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

The morphology and connectivity of subsurface fracture networks are critical factors controlling wellbore stability and hydraulic fracturing efficiency. Accurate characterization of the three-dimensional complexity of fractures holds significant importance for engineering safety and performance enhancement. A novel image segmentation model is established in this study. It enhances the iterative threshold method by incorporating simple linear iterative clustering superpixels, ResNet50, and a Gaussian mixture model. The model first divides complex computed tomography images into numerous superpixel images using simple linear iterative clustering superpixel segmentation. Subsequently, ResNet50 is employed to classify these superpixel images. Based on the classification results, the iterative threshold segmentation method is applied to segment different categories of superpixel images accordingly. Following preliminary image segmentation, Gaussian mixture module is used for denoising the segmented fracture images, resulting in high-precision segmented images. The two-dimensional segmented images are then reconstructed in three-dimensional space, and the three-dimensional distribution characteristics of fractures are analyzed. This study concludes that the new fracture segmentation method enables high-precision extraction of fracture regions. Compared with threshold segmentation, the morphological analysis noise value in the two-dimensional images segmented by the method proposed in this study was reduced from 0.21% to 0.08%. Fracture distribution in three-dimensional space is complex, and areas with larger fracture networks exhibit greater complexity in their three-dimensional distribution.

Coal seam casing deformation is a key engineering challenge restricting the efficient development of coalbed methane. Frequent casing deformation in complex tectonic stress zones seriously affects wellbore integrity and fracturing performance. This study takes the in situ stress characteristics of coal seams and the mechanism of casing deformation as the research objects, comprehensively employing theoretical analysis and numerical simulation methods to systematically carry out in situ stress evaluation, numerical simulation of casing deformation, and sensitivity analysis of formation dip angle. First, based on five indicators, including stress difference, stress ratio, maximum shear stress, lateral pressure coefficient, and failure proximity, a comprehensive risk index evaluation model was constructed, and the analytic hierarchy process was used to determine the weights of each indicator. Second, numerical simulations of casing deformation under different stress differences and rock Young’s modulus conditions were carried out, and the influence of formation dip angle variation on casing deformation was analyzed. The results show that in the in situ stress reversal zones, multiple indicators overlap, indicating an extremely high risk of casing deformation, with stress difference and stress ratio being the main controlling factors. For the formation block in this study, when the stress difference exceeds 15 MPa, the radial displacement of the casing increases significantly. In addition, when the formation dip angle exceeds 50°, casing deformation increases sharply, and the coupling effect of compression and shear significantly increases the risk of casing deformation. The above results have important guiding significance for casing deformation assessment in the target block.