Abstract
Fractures play a crucial role in discovering and developing petroleum reserves. However, traditional logging techniques face significant challenges in identifying fractures. To address such challenges, this article proposes a new method that combines conventional logging data with a small amount of marker data from cores and image logs to identify fracture development in reservoirs with high accuracy and fast operation. The proposed method is based on the improved YOLOv5, which offers a new idea for fracture identification. The fracture data from the carbonate rocks of the Sulige gas field in the Ordos Basin were used for training and validation. Finally, positive experimental outcomes were achieved, demonstrating the usefulness of the improved YOLOv5 algorithm in detecting fracture development in carbonate rocks.
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Data availability
The data supporting the reserach results can be obtained from China Petroleum Changqing Oilfield Exploration and Development Research Institute, but the availability of these data is limited. These data are used under the permission of the current research, so they are not disclosed. However, the author can provide data according to reasonable requirements and with the permission of China Petroleum Changqing Oilfield Exploration and Development Research Institute. If anyone wants to obtain data from this study, please contact the corresponding author Zhang Yuanpei.
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Acknowledgements
This work was supported by the Natural Science Foundation of Shandong Province (No. ZR2022MD033).
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This work was supported by the Natural Science Foundation of Shandong Province (No. ZR2022MD033).
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Author 1 (First Author): Investigation, Formal Analysis, Writing - Original Draft; Author 2: Conceptualization, Methodology, Software, Data Curation, Writing - Original Draft; Author 3: Visualization, Investigation; Author 4: Resources, Supervision; Author 5: Software, Validation Author 6: Visualization, Writing - Review & Editing
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Xie, J., Gao, R., Zhang, Y. et al. Carbonate Rock Fracture Identification Method Based on an Improved YOLOv5 Algorithm. Pure Appl. Geophys. 181, 189–201 (2024). https://doi.org/10.1007/s00024-023-03408-6
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DOI: https://doi.org/10.1007/s00024-023-03408-6