Integration of image recognition and expert system for real-time wellbore stability analysis

Authors

  • Yongdong Fan College of Artificial Intelligence, China University of Petroleum, Beijing 102249, P. R. China
  • Huiwen Pang College of Science, China University of Petroleum, Beijing 102249, P. R. China
  • Yan Jin* State Key Laboratomy of Petroleum Resources and Prospecting, China Umiversity of Petroleum, Beiiing 102249, P. R. China (Email: jiny@cup.edu.cn)
  • Han Meng College of Artificial Intelligence, China University of Petroleum, Beijing 102249, P. R. China
  • Yunhu Lu State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, P. R. China
  • Shiming Wei State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, P. R. China
  • Hanqing Wang Resource Geophysics Academy, Imperial College London, London SW7 2BP, United Kingdom

Keywords:

Image recognition, wellbore stability, expert system, oil and gas extraction, drilling cuttings

Abstract

Wellbore stability is a key factor affecting safe and efficient drilling. At present, it is difficult to conduct real-time and accurate analysis of wellbore stability in related research. To address the current research shortcomings, this study proposes a real-time analysis model of wellbore stability integrating image recognition and an expert system, which mainly includes caving image segmentation and recognition, and a wellbore stability expert system. The caving image recognition proposes a new dynamic threshold segmentation method based on simple linear iterative clustering superpixel segmentation and visual geometry group 19-layer image classification. After completing the segmentation of the caving image, the geometric features of the caving are calculated, and the multi-source feature fusion GoogleNet model is established by integrating the geometric features with the convolution features extracted by GoogleNet to identify the caving types efficiently. After segmentation and recognition of caving images. The wellbore stability expert system uses the caving features to establish an expert system model to determine the mechanism of wellbore instability and provide reasonable solutions. Finally, the wellbore stability integrating image recognition and an expert system model was applied to a well in field production, accurately determining the mechanism of wellbore instability in real time and effectively solving the corresponding wellbore instability problem based on the measures provided by the model.

Document Type: Original article

Cited as: Fan, Y., Pang, H., Jin, Y., Meng, H., Lu, Y., Wei, S., Wang, H. Integration of image recognition and expert system for real-time wellbore stability analysis. Advances in Geo-Energy Research, 2025, 15(2): 158-171. https://doi.org/10.46690/ager.2025.02.07

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Published

2025-01-12

Issue

Vol. 15 No. 2 (2025): February

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Articles

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