AI-BASED LEAK DETECTION, ENVIRONMENTAL RISK ASSESSMENT AND POLLUTION PREVENTION FOR OFFSHORE AND UNDERGROUND OIL & GAS PIPELINE SYSTEMS

Abstract

Offshore and underground pipelines are integral to the oil and gas sector, transporting hydrocarbons, but pipeline leaks and failures remain a challenge for the environment, economy and operations. Traditional leak detection techniques have delays in response time, limited predictability, and a high rate of false alarms, which decreases effectiveness and leads to damage to the environment. This research examines how Artificial Intelligence (AI) can be applied to improve the efficiency of leak detection and reduce environmental risks and pollution in offshore and underground oil and gas pipelines. Survey data from 228 industry professionals such as pipeline engineers, environmental specialists and operations managers were gathered and analyzed through a quantitative research approach. Descriptive statistics, reliability analysis, correlation analysis and multiple regression technique were used to analyze the data. The findings reveal that AI has a significant positive influence on leak detection efficiency (β = 0.781, p < 0.001), environmental risk assessment (β = 0.724, p < 0.001), and pollution prevention (β = 0.804, p < 0.001). The outcomes show that AI-powered solutions such as machine learning, predictive analytics, Internet of Things (IoT) sensors, digital twins, and intelligent monitoring systems significantly enhance pipeline integrity management and environmental sustainability. The study adds a step forward to the literature by providing an integrated framework by integrating operational performance and environmental protection objectives. The results offer some real-world advice for industry practitioners, policy makers and environmental regulators to improve pipeline safety, minimize environmental risks and promote sustainable management of energy infrastructure.

Keywords:

Artificial Intelligence (AI), Leak Detection Efficiency, Environmental Risk Assessment, Pollution Prevention, Oil and Gas Pipelines.