A Bayesian Approach for Thermal History Reconstruction in Basin Modeling

Abstract

AbstractWe present a novel method for the joint inversion of thermal indicator data (vitrinite reflectance and apatite fission track) and additional data (bottom‐hole temperature and porosity) for thermal history reconstruction in basin modeling. A transdimensional and hierarchical Bayesian formulation is implemented with a reversible jump Markov chain Monte Carlo algorithm, with a 1‐D transient thermal model. The objective of the inverse problem is to infer the heat flow history below a borehole given the data and a set of geological constraints (e.g., burial histories and physical properties of the sediments). The algorithm incorporates an adaptive, data‐driven parametrization of the heat flow history, allows for automatic estimation of the relative importance of each data type and quantification of parameter uncertainties and trade‐offs. Our approach deals with uncertainties on the imposed geological constraints in two ways. First, the amount of erosion and timing of an erosional event are treated as independent parameters. Second, uncertainties on compaction parameters and surface temperature history are directly propagated into the final solution. Synthetic tests show that porosity data can be used to reduce uncertainties on the amount of erosion. This work illustrates a truly probabilistic analysis of the trade‐off between the magnitude of erosion and variations in heat flow histories which is key in basin modeling. The algorithm is then applied to real data from a well in the Barents Sea. Our algorithm can reconcile estimates of erosion from the thermal indicator and porosity data, which is a difficult and subjective task in basin modeling.