Seismic horizons depth positioning analysis for Bayesian geophysical basin modeling

Abstract

This work explores the links between basin modeling and different kinds of data, to establish probabilistic assessments of data-calibrated subsurface basin models through an interdisciplinary approach. The proposed workflow is built upon the geophysical basin modeling approach in a Bayesian framework. We call this interdisciplinary approach Bayesian geophysical basin modeling (BGBM), which involves basin modeling, rock physics, reflection seismology and statistics. In the conventional BGBM workflow it is computational expensive to spatially condition basin models ensembles with pre-stack seismic data. This work presents the use of the positioning of the interpreted seismic depth horizons from post-stack seismic data, instead of the migration processes, to spatially conditioning basin models thus reducing computational cost. We applied the proposed workflow to a 2D basin model using data from the Gulf of Mexico. We compare the uncertainty quantification when using both spatial conditioning schemes: depth positioning of horizons from post-stack data, and seismic imaging analysis using pre-stack data, showing that the results are comparable. This makes the BGBM workflow much more efficient and practically feasible.