Geological Insights from Porosity Analysis for Sustainable Development of Santos Basin’s Presalt Carbonate Reservoir

Richard Guillermo Vásconez Garcia (), SeyedMehdi Mohammadizadeh (), Michelle Chaves Kuroda Avansi, Giorgio Basilici, Leticia da Silva Bomfim, Oton Rubio Cunha, Marcus Vinícius Theodoro Soares, Áquila Ferreira Mesquita, Seyed Kourosh Mahjour () and Alexandre Campane Vidal
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Richard Guillermo Vásconez Garcia: Center for Energy and Petroleum Studies (CEPETRO), State University of Campinas (UNICAMP), Campinas 13083-896, SP, Brazil
SeyedMehdi Mohammadizadeh: Department of Water Resources (DRH), College of Civil Engineering, Architecture and Urban Design (FECFAU), State University of Campinas (UNICAMP), Campinas 13083-852, SP, Brazil
Michelle Chaves Kuroda Avansi: Center for Energy and Petroleum Studies (CEPETRO), State University of Campinas (UNICAMP), Campinas 13083-896, SP, Brazil
Giorgio Basilici: Department of Geology and Natural Resources, Geosciences Institute (IG), State University of Campinas (UNICAMP), Campinas 13083-855, SP, Brazil
Leticia da Silva Bomfim: Center for Energy and Petroleum Studies (CEPETRO), State University of Campinas (UNICAMP), Campinas 13083-896, SP, Brazil
Oton Rubio Cunha: Center for Energy and Petroleum Studies (CEPETRO), State University of Campinas (UNICAMP), Campinas 13083-896, SP, Brazil
Marcus Vinícius Theodoro Soares: Center for Energy and Petroleum Studies (CEPETRO), State University of Campinas (UNICAMP), Campinas 13083-896, SP, Brazil
Áquila Ferreira Mesquita: Center for Energy and Petroleum Studies (CEPETRO), State University of Campinas (UNICAMP), Campinas 13083-896, SP, Brazil
Seyed Kourosh Mahjour: Texas Institute for Applied Environment Research, Tarleton State University, Stephenville, TX 76401, USA
Alexandre Campane Vidal: Center for Energy and Petroleum Studies (CEPETRO), State University of Campinas (UNICAMP), Campinas 13083-896, SP, Brazil

Sustainability, 2024, vol. 16, issue 13, 1-35

Abstract: Carbonate reservoirs, influenced by depositional and diagenetic processes and characterized by features like faults and vugs that impact storage capacity, require more than traditional Borehole Imaging logs ( B H I s ) for accurate porosity data. These data are essential for geological assessments, production forecasting, and reservoir simulations. This work aims to address this limitation by developing methods to measure and monitor the sustainability of carbonate reservoirs and exploring the application of sustainability principles to their management. The study integrates B H I s and conventional logs from two wells to classify porosity-based facies within the Barra Velha Formation ( B V F ) in the Santos Basin. The methodology involves four steps: (i) analyzing conventional logs; (ii) segmenting BHI logs; (iii) integrating conventional and segmented BHI logs using Self-Organizing Maps ( S O M ); and (iv) interpreting the resulting classes. Matrix porosity values and non-matrix pore sizes categorize the porosity into four facies: ( A to D ). The results of this research indicate the following: Facies A has high non-matrix porosity with 14,560 small megapores, 5419 large megapores, and 271 gigapores (71.9%, 26.76%, and 1.34% of the 20,250 pores, respectively). Facies B shows moderate non-matrix porosity with 8,669 small megapores, 2642 large megapores, and 33 gigapores (76.42%, 23.29%, and 0.29% of the 11,344 pores, respectively) and medium matrix porosity. Facies C exhibits low non-matrix porosity with 7749 small megapores, 2132 large megapores, and 20 gigapores (78.27%, 21.53%, and 0.20% of the 9901 pores, respectively) and medium matrix porosity. Facies D has low non-matrix porosity with 9355 small megapores, 2346 large megapores, and 19 gigapores (79.82%, 20.02%, and 0.16% of the 11,720 pores, respectively) and low matrix porosity. The results of this research reveal the effectiveness of a semiautomatic methodology that combines B H I and conventional well logs to distinguish between matrix and non-matrix-related pore spaces, thus enabling a preliminary classification of reservoir facies based on porosity. This study advances our understanding of carbonate reservoir sustainability and heterogeneity, thus offering valuable insights for robust, sustainable reservoir characterization and management in the context of global environmental and geological changes. The novelty of this work lies in integrating data from two sources to classify porosity across the presalt reservoir interval, thus serving as a proxy for preliminary lithofacies identification without core data.

Keywords: porosity-based facies; borehole image logs; machine learning; sustainability; dataset integration; presalt carbonate reservoir; carbonate petrophysics; carbon capture storage; sustainable geological resources (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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