highly informative summary statistics from seismic data, which help minimizing computational costs of the approach. We demonstrate the efficacy of our approach by estimating the posterior distribution of reservoir net-to-gross for sub-resolution thin-sand synthetic reservoir.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 209A (Anaheim Convention Center)
Presentation Type: Oral