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Bayesian geophysical basin modeling (BGBM) methodology is aninterdisciplinary workflow that incorporates data, geological expertise, andphysical processes through Bayesian inference in sedimentary basin models. Itsapplication culminates in subsurface models that integrate the geo-history of abasin, rock physics definitions, well log and drilling data, and seismicinformation. Monte Carlo basin modeling realizations are performed by samplingfrom prior probability distributions on facies parameters and basin boundaryconditions. After data assimilation, the accepted set of posterior sub-surfacemodels yields uncertainty quantification of subsurface properties. Thisprocedure is especially suitable for pore pressure prediction in a predrillstage. However, the high computational cost of seismic data assimilationdecreases the practicality of the workflow. Therefore, we introduce andinvestigate seismic traveltimes criteria as computationally faster proxies foranalyzing the seismic data likelihood when employing BGBM. The proposedsurrogate schemes weigh the prior basin model results with the availableseismic data with no need to perform expensive seismic depth-migrationprocedures for each Monte Carlo realization. Furthermore, we apply BGBM in areal field case from the Gulf of Mexico using a 2D section for pore pressureprediction considering different kinematics criteria. BGBM implementation withthe novel seismic data assimilation proxies is compared with a computationallyexpensive benchmark approach. Moreover, we validate and compare the outcomesfor predicted pore pressure with mud-weight data from a blind well. The fastproxy of analyzing the depth-positioning of seismic horizons proposed in thiswork yields similar uncertainty quantification results in pore pressureprediction compared to the benchmark. These fast proxies make the BGBMmethodology efficient and practical.

Bayesian Geophysical Basin Modeling with Seismic Kinematics Metrics to Quantify Uncertainty for Pore Pressure Prediction