Geosystems risk and uncertainty: The application of ChatGPT with targeted prompting

Mahjour, Seyed Kourosh; Soltanmohammadi, Ramin; Heidaryan, Ehsan; Faroughi, Salah A.

doi:10.1016/j.geoen.2024.212889

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Numerous studies underscore the transformative impact of ML techniques across diverse domains, with particular relevance to geosciences, as exemplified in works by [57,58]. Notably, recent developments emphasize novel methodologies tailored for big data analysis, leveraging deep learning algorithms, as evidenced by the works of [59].…”

Section: Advancements In Machine Learning Techniques For Geological A...mentioning

confidence: 99%

Geological Insights from Porosity Analysis for Sustainable Development of Santos Basin’s Pre-Salt Carbonate Reservoir

Vásconez Garcia,

Mohammadizadeh,

Avansi

et al. 2024

Preprint

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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 (BHI) for accurate porosity data. This data is 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 BHI and conventional logs from two wells to classify porosity-based facies within the Barra Velha Formation (BVF) 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 (SOM); and (iv) interpreting the resulting classes. Matrix porosity values and non-matrix pore sizes categorize the porosity into four facies (A to D). Facies A has high non-matrix porosity with 14,560 small megapores, 5,419 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, 2,642 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 7,749 small megapores, 2,132 large megapores, and 20 gigapores (78.27%, 21.53%, and 0.20% of the 9,901 pores, respectively) and medium matrix porosity. Facies D has low non-matrix porosity with 9,355 small megapores, 2,346 large megapores, and 19 gigapores (79.82%, 20.02%, and 0.16% of the 11,720 pores, respectively) and low matrix porosity. The novelty of this work lies in integrating data from two sources to classify porosity across the Pre-Salt reservoir interval, serving as a proxy for preliminary litho-facies identification without core data. This study advances our understanding of carbonate reservoir sustainability and heterogeneity, offering valuable insights for robust, sustainable reservoir characterization and management in the context of global environmental and geological changes.

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Section: Advancements In Machine Learning Techniques For Geological A...mentioning

confidence: 99%

Geological Insights from Porosity Analysis for Sustainable Development of Santos Basin’s Pre-Salt Carbonate Reservoir

Vásconez Garcia,

Mohammadizadeh,

Avansi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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

Vásconez Garcia,

Mohammadizadeh,

Avansi

et al. 2024

Sustainability

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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 (BHIs) 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 BHIs and conventional logs from two wells to classify porosity-based facies within the Barra Velha Formation (BVF) 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 (SOM); 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 BHI 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.

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Geosystems risk and uncertainty: The application of ChatGPT with targeted prompting

Cited by 2 publications

References 30 publications

Geological Insights from Porosity Analysis for Sustainable Development of Santos Basin’s Pre-Salt Carbonate Reservoir

Geological Insights from Porosity Analysis for Sustainable Development of Santos Basin’s Pre-Salt Carbonate Reservoir

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

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