Multiscale integration for geological modeling

• Grant number: 20/01305-3
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: August 01, 2020
• End date: January 22, 2022
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geology
• Agreement: Equinor (former Statoil)
• Principal Investigator: Alexandre Campane Vidal
• Grantee: Amir Abbas Babasafari
• Host Institution: Centro de Estudos do 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

The brazilian pre-salt reservoir (Aptian) is composed of a set of carbonate rocks, whose peculiar genesis and lithofacies distribution has been intensely discussed. The main facies correspond to fascicular calcite crusts interspersed with grainstones and rudstones of reworked crust fragments and layers rich in magnesian silicates with calcite spherulites. The challenge for the generation of geological models for pre-salt reservoirs is related to the absence of analogous depositional environments and the difficulty of mapping the heterogeneities due to attenuation of the seismic signal by salt layer. The uncertainties regarding the reservoir faciological distribution produce petrophysical models inconsistent with the production data. Recognizing and reconciling different scales associated with data from different sources is an important aspect of reservoir modeling. Since properties are heterogeneously distributed in space, heterogeneity itself must be expanded so that the adjusted measurements correctly reflect the property on the coarser scale. This is often overlooked, with the traditional upscaling of properties only, not their heterogeneity. After more than a decade of exploration in the Santos Basin, the Lula Field, with estimated reserves of between 6.5 and 8.3 billion barrels of oil equivalent, accumulates more than 95 wells, 4000 side samples and 700 core, constituting the largest database of a pre-salt field. This large amount of data enables an innovative methodology for reservoir characterization using a data science approach such as geostatistics, machine learning and a multiscale approach considering the huge amount of data available. This postdoctoral project is part of a large research group of masters and doctorates students and the aim is to group and synthesize the data from the other projects. The challenge is to apply different methods to integrate the large amount of data and the different sources from facies and petrophysical characterization, karst structures, faults and fractures modeling and seismic interpretation. (AU)