Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data.

Pipeline risk assessment is crucial for pipeline safety management and operation. The aim of this study is to develop a comprehensive assessment model that accurately evaluates pipeline risks and ensures the safe and reliable operation of the pipeline system. The model is based on multisource spatial data and is primarily applicable to long-distance oil and gas pipelines that traverse complex geological conditions in mountainous areas. The research is conducted using the example of the Jinliwen natural gas pipeline in Zhejiang Province, China. By analyzing the geological data of the study area and the potential risks that the pipeline may encounter, a comprehensive risk assessment indicator system for the pipeline was developed using slope units to divide pipeline sections. The pipeline risk levels are classified using the K-means clustering-entropy weighted-random forest algorithm. The model is evaluated using accuracy (Acc), precision (Pre), recall (R), F1-score, and the ROC curve. The results show that the model has an accuracy of 0.917, a precision of 0.92, a recall of 0.916, an F1-score of 0.914, and an AUC (Area Under Curve) of 0.93, indicating its strong predictive capability. The risk assessment results demonstrate a strong consistency when compared with actual incident events. This indicates that the constructed model effectively reflects the influencing factors of pipeline risk, providing a basis for pipeline risk assessment and disaster prevention and mitigation efforts in similar regions.

Using the meteorological early warning model to improve the prediction accuracy of water damage geological disasters around pipelines in mountainous areas.

This paper focuses on the threat of water damage geological disasters brought by the complex terrain along the long-distance natural gas pipeline. The role of rainfall factors in the occurrence of such disasters has been fully considered, a meteorological early warning model for water damage geological disasters in mountainous areas based on slope units has been constructed to improve the prediction accuracy of such disasters and timely early warning and forecasting. An actual natural gas pipeline in a typical mountainous area of Zhejiang Province is taken as an example. The hydrology-curvature combined analysis method is chosen to divide the slope units, and the SHALSTAB model is used to fit the slope soil environment to calculate the stability level. Finally, the stability level is coupled with rainfall data to calculate the early warning index for water damage geological disasters in the study area. The results show that compared with the separate SHALSTAB model, the early warning results coupled with rainfall are more effective in predicting water damage geological disasters. The early warning results are compared with the actual disaster points, among the nine actual disaster points, most of the slope units around seven disaster points are in the state of needing early warning, the early warning accuracy rate reaches 77.8 %. The proposed early warning model can carry out targeted deployment in advance according to the divided slope units, and the prediction accuracy of geological disasters induced by heavy rainfall weather is significantly higher and more suitable for the actual location of the disaster point, which can provide a basis for accurate disaster prevention in the research area and areas with similar geological environments.