On tomography velocity uncertainty in relation with structural imaging

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 approach...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2020-12
Main Authors: Messud, Jérémie, Guillaume, Patrice, Lambaré, Gilles
Format: Article
Language:English
Subjects:
Advantages
Bayesian analysis
Conditional probability
Decision making
Documents
Industrial applications
Linearity
Model accuracy
Oil exploration
Probability density functions
Quality assessment
Target recognition
Tomography
Uncertainty analysis
Velocity
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422