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<p>The dissolution features in carbonate rocks are common and highly variable; ranging from millimetric pores to enlarged conduits, open fractures, and even caves. Identifying these features is essential in multiple contexts, particularly in the oil industry, as they significantly impact fluid flow capacity and reservoir performance. Furthermore, their identification helps drilling campaigns predict and mitigate zones with high losses of drilling mud. In this context, we propose a novel methodology for identifying dissolution features in carbonate rocks using BHI and CNN models. For this task, seven wells with acoustic BHI from carbonates from Barra Velha Formation located at Santos Basin, Brazil, were used. The proposed workflow includes: (i) defining the display image scale and cut height size (CHS); (ii) class definition and dataset labeling; (iii) CNN model training; and finally (iv) applying the best CNN model to a blind well to check the performance in an untrained dataset. The display resolution of 1:10 was defined and four CHS scenarios were tested: 10 cm (S1), 40 cm (S2), 70 cm (S3) and 100 cm (S4). Six classes were defined based on the type and intensity of dissolution, encompassing a range from non-vuggy rock to facies dominated by vuggy matrix porosity, fracture related dissolution, and large scale conduits or caves. Four CNN models were tested to predict dissolution features: ResNet, RegNet, ShuffleNet, and MobileNet. For scenarios S1 to S3, the ResNet model achieved the best performance, with accuracy values around 0.90 and F1-macro of 0.80. In S4, MobileNet was the best model, with accuracy values close to 0.70 and F1-macro of 0.58. Finally, a qualitative analysis was made using the blind well to evaluate the performance of each model in an untrained/unseen dataset. Although CNN performance decreases with increasing CHS, the results indicate that a multi-scale approach combining 10 cm and 70 cm sample heights provides a robust workflow for capturing both fine-scale dissolution textures and larger-scale geological trends along the wellbore.</p>

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The use of convolutional neural networks to predict carbonate dissolution features in acoustic borehole images: an example from brazilian pre-salt reservoirs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月21日

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  • Guilherme Furlan Chinelatto,
  • João Paulo da Ponte Souza,
  • Mateus Basso,
  • Alexandre Campane Vidal

摘要

The dissolution features in carbonate rocks are common and highly variable; ranging from millimetric pores to enlarged conduits, open fractures, and even caves. Identifying these features is essential in multiple contexts, particularly in the oil industry, as they significantly impact fluid flow capacity and reservoir performance. Furthermore, their identification helps drilling campaigns predict and mitigate zones with high losses of drilling mud. In this context, we propose a novel methodology for identifying dissolution features in carbonate rocks using BHI and CNN models. For this task, seven wells with acoustic BHI from carbonates from Barra Velha Formation located at Santos Basin, Brazil, were used. The proposed workflow includes: (i) defining the display image scale and cut height size (CHS); (ii) class definition and dataset labeling; (iii) CNN model training; and finally (iv) applying the best CNN model to a blind well to check the performance in an untrained dataset. The display resolution of 1:10 was defined and four CHS scenarios were tested: 10 cm (S1), 40 cm (S2), 70 cm (S3) and 100 cm (S4). Six classes were defined based on the type and intensity of dissolution, encompassing a range from non-vuggy rock to facies dominated by vuggy matrix porosity, fracture related dissolution, and large scale conduits or caves. Four CNN models were tested to predict dissolution features: ResNet, RegNet, ShuffleNet, and MobileNet. For scenarios S1 to S3, the ResNet model achieved the best performance, with accuracy values around 0.90 and F1-macro of 0.80. In S4, MobileNet was the best model, with accuracy values close to 0.70 and F1-macro of 0.58. Finally, a qualitative analysis was made using the blind well to evaluate the performance of each model in an untrained/unseen dataset. Although CNN performance decreases with increasing CHS, the results indicate that a multi-scale approach combining 10 cm and 70 cm sample heights provides a robust workflow for capturing both fine-scale dissolution textures and larger-scale geological trends along the wellbore.