Importance of geological and rock physics prior information for lithology and pore fluid estimation from inverted seismic data: Exploration in a turbidite reservoir case study

摘要

Using inverted seismic data from a turbidite depositional-environment, we show that accounting only for rock types sampled by wells, can lead to biased predictions of the reservoir's fluids. As it is common in an exploration setting, information from a single well (well logs and petrological analysis) was used to define a set of initial facies that combine lithology and fluids in a single reservoir property. Based on our understanding of the depositional environment, we augmented our model with expected lithofacies and associated elastic properties, which were not sampled by the well (here different types/proportions of sand-shale mixtures). Given a-geologically consistent, spatially variant, prior probability of facies occurrence, Bayesian estimation of each facies probability was computed at every sample of the inverted seismic data. In this study, we used deterministic seismic inversion to produce the input data for our analysis, which is customary in similar field studies. Accounting for the augmented geological prior we were able to generate a scenario consistent with all available data, which supports further development of the field. In contrast, the purely data driven Bayesian classification (well log and seismic) would lead to downgrading of the prospectivity of the field in our case. Based on our findings, we argue that lack of data in Quantitative Interpretation needs to be counterweighted by robust geological prior information In order to risk geological scenarios without bias in exploration settings. In this work, using inverted seismic data from a turbidite depositional environment, we show that accounting only for rock types sampled at the wells can lead to biased predictions of the reservoir fluids. We propose two critical improvements on the purely data-driven approach: first we extend the rock physics model with facies expected in the depositional environment but not sampled by wells, and second we impose a spatially variant prior probability density of lithologies and fluids. Accounting for the augmented geological prior in this way, we were able to generate a scenario consistent with all available data that supports further development of the field. In contrast, the purely data-driven Bayesian classification (well log and, seismic) would lead to downgrading the prospectivity of this field.