A Novel Three-Dimensional Reconstruction Technology for the Defect Inspection of Tubing and Casing

Author:

Huang Zhiqiang12ORCID,Bai Xiaoliang13,Yu Zhi34,Chen Zhen12,Feng Na34,Ai Yufeng34,Song Shigang5,Xue Lili5

Affiliation:

1. School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China

2. Oil and Gas Equipment Technology Sharing and Service Platform of Sichuan Province, Chengdu 610500, China

3. China National Quality Inspection and Testing Center of Oil Tubular Goods, Xi’an 710077, China

4. CNPC Tubular Goods Research Institute, Xi’an 710077, China

5. Xi’an Hypervision Technology Co., Ltd., Xi’an 710100, China

Abstract

The three-dimensional reconstruction of high-gloss/reflection and low-texture objects (e.g., oil casing threads) is a complex task. In this paper, we present a novel approach that combines convolutional neural networks (CNNs) and multi-layer perception (MLP) with traditional three-dimensional reconstruction methods, thereby enhancing the detection efficiency. Our method utilizes a dataset of 800 samples that includes a variety of thread defects to train a U-net-like model as a three-dimensional reconstructor. Then, an MLP model is proposed to improve the accuracy of the three-dimensional reconstructed thread profile to the level of three-coordinate measurements through a regression analysis. The experimental results demonstrate that the method can effectively detect the black-crested threads of oil casing threads and quantify their proportions in the entire sample for accurate quality assessment. The method is easy to operate and can detect black threads effectively, providing a powerful tool for oil companies to ensure exploration benefits.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering

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