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Although the natural gas pipeline network is the most efficient and secure transportation mode for natural gas, it remains susceptible to external and internal risk factors. It is vital to address the associated risk factors such as corrosion, third-party interference, natural disasters, and equipment faults, which may lead to pipeline leakage or failure. The conventional quantitative risk assessment techniques require massive historical failure data that are sometimes unavailable or vague. Experts or researchers in the same field can always provide insights into the latest failure assessment picture. In this paper, fuzzy set theory is employed by obtaining expert elicitation through linguistic variables to obtain the failure probability of the top event (pipeline failure). By applying a combination of T- and S-Norms, the fuzzy aggregation approach can enable the most conservative risk failure assessment. The findings from this study showed that internal factors, including material faults and operational errors, significantly impact the pipeline failure integrity. Future directions should include sensitivity analyses to address the uncertainty in data to ensure the reliability of assessment results. 

Natural gas pipelines are susceptible to external and internal risk factors, such as corrosion, environmental conditions, external interferences, construction and design faults, and equipment failures. Bayesian Networks (BN) is a promising risk assessment approach widely used to evaluate these risk factors. One of BN's inherent limitations is its inability to accurately capture statistical dependencies and causal relationships, which can be overcome by incorporating expert elicitation into BN. To account for uncertainty and vagueness in assessing pipeline failure risks, fuzzy set theory (FST) can be combined with BN, commonly known as Fuzzy Bayesian Networks (FBN). This study developed an FBN framework that uses linguistic variables to calculate fuzzy probability (FPr) through domain expert elicitation, and crisp probabilities (CPr) are computed using historical incident data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). Based on the findings from the case study of the Midwest region of the USA, external factors, i.e., third-party interference, outside force, and other incidents, significantly impact pipeline performance and reliability. Diagnosis inference indicates that in the Midwest region of the USA, pipeline material and age are critical factors leading to corrosion failure by threatening pipeline integrity. The findings from this study suggested that a targeted risk mitigation strategy is paramount for minimizing the risks associated with pipeline networks. 

Pipeline networks are a crucial component of energy infrastructure, and natural force damage is an inevitable and unpredictable cause of pipeline failures. Such incidents can result in catastrophic losses, including harm to operators, communities, and the environment. Understanding the causes and impact of these failures is critical to preventing future incidents. This study investigates artificial intelligence (AI) algorithms to predict natural gas pipeline failures caused by natural forces, using climate change data that are incorporated into pipeline incident data. The AI algorithms were applied to the publicly available Pipeline and Hazardous Material Safety Administration (PHMSA) dataset from 2010 to 2022 for predicting future patterns. After data pre-processing and feature selection, the proposed model achieved a high prediction accuracy of 92.3% for natural gas pipeline damage caused by natural forces. The AI models can help identify high-risk pipelines and prioritize inspection and maintenance activities, leading to cost savings and improved safety. The predictive capabilities of the models can be leveraged by transportation agencies responsible for pipeline management to prevent pipeline damage, reduce environmental damage, and effectively allocate resources. This study highlights the potential of machine learning techniques in predicting pipeline damage caused by natural forces and underscores the need for further research to enhance our understanding of the complex interactions between climate change and pipeline infrastructure monitoring and maintenance. 

The oil and gas (O&G) sector is a critical energy infrastructure to a Nation’s welfare. As developed as the O&G industry may seem, its aging infrastructure gradually shows numerous challenges to keep up with the growing energy demand, increasing operation costs, and environmental concerns. A robust O&G infrastructure that is risk-free, reliable, and resilient towards expected or unexpected threats can offer an uninterrupted supply of O&G to downstream stakeholders, competitive prices to customers, and better environmental footprints. With the shift towards renewable energy, the notion of sustainable development should be firmly embedded in O&G infrastructure and operations to facilitate the smooth transition towards future renewable energy generation. This paper offers a comprehensive and innovative approach to achieving sustainable development for O&G infrastructure by examining it from a holistic risk, reliability, and resilience (3Rs) perspective. The role of each individual concept and their collective influence on sustainable development in the O&G industry will be thoroughly discussed. Moreover, this paper will highlight the significant impact of the holistic 3Rs approach on sustainable development and propose future research directions. Given the complexity of O&G infrastructure, it is crucial to incorporate sustainable development practices into every dimension of the O&G infrastructure, iteratively and continuously, to achieve the ultimate goal of long-term sustainability. This paper makes a significant contribution to the field by providing valuable insights and recommendations for achieving sustainable development in the O&G industry.