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Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

MICHELIOUDAKIS, DIMITRIOS (2018) Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics. Doctoral thesis, Durham University.

PDF (PhD Thesis) - Accepted Version


Velocity model building is a critical step in seismic reflection data processing. An optimum velocity field can lead to well focused images in time or depth domains. Taking into account the noisy and band limited nature of the seismic data, the computed velocity field can be considered as our best estimate of a set of possible velocity fields. Hence, all the calculated depths and the images produced are just our best approximation of the true subsurface.

This study examines the quantification of uncertainty of the depths to drilling targets from two dimensional (2D) seismic reflection data using Bayesian statistics. The approach was tested in Mentelle Basin (south west of Australia), aiming to make depths predictions for stratigraphic targets of interest related with the International Ocean Discovery Program (IODP), leg 369.

For the purposes of the project, Geoscience Australia 2D seismic profiles were reprocessed. In order to achieve robust predictions, the seismic reflection processing sequence was focused on improving the temporal resolution of the data by using deterministic deghosting filters in pre-stack and post-stack domains. The filters, combined with isotropic/anisotropic pre-stack time and depth migration algorithms, produced very good results in terms of seismic resolution and focusing of subsurface features. The application of the deghosting filters was the critical step for the subsequent probabilistic depth estimation of drilling targets.

The best estimate of the velocity field along with the migrated seismic data were used as input to the Bayesian algorithm. The analysis, performed in one seismic profile intersecting the site location MBAS-4A, produced robust depth predictions for lithological boundaries of interest compared to the observed depths as reported in the IODP expedition. The significance of the result is more pronounced taking into account the complete lack of independent velocity information.

Petrophysical information collected from the expedition was used to perform well-seismic tie, mapping the lithological boundaries with the reflectivity in the seismic profile. A very good match between observed and modelled traces was achieved and a new interpretation of the Mentelle Basin lithological boundaries in seismic image was provided. Velocity information from sonic logs was also implemented to perform anisotropic pre-stack depth migration. The migrated image successfully mapped the subsurface targets to their correct depth location while preserving the focus of the image.

The pre-drilling depth estimation of subsurface targets using Bayesian statistics can be considered as a great example of successfully quantifying the uncertainty in depths and effectively merging seismic reflection data processing with statistical analysis. The derived well-seismic tie in MBAS-4A will be a valuable tool towards a more complete regional interpretation of the Mentelle Basin.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Keywords:IODP, Mentelle Basin, Seismic reflection processing, Bayesian, migration, well-seismic tie
Faculty and Department:Faculty of Science > Earth Sciences, Department of
Thesis Date:2018
Copyright:Copyright of this thesis is held by the author
Deposited On:20 Nov 2018 11:46

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