Uncertainty analysis using Monte Carlo simulation and rock physics modelling to explore subsurface properties of Krishna–Godavari Basin, India

One of the main challenges in reservoir characterization is the accurate prediction of lithology and saturation heterogeneities. In this study, a methodology has been developed that combines Monte Carlo simulation (MCS) and rock physics modelling (RPM) to explore subsurface properties and characterize reservoirs of the Krishna–Godavari (KG) Basin, India. RPM and MCS were effecti­vely applied to well-log data in this study to discriminate distinct lithologies and fluid types, as well as uncertainty analysis. Various diagnostic models, such as the contact cement model, constant cement model and friable sand model, were used for this purpose. The cementation of reservoir sand ranged from 1% to more than 4%, according to the analysis. The gas sand reservoir, cap shale and brine sand were categorized using a rock physics template (RPT) model built over VP/VS against the AI cross-plot. Gas saturation was appropriately indicated by the RPT model produced from the shallow marine environment. The present study proves that RPM developed in the first part of the study may be used to perform uncertainty analysis using MCS. We simulated three different lithologies in this study, viz. shale, brine sand and gas sand, and then categorized them using VP/VS versus P-impedance cross-plot.