Mitigation of Risks Associated with Gas Pipeline Failure by Using Quantitative Risk Management Approach: A Descriptive Study on Gas Industry

Thermal Characteristics of Fireproof Plaster Compositions in Exposure to Various Regimes of Fire

The problems of the fire safety of oil and gas facilities are particularly relevant due to the increasing complexity of technological processes and production. Experimental studies of steel structures with three different types of plasters are presented to determine the time taken to reach the critical temperature and loss of bearing capacity (R) of the sample, as a result of reaching a rate of deformation growth of more than 10 mm/min and the appearance of the ultimate vertical deformation. The simulation of the heating of steel structures showed a good correlation with the results of the experiment. The consumption of the plaster composition for the steel column was predicted, which allowed a 38% reduction in the consumption of fireproofing. It was found that to obtain the required fire resistance limit, it is necessary to consider the fire regime and apply plaster compositions with a thickness of 30–35 mm, depending on their thermal characteristics. The dependence of thermal conductivity and temperature on density is obtained, showing that the use of plaster compositions with a density of 200 to 600 kg/m3 is optimal to ensure a higher fire resistance limit. It is shown that the values of thermal conductivity of plaster compositions at 1000 °C are higher by 8–10% if the structure is exposed to a hydrocarbon fire regime. It is shown that the values of the heat capacity of plaster compositions at 1000 °C are higher by 10–15% if the structure is exposed to a standard fire regime.

Data-Driven Models for Forecasting Failure Modes in Oil and Gas Pipes

Oil and gas pipelines are lifelines for a country’s economic survival. As a result, they must be closely monitored to maximize their performance and avoid product losses in the transportation of petroleum products. However, they can collapse, resulting in dangerous repercussions, financial losses, and environmental consequences. Therefore, assessing the pipe condition and quality would be of great significance. Pipeline safety is ensured using a variety of inspection techniques, despite being time-consuming and expensive. To address these inefficiencies, this study develops a model that anticipates sources of failure in oil pipelines based on specific factors related to pipe diameter and age, service (transported product), facility type, and land use. The model is developed using a multilayer perceptron (MLP) neural network, radial basis function (RBF) neural network, and multinomial logistic (MNL) regression based on historical data from pipeline incidents. With an average validity of 84% for the MLP, 85% for the RBF, and 81% for the MNL, the models can forecast pipeline failures owing to corrosion and third-party activities. The developed model can help pipeline operators and decision makers detect different failure sources in pipelines and prioritize the required maintenance and replacement actions.