On tomography velocity uncertainty in relation with structural imaging

TL;DR

This paper introduces a novel Bayesian approach to seismic velocity model uncertainty quantification using non-linear slope tomography, improving efficiency and providing detailed uncertainty analysis for structural imaging in oil and gas exploration.

Contribution

It extends industrial velocity model building methods by sampling an equi-probable contour of the posterior, allowing for better uncertainty assessment and validation of Bayesian assumptions.

Findings

Method effectively assesses volumetric migration uncertainties.

Unresolved space uncertainties highlight the most uncertain areas.

Demonstrated on synthetic and real 3D datasets.

Abstract

Evaluating structural uncertainties associated with seismic imaging and target horizons can be of critical importance for decision-making related to oil and gas exploration and production. An important breakthrough for industrial applications has been made with the development of industrial approaches to velocity model building. We propose an extension of these approaches, sampling an equi-probable contour of the tomography posterior probability density function (pdf) rather than the full pdf, and using non-linear slope tomography (rather than standard tomographic migration velocity analysis as in previous publications). Our approach allows to assess the quality of uncertainty-related assumptions (linearity and Gaussian hypothesis within the Bayesian theory) and estimate volumetric migration positioning uncertainties (a generalization of horizon uncertainties), in addition to the advantages in terms of efficiency. We derive the theoretical concepts underlying this approach and unify our derivations with those of previous publications. As the method works in the full model space rather than in a preconditioned model space, we split the analysis into the resolved and unresolved tomography spaces. We argue that the resolved space uncertainties are to be used in further steps leading to decision-making and can be related to the output of methods that work in a preconditioned model space. The unresolved space uncertainties represent a qualitative byproduct specific to our method, strongly highlighting the most uncertain gross areas, thus useful for QCs. These concepts are demonstrated on a synthetic data. Complementarily, the industrial viability of the method is illustrated on two different 3D field datasets.