Developing a probabilistic compaction model for the Northern Carnarvon Basin using Bayesian inference

Abstract

AbstractExhumation affects sedimentary basin evolution by influencing structural, pressure and temperature dynamics, thereby impacting energy resource formation. Compaction‐based methods are widely used to quantify exhumation, utilising sonic and porosity data to track sediment uplift from its maximum burial depths. However, uncertainties arise from applying empirical compaction models developed for specific geological regions, highlighting the need for region‐specific models. Even such region‐specific models contain uncertainties, which can compromise exhumation estimates. We, therefore, develop a probabilistic compaction model for the Northwest Shelf Basins using sonic data from normally compacted and unexhumed shales from the Northern Carnarvon Basin (NCB). The model's robustness is estimated using MCMC, and uncertainty propagation analysis is employed to assess the impact of model uncertainty on the model's predictive applications. The model shows exponential porosity reduction with depth, demonstrating rapid compaction from the surface to ca. 2 km and slower compaction thereafter. The model is then applied to interpret new datasets from the Canning, Gippsland and NCB regions. The results reveal that while some parts of the NCB exhibit normal compaction without exhumation, others were significantly exhumed. Conversely, Canning and Gippsland Basin data indicate signs of significant exhumation, as suggested by previous studies, thereby confirming the model's effectiveness outside the Northwest Shelf. Since the model could not explain data from exhumed regions, we inferred new models incorporating “exhumation” parameters to interpret the complex compaction histories of these areas, and the best‐fitting models were selected using the Bayes Factor method. Uncertainty analysis revealed that the impacts of model uncertainty on exhumation estimates are consistent across wide depth ranges. Our findings highlight the need to refine compaction models for better predictive reliability and informed resource exploration in sedimentary basins.