Prestack inversion and multiattribute analysis for porosity, shale volume, and sand probability in the Havert Formation of the Goliat field, southwest Barents Sea

Abstract

An integrated innovative multidisciplinary approach has been used to estimate effective porosity (PHIE), shale volume (V sh ), and sand probability from prestack angle gathers and petrophysical well logs within the Lower Triassic Havert Formation in the Goliat field, Southwest Barents Sea. A rock-physics feasibility study revealed the optimum petrofacies discriminating ability of extended elastic impedance (EEI) tuned for PHIE and V sh . We then combined model-based prestack inversion outputs from a simultaneous inversion and an EEI inversion into a multilinear attribute regression analysis to estimate absolute V sh and PHIE seismic attributes. The quality of the V sh and PHIE prediction is shown to increase by integrating the EEI inversion in the workflow. Probability distribution functions and a priori petrofacies proportions extracted from the well data are then applied to the V sh and PHIE volumes to obtain clean and shaly sand probabilities. A tectonic-controlled point-source depositional model for the Havert Formation sands is then inferred from the extracted sand bodies and the seismic geomorphological character of the different attributes. IntroductionThe past two decades have seen a significant increase in the use of quantitative seismic interpretation methods to characterize the subsurface. Quantitative seismic interpretation techniques are increasingly being implemented into reservoir characterization schemes to minimize hydrocarbon exploitation risks. The measured seismic data respond to the contrasts in the effective elastic properties in the subsurface area of investigation. However, the main goal is usually aimed at characterizing the different lithofacies, porosity distribution, and fluid content responsible for the effective elastic response. These underlying properties are only indirectly measured using the available proxies in the seismic data, which are then transformed to the geologic variables of interest using rock physics.Direct hydrocarbon indicators (DHIs) from seismic data are not always successful because the seismic amplitude is a composite response of the saturated rock. Therefore, it is important to spend as much time characterizing the lithology response before fluid anomaly hunting. Reservoir quality assessment from seismic, well log, and laboratory data requires an integrated approach involving geology, petrophysics, rock physics,