Intelligent image mosaic and fusion technology of downhole pipe string based on local feature matching

Downhole TV imaging logging can directly monitor the anomalies of downhole string, but the acquired downhole string images have problems such as low texture, insufficient illumination and repetitive background. Traditional algorithms such as scale-invariant feature transform (SIFT) can hardly detect high-quality feature points stably, resulting in poor robustness of image mosaic and fusion. In this paper, according to the idea of local feature matching, inverse pixel mapping algorithm is used to unfold the string image into a planar diagram, and the radial error is precisely corrected. Then, the convolutional neural network is used to extract local features, and the attention mechanism is adopted to establish pixel-level matching at the coarse level. Finally, the optimal mosaic line and the smoothing function are introduced to eliminate the mosaic error, and thus the intelligent mosaic and fusion of the large-scale image of downhole string is realized. And the following research results are obtained. First, the intelligent image mosaic and fusion technology of downhole pipe string based on local feature matching achieves a stable mosaic and fusion of downhole string image through image preprocessing, feature matching and image fusion. Second, when the smoothing weight factor (k) indicating the intelligent image fusion quality is 0.05, the fusion effect is the best. The smaller the k value, the more obvious the image mosaic seam. If the k value is too high, double image can be formed easily in the overlap zone. Third, the error caused by angle tilt is eliminated by calculating the optimal mosaic line of the to-be-treated image, so as to realize stable intelligent mosaic and fusion. Fourth, compared with the SIFT algorithm, the number of feature points that can be detected by this method is increased by 74.6% on average, and the average accuracy rate of intelligent matching is increased from 83.9% to 98.8%. In conclusion, the number and accuracy rate of detected feature points by this method are significantly improved, and the structural similarity, peak signal-to-noise ratio, and mean squared error of the intelligent fusion image are better than those obtained by the traditional algorithm. The research results provide new ideas and technologies for solving the difficulties in downhole pipeline detection. © 2024 Natural Gas Industry Journal Agency. All rights reserved.