Estratigrafia de alta frequência e modelagem de reservatório a partir de modelos virtuais de afloramento, exemplo da formação Pedra Pintada, Bacia do Camaquã

Abstract

Fluvial aeolian deposits correspond to excellent water, oil and gas reservoirs. Several studies have shown that these types of deposits present extremely complex depositional architecture generating heterogeneities in reservoirs at different scales, from microscopic to reservoir scale. Traditionally, the oil industry performs its stratigraphic studies based on subsurface data such as seismic lines and well profiles. However, the high cost and the large spacing between the data, as well as the low resolution of the seismic data, make the geological characterization of the subsurface rocks difficult. The use of analogous outcrops from surface data aims to solve this problem because, through them, it’s possible to improve the understanding of the reservoir architecture, geometry and facies distribution of interest. The analogous outcrop studies can be realized from computational models (Virtual Outcrop Models – VOM). This method allows the reconstruction of the rocks in a digital platform, making possible their perspective analysis. Aiming to progress at this methodology and help in this context, the goal of this works is to analyze the heterogeneities of the rocks deposited in fluvial aeolian systems by using high-resolution sequence stratigraphy and using virtual outcrop models applied to the Pedra Pintada Formation, having as product a geocellular model that serves as an analog for hydrocarbon and water reservoirs. Through this work nine lithofacies, three lithofacies associations, seven photofacies, and four photofacies associations were delimited characterizing the stratigraphic architecture and the depositional model that allowed the characterization of heterogeneity with gigascopic, megascopic, macroscopic and mesoscopic scales present in the study área. From the photofacies/lithofacies associations, we obtained geocellular volumes that represent the geometry of the deposits and allow the representation of the internal variations of a reservoir in fluvial-eolian systems, thus giving us, a predictive character usable in reservoir exploration.