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 here an extension of these approaches,  using non-linear slope tomography (rather than standard tomographic migration velocity analysis as in previous publications). In addition to the advantages in terms of accuracy and efficiency of the velocity model building (compared to standard tomography) it can be used to assess the quality of standard uncertainty-related assumptions (linearity and Gaussian hypothesis within the Bayesian theory) and estimate volumetric migration positioning uncertainties (a generalization of horizon uncertainties)  . We derive and discuss the theoretical concepts underlying this approach and compare our derivations with those of previous publications. A main advantage is that we work directly in the full model space rather than in a preconditioned model space, (1) avoiding biased uncertainty analysis and (2) splitting the analysis into the resolved and unresolved tomography spaces. Another advantage is that , within the Bayesian formalism, we sample an equi-probable contour of the tomography posterior probability density function (pdf) rather than the full pdf, stabilizing the estimation of error bars. These advantages provide robustness to the approach . These concepts are  illustrated on two different 3D field datasets . The first one illustrates structural uncertainties on a merge of different seismic surveys in the North Sea. The second one shows the impact of structural uncertainties on gross-rock volume computation .
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 .
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here
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sampling an equi-probable contour of the tomography posterior probability density function (pdf) rather than the full pdf, and
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In addition to the advantages in terms of accuracy and efficiency of the velocity model building (compared to standard tomography) it can be used to
Our approach allows to
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standard
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, in addition to the advantages in terms of efficiency
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and discuss
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compare
unify
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A main advantage is that we work directly
As the method works
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(1) avoiding biased uncertainty analysis and (2) splitting
we split
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Another advantage is that , within the Bayesian formalism, we sample an equi-probable contour of the tomography posterior probability density function (pdf) rather than the full pdf, stabilizing the estimation of error bars. These advantages provide robustness to
We argue that
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approach
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
A
demonstrated on a synthetic data. Complementarily, the industrial viability of the method is
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. The first one illustrates structural uncertainties on a merge of different seismic surveys in the North Sea. The second one shows the impact of structural uncertainties on gross-rock volume computation