Oil producers or operators such as Shell, Petronas, Petron, Chevron, and Lukoil have always placed their equipment as the highest priority for operations. Still, the study shows that many failures in the facility associated with piping systems lead to billions of dollars’ loss. In the oil and gas industry, these piping systems are subjected to various failure mechanisms since it has been operated in various processes and harsh geographical environment. Most of the piping systems are susceptible to corrosion caused by several factors, as reported in the literature. Corrosions of the piping system weakened the piping strength as well as its fittings, thus reducing its ability to withstand the fluctuation of temperature and pressure generated towards the piping system. This work focussed on the factors that contribute to the life of the piping system based on the real-time risk inspection data that were obtained from PETRONAS facilities. The parameters considered were pressure, corrosion, wall thinning, age, nominal thickness, outer radius, and product type. The neural network model has been developed to predict the remaining useful life of piping based on the selected parameters. The proposed model showed promising results of R² value 0.99, which is close to 1.0, and the validation accuracy of a model was found 97.51% when compared with the actual data. The deterioration trends of individual factors considered in this study are generated to know the effect on pipe life conditions. This work may help oil and gas companies in determining the Fitness For service (FFS) of the piping system by estimating the life of the piping system affected by various corrosion phenomena.

Shell、Petronas、Petron、Chevron 和 Lukoil 等石油生产商或运营商一直将他们的设备作为运营的最高优先级。尽管如此，该研究表明，与管道系统相关的设施中的许多故障会导致数十亿美元的损失。在石油和天然气行业，这些管道系统在各种工艺和恶劣的地理环境中运行，因此会遭受各种故障机制。正如文献中所报道的，大多数管道系统都容易受到由多种因素引起的腐蚀的影响。管道系统的腐蚀削弱了管道的强度及其配件，从而降低了其承受管道系统产生的温度和压力波动的能力。这项工作基于从马石油设施获得的实时风险检查数据，重点关注影响管道系统寿命的因素。考虑的参数是压力、腐蚀、壁薄、年龄、公称厚度、外半径和产品类型。神经网络模型已被开发用于根据所选参数预测管道的剩余使用寿命。所提出的模型显示了 R 的有希望的结果²值 0.99，接近 1.0，与实际数据相比，模型的验证准确率为 97.51%。生成本研究中考虑的各个因素的恶化趋势，以了解对管道寿命条件的影响。这项工作可以帮助石油和天然气公司通过估计受各种腐蚀现象影响的管道系统的寿命来确定管道系统的适用性 (FFS)。