Multi-scale uncertainty quantification in geostatistical seismic inversion

TL;DR

This paper introduces a framework that enhances geostatistical seismic inversion by jointly estimating large-scale geological parameters and local properties, leading to more accurate and realistic uncertainty quantification in subsurface models.

Contribution

It proposes a novel coupling of seismic inversion with Bayesian inference and adaptive sampling to better quantify uncertainties in geological parameters.

Findings

More reliable acoustic impedance models obtained

Enhanced uncertainty spread compared to conventional methods

Effective separation of large-scale and local-scale uncertainties

Abstract

Geostatistical seismic inversion is commonly used to infer the spatial distribution of the subsurface petro-elastic properties by perturbing the model parameter space through iterative stochastic sequential simulations/co-simulations. The spatial uncertainty of the inferred petro-elastic properties is represented with the updated a posteriori variance from an ensemble of the simulated realizations. Within this setting, the large-scale geological (metaparameters) used to generate the petro-elastic realizations, such as the spatial correlation model and the global a priori distribution of the properties of interest, are assumed to be known and stationary for the entire inversion domain. This assumption leads to underestimation of the uncertainty associated with the inverted models. We propose a practical framework to quantify uncertainty of the large-scale geological parameters in seismic inversion. The framework couples geostatistical seismic inversion with a stochastic adaptive sampling and Bayesian inference of the metaparameters to provide a more accurate and realistic prediction of uncertainty not restricted by heavy assumptions on large-scale geological parameters. The proposed framework is illustrated with both synthetic and real case studies. The results show the ability retrieve more reliable acoustic impedance models with a more adequate uncertainty spread when compared with conventional geostatistical seismic inversion techniques. The proposed approach separately account for geological uncertainty at large-scale (metaparameters) and local scale (trace-by-trace inversion).