Geostatistical Inversion for Subsurface Characterization Using Stein
Variational Gradient Descent with Autoencoder Neural Network: An
Application to Geologic Carbon Sequestration
Abstract
Geophysical subsurface characterization plays a key role in the success
of geologic carbon sequestration (GCS). While deterministic inversion
methods are commonly used due to their computational efficiency, they
often fail to adequately quantify the model uncertainty, which is
essential for informed decision-making and risk mitigation in GCS
projects. In this study, we propose the SVGD-AE method, a novel
geostatistical inversion approach that integrates geophysical data with
prior geological knowledge to estimate subsurface properties. SVGD-AE
combines Stein Variational Gradient Descent (SVGD) for sampling
high-dimensional distributions with an autoencoder (AE) neural network
for re-parameterizing reservoir models, aiming to accurately preserve
geostatistical characteristics of reservoir models derived from
geological priors. Through two synthetic examples, we demonstrate that
the SVGD-AE method outperforms traditional probabilistic methods,
particularly in inverse problems with complex posterior distributions.
Then, we apply SVGD-AE to the Illinois Basin - Decatur Project (IBDP), a
large-scale CO2 storage initiative in Decatur, Illinois, USA. The
resulting petrophysical models with quantified uncertainty enhance our
understanding of subsurface properties and have broad implications for
the feasibility, decision making, and long-term safety of CO2 storage at
the IBDP.