Dynamic risk assessment of natural gas transmission pipelines with LSTM networks and historical failure data

Abstract

In the realm of energy infrastructure, ensuring the security of gas transmission pipelines is critical. This research introduces an advanced dynamic risk assessment framework that leverages the predictive capabilities of LSTM networks, presenting an improvement over conventional failure prediction models. Unlike traditional approaches that rely on averaging historical failure records, this framework dynamically processes historical pipeline failure incidents into sequential time series analysis, facilitating and improving the accuracy of the current failure rate. The model refines the failure rate estimation for individual pipelines by incorporating unique characteristics and modification factors, resulting in a highly precise failure likelihood estimation. Additionally, this study introduces a quantifiable linkage between mortality risk and the fatality probit value across various accident scenarios, enhancing consequence evaluation. A sensitivity analysis is then performed to assess the impact of various input parameters on the model's performance. The practical application of the model on a U.S. pipeline confirms its effectiveness. This proposed methodology substantially enhances the understanding of incident causation in gas pipeline systems, paving the way for superior safety management strategies. It is instrumental in enhancing pipeline safety, refining infrastructure planning, and optimizing safety resource allocation. The methodology offers benefits for pipeline operators, industry professionals, and regulatory agencies, contributing to improved operational safety and resource management in the pipeline industry.