Semi-supervised learning framework for oil and gas pipeline failure detection.

Area of Science:

• Engineering
• Data Science
• Petroleum Engineering

Background:

• Real-time quantification of oil and gas pipeline failures is crucial for effective response planning.
• Supervised machine learning models struggle with incomplete historical incident data, hindering accurate failure assessment.
• Existing literature lacks robust methods for utilizing incomplete databases in pipeline failure analysis.

Purpose of the Study:

• To develop a novel semi-supervised machine learning framework for analyzing incomplete oil and gas pipeline failure databases.
• To address the challenge of limited and missing information in historical incident reports for predictive modeling.
• To enable more accurate and timely pipeline failure assessments.

Main Methods:

• A cluster-impute-classify (CIC) approach is proposed to mine existing failure databases.
• The CIC method reconstructs missing information in incident reports by mapping relevant data subsets.
• An ensemble learning architecture trains a classifier on-the-fly using diverse features.

Main Results:

• The proposed framework achieves up to 91% detection accuracy for pipeline failures.
• The model demonstrates stable generalization ability even with a high rate of missing information.
• The approach is scalable to various pipeline failure datasets.

Conclusions:

• The semi-supervised CIC framework effectively handles incomplete data for pipeline failure assessment.
• This data-driven approach enhances the reliability of predictive models in the oil and gas industry.
• The method offers a scalable solution for improving pipeline safety and operational efficiency.