Abstract

Pipelines are vital infrastructure in the oil and gas industry, serving a crucial role in energy distribution. Damage to pipelines caused by Pipeline Hit Incidents (PHI) can result in significant material, environmental, and safety losses. This study aims to develop a fuzzy logic-based risk scoring model to assess the PHI risk level based on pipeline conditions and environmental factors within PT. X’s operational area. The fuzzy logic approach is applied due to its ability to handle data uncertainty and ambiguity, particularly for variables such as soil conditions, human activities, and extreme weather. The research methodology includes historical PHI incident analysis, field observations, and interviews with relevant stakeholders. Quantitative and qualitative data are processed using MATLAB software to build a scoring model that prioritizes risk levels for each pipeline segment. The model is validated using historical incident data to evaluate its accuracy and reliability. The results are expected to assist PT. X in identifying high-risk areas, improving decision-making effectiveness, and supporting a sustainable risk monitoring system. Therefore, this research contributes to advancing risk management theory and applying fuzzy logic in managing critical infrastructure within the energy sector.