Approximate Bayesian inference of seismic velocity and pore pressure uncertainty with basin modeling, rock physics and imaging constraints

TL;DR

This paper introduces a Bayesian framework combining basin modeling, rock physics, and seismic data to quantify and reduce uncertainty in seismic velocity and pore pressure predictions.

Contribution

It presents a novel probabilistic approach that integrates geological history, rock physics, and seismic data for improved uncertainty quantification in basin modeling.

Findings

Reduced uncertainty in velocity and pore pressure predictions.

Effective integration of well data and basin simulations.

Application demonstrated on Gulf of Mexico data.

Abstract

We present a methodology for quantifying seismic velocity and pore pressure uncertainty that incorporates information regarding the geological history of a basin, rock physics, well log, drilling and seismic data. In particular, our approach relies on linking velocity models to the basin modeling outputs of porosity, mineral volume fractions and pore pressure through rock physics models. We account for geological uncertainty by defining prior probability distributions on uncertain parameters and performing Monte Carlo basin simulations. We perform probabilistic calibration of the basin model outputs by defining data likelihood distributions to represent well data uncertainty. Rock physics modeling transforms the basin modeling outputs to give us multiple velocity realizations used to perform multiple depth migrations. We present an approximate Bayesian inference framework which uses migration velocity analysis in conjunction with well data for updating velocity and basin modeling uncertainty. We apply our methodology in 2D to a real field case from Gulf of Mexico and demonstrate that our methodology allows building a geologic and physical model space for velocity and pore pressure prediction with reduced uncertainty.