Bayesian RockAVO: Direct petrophysical inversion with hierarchical conditional GANs

Miguel Angel Corrales Guerrero, Muhammad Izzatullah, Matteo Ravasi, Hussein Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Reservoir characterization is a critical component in any oil and gas, geothermal, and CO2 sequestration project. A fundamental step in the process of characterizing the subsurface is represented by the inversion of petrophysical parameters from seismic data. However, this problem suffers from various uncertainty sources originating from inaccuracies in the measurements, modeling errors, and complex geological processes. Moreover, the non-linearity of the rock-physics model and Zoeppritz equation that constitute the modelling operator, further complicates the inversion process. In this work, we propose a novel data-driven approach where well-log information is used to obtain optimal basis functions that link band-limited petrophysical reflectivities to pre-stack seismic data. Subsequently, the inversion of such band-limited reflectivities for petrophysical parameters is framed in a Bayesian framework where a generative adversarial network is used to produce a geologically realistic prior distribution. The trained prior distribution is updated using the Stein Variational Gradient Descent and a set of representative solutions is produced that is consistent with the uncertainties in the data and the nonlinear operators.
Original languageEnglish (US)
Title of host publicationSecond International Meeting for Applied Geoscience & Energy
PublisherSociety of Exploration Geophysicists and American Association of Petroleum Geologists
DOIs
StatePublished - Aug 15 2022

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