3D Wellbore Imaging Based on Point Cloud Conversion of Logging Data

To overcome limitations in spatial representation and imaging accuracy in traditional two-dimensional wellbore imaging, a 3D wellbore imaging method based on point cloud conversion and grid reconstruction of standardized logging data was proposed. First, a 3D coordinate-mapping model for logging data was constructed using a point-cloud conversion method. The radial measurement values and depth information were mapped to a 3D coordinate system, and the data structure was redefined to achieve standardized representation of the logging data. Then, the point cloud data were corrected and optimized by combining wellbore trajectory parameters. High-precision 3D imaging of the wellbore structure was achieved using spherical linear interpolation (SLERP) and gridding. Finally, by using the actual measurement data of Well X in the Ordos Basin as an example, imaging inversion verification and comparative experiments were conducted. The results indicate that the proposed method outperforms traditional methods in both 3D structure restoration accuracy and imaging efficiency. Compared with the conventional convex-hull and ball-pivoting algorithm (BPA) approaches, imaging time was reduced by 34.5% and 57.8%, respectively, and mesh completeness reached 98.6%, improvements of 6.9 and 27.9 percentage points, respectively. This method can more authentically reflect the wellbore morphology and its deformation characteristics, providing an efficient and reliable technical approach for the 3D imaging of complex wellbore structures and downhole condition assessment.