Pipe Defect Recognition Using Foundation Model Segmentation
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Solution Overview
Problem
Current methods for defect recognition in hydrocarbon recovery pipes rely heavily on manual expert analysis, which is time-consuming, costly, and prone to human error, and existing deep learning approaches are cumbersome and struggle to generalize across diverse geological formations and environmental conditions.
Innovation Solution
The use of a Large Foundation Model to automate defect recognition in pipes by receiving data, preparing visual representations, placing positive and negative data points, and producing masks to identify defects, thereby reducing the need for manual expert analysis and improving efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert analysis is used for defect recognition, then measurement precision is maintained, but productivity is reduced and loss of time increases
Solution Approach 1:
A Large Foundation Model is introduced as an intermediary between the raw pipe inspection data and the final defect recognition output. The model processes visual representations of pipe data with positive and negative data points, producing masks that identify defects. This intermediary system maintains measurement precision while dramatically improving productivity by automating the analysis process.
2Productivity
If conventional deep learning models are used, then productivity is improved, but adaptability deteriorates due to difficulty in generalizing across diverse geological formations and environmental conditions
Solution Approach 1:
The Large Foundation Model is designed with universal adaptability to handle diverse geological formations and environmental conditions. The model accepts visual representations of pipe data with annotated positive and negative data points across various conditions, producing accurate defect masks regardless of the specific geological or environmental context. This multi-functional capability allows the same model to generalize across different scenarios without requiring separate specialized models.
3Measurement precision
If extensive annotation by domain experts is performed, then measurement precision is improved, but loss of time and device complexity increase
Solution Approach 1:
The system performs preliminary annotation by placing positive and negative data points on visual representations of pipe data before feeding them to the Large Foundation Model. This preliminary action prepares the data in advance, allowing the model to process it efficiently and produce accurate defect masks without requiring extensive real-time expert annotation during the analysis phase.
4Measurement precision
If conventional corrosion mapping tools are used, then measurement precision is maintained, but ease of operation deteriorates due to heavy reliance on manual defect picking
Solution Approach 1:
The Large Foundation Model performs self-service by automatically analyzing visual representations of pipe data and producing defect masks without requiring manual defect picking. The system takes visual data with positive and negative data points as input and autonomously generates accurate defect identification, eliminating the need for operators to manually interpret and pick defects while maintaining measurement precision.
Data Source
AI summary
Embodiments presented provide for a method for defect recognition for pipes used in hydrocarbon recovery operations. In embodiments, a large foundation model is used to help automatically detect and characterize the defects, eliminating the need for expert analysis.