Various models have been proposed to link partial gas saturation to seismic attenuation and dispersion, suggesting that the reflection coefficient should be frequency-dependent in many cases of practical importance. Previous approaches to studying this phenomenon have typically been limited to single interface models. Here we propose a modelling technique which allows us to incorporate frequency-dependent reflectivity into convolutional modelling. With this modelling framework, seismic data can be synthesized from well logs of velocity, density, porosity and water saturation. This forward modelling could act as a basis for inversion schemes aimed at recovering gas saturation variations with depth. We present a Bayesian inversion scheme for a simple 3-layer case and a particular rock physics model, and show that with appropriate prior geological information, the technique could potentially estimate gas saturation and layer thickness despite the occurrence of interfering reflections.
Presentation Date: Monday, October 17, 2016
Start Time: 3:45:00 PM
Presentation Type: ORAL