  Bayesian geophysical basin modeling (BGBM) methodology is an
interdisciplinary workflow that incorporates data, geological expertise, and
physical processes through Bayesian inference in sedimentary basin models. Its
application culminates in subsurface models that integrate the geo-history of a
basin, rock physics definitions, well log and drilling data, and seismic
information. Monte Carlo basin modeling realizations are performed by sampling
from prior probability distributions on facies parameters and basin boundary
conditions. After data assimilation, the accepted set of posterior sub-surface
models yields uncertainty quantification of subsurface properties. This
procedure is especially suitable for pore pressure prediction in a predrill
stage. However, the high computational cost of seismic data assimilation
decreases the practicality of the workflow. Therefore, we introduce and
investigate seismic traveltimes criteria as computationally faster proxies for
analyzing the seismic data likelihood when employing BGBM. The proposed
surrogate schemes weigh the prior basin model results with the available
seismic data with no need to perform expensive seismic depth-migration
procedures for each Monte Carlo realization. Furthermore, we apply BGBM in a
real field case from the Gulf of Mexico using a 2D section for pore pressure
prediction considering different kinematics criteria. BGBM implementation with
the novel seismic data assimilation proxies is compared with a computationally
expensive benchmark approach. Moreover, we validate and compare the outcomes
for predicted pore pressure with mud-weight data from a blind well. The fast
proxy of analyzing the depth-positioning of seismic horizons proposed in this
work yields similar uncertainty quantification results in pore pressure
prediction compared to the benchmark. These fast proxies make the BGBM
methodology efficient and practical.
