Acoustic image log facies and well log petrophysical evaluation of the Barra Velha Formation carbonate reservoir from the Santos Basin, offshore Brazil

Basso, Mateus; Souza, João Paulo Ponte; Honório, Bruno César Zanardo; Melani, Leandro Hartleben; Chinelatto, Guilherme Furlan; Belila, Aline Maria Poças; Vidal, Alexandre Campane

doi:10.1007/s13146-022-00791-4

Cited by 11 publications

(15 citation statements)

References 34 publications

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“…On the other hand, borehole image logs (BH I) have gained widespread utilization for several purposes, including detecting sub-seismic features like fractures and vugs, predicting porosities ( [14]), establishing connections between acoustic facies (derived from BH I) and petrophysical parameters ( [15]), and interpreting acoustic facies in the context of depositional settings ( [16]).…”

Section: Of 35mentioning

confidence: 99%

“…[69] utilized plug samples and N MR techniques to categorize groups with analogous petrophysical behaviors, facilitating a qualitative differentiation of the Barra Velha reservoir into categories of tight, good, and excellent. [15], employing a holistic approach integrating borehole image logs, nuclear magnetic resonance, and core samples, conducted a meticulous acoustic facies classification, subsequently establishing correlations between these facies and specific lithologies. It is noteworthy that the aforementioned studies share a common reliance on specific databases, primarily comprised of cores or plugs, enabling a robust analysis of lithological aspects within the reservoir.…”

Section: Porosity-based Facies As Lithology Proxiesmentioning

confidence: 99%

“…However, the prevalent absence of cores and plug samples in most reservoirs poses a common challenge, impeding accurate lithological interpretations. In response to this challenge, recent noteworthy endeavors by [15,70], and [16] have involved the interpretation of BH I logs to identify acoustic facies. Subsequently, these acoustic facies have been associated with the prevailing lithologies, incorporating crucial petrophysical parameters such as porosity and permeability into their analyses.…”

Section: Porosity-based Facies As Lithology Proxiesmentioning

confidence: 99%

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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: Of 35mentioning

confidence: 99%

Section: Porosity-based Facies As Lithology Proxiesmentioning

confidence: 99%

Section: Porosity-based Facies As Lithology Proxiesmentioning

confidence: 99%

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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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“…The visual characteristics of electrical imaging logging can provide a solution by compensating for these deficiencies and enhancing the accuracy of coal texture classification. The national standard GB/T 30050-2013 classifies coal textures into four categories: undeformed coal, cataclastic coal, granulated coal, and mylonitized coal, with the latter two often grouped together as structural coal or replaced directly with granulated coal during coalbed methane exploration and development. − …”

Section: Establishment Of a Logging Interpretation Chart For Coal Tex...mentioning

confidence: 99%

“…The national standard GB/T 30050-2013 classifies coal textures into four categories: undeformed coal, cataclastic coal, granulated coal, and mylonitized coal, with the latter two often grouped together as structural coal or replaced directly with granulated coal during coalbed methane exploration and development. 17 − 21 …”

Section: Establishment Of a Logging Interpretation Chart For Coal Tex...mentioning

confidence: 99%

Prediction Method of Coal Texture Considering Longitudinal Resolution of Logging Curves and Its Application─Taking No. 15 Coal Seam in the Shouyang Block as an Example

et al. 2023

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The quantitative identification of the coal texture is of great importance as a crucial parameter for coalbed methane (CBM) reservoir evaluation. This study combined drilling core data, electrical imaging logging data, and four conventional logging data, namely, compensation density (DEN), natural γ (GR), deep lateral resistivity (RD), and acoustic time difference (AC), to achieve accurate inversion of coal texture in the Shouyang Block. Meanwhile, wavelet analysis and Fisher discriminant analysis were introduced to the inversion process to further improve the accuracy. Through the utilization of software packages, such as Matlab and SPSS, the establishment of the coal texture logging interpretation chart of the No. 15 coal seam in the Shouyang block was successfully realized. The outcome of this comprehensive study reveals that the coal texture logging interpretation chart is an effective tool for the identification and classification of each coal texture and gangue. Moreover, the validity and reliability of this method were tested and confirmed using wells CS-8 and CS-9 in the region, achieving an accuracy of 97.1 and 93.2%, respectively. This innovative method has significant prospects for predicting and evaluating the coal texture in the Shouyang Block, which can be further applied to other regions.

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Fast permeability estimation using NMR well logging data log-normal decomposition

Stefanelli,

Santos,

Vidal

2023

Geoenergy Science and Engineering

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Acoustic image log facies and well log petrophysical evaluation of the Barra Velha Formation carbonate reservoir from the Santos Basin, offshore Brazil

Cited by 11 publications

References 34 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

Prediction Method of Coal Texture Considering Longitudinal Resolution of Logging Curves and Its Application─Taking No. 15 Coal Seam in the Shouyang Block as an Example

Fast permeability estimation using NMR well logging data log-normal decomposition

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