UPSCALING METHODS FOR PORE SYSTEM MODELING: AN INTEGRATION OF MULTISCALE PORE SIZE AND FLOW ANALYSIS

• Grant number: 24/15599-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: February 01, 2025
• End date: June 30, 2028
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geology
• Principal Investigator: Alexandre Campane Vidal
• Grantee: Luis Augusto Antoniossi Mansini
• Host Institution: Centro de Estudos de Energia e Petróleo (CEPETRO). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Company: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Mecânica (FEM)
• Associated research grant: 17/15736-3 - Engineering Research Centre in Reservoir and Production Management, AP.PCPE

Abstract

Modeling the pore system of carbonate rocks is not a simple task. Unlike sandstones that have a "homogeneous" pore system, carbonates exhibit a large range of pore types and pore throats with distinct sizes and morphologies (CHOQUETTE and PRAY, 1970; LUCIA, 1999; LØNØY 2006). This occurs due to a large range of factors such as the origin of the carbonate grains, sedimentary processes and diagenetic evolution and also due to the structural evolution of the basin (SKALINSKI and KENTER, 2015). Furthermore, the mapping of porosity distribution is a challenge for reservoir characterization, due to carbonate heterogeneity in space and time. The Brazilian pre-salt carbonates of Barra Velha and Macabu Formations are unconventional lacustrine deposits with absence of recent analogue formations, in addition to not showing outcrops. Thus, many efforts have been made to characterize the correlation of sedimentary and diagenetic process with petrophysical characteristics (HERLINGER; ZAMBONATO; DE ROS et al., 2017; BELILA et al., 2020; GOMES et al., 2020) but only a few of them integrate a multiscale analysis including uncertainties. This project is focused on the upscaling methods to model the pore system of pre-salt carbonates integrating the porosity results in multi-scale. For this purpose and approach based on, well core samples, image interpretation from computed microtomography (micro-CT) of plug samples, conventional computed tomography (CT) of core samples and borehole images (BHI) will be applied. The methods will integrate workflows for pore type classification, segmentation and quantification in multiscale analysis (micro to macro) and the use of machine learning methods to improve the upscaling of the pore system. At the end, a small grid (50*50*5m) will be created to simulate the reservoir condition close to the selected well to characterize the fluid flow patterns in different geological situations, always taking into account the reservoir heterogeneity and lateral and vertical distribution of petrophysical properties. When available, the results will be compared with production data.