Enhanced Pipeline Safety: Cloud-Based Leak Prediction Using XGBoost

The oil and gas industry face significant risks from pipeline leaks, which can result in explosions, environmental damage, and loss of life. This paper introduces a cloud-based explosion risk detection system utilizing flow and pressure sensors for real-time monitoring. Sensor data is transmitted to the cloud, where an advanced AI model, specifically the XGBoost algorithm, analyzes the data to detect leaks and assess risks. The XGBoost model, optimized through Bayesian Optimization adjusting parameters such as step size, max depth, and total estimators-achieved an accuracy score of 97.09<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and a cross-validation score of 97.01 %, demonstrating its robust performance. Trained on extensive historical leak patterns, the model predicts potential leak scenarios. Additionally, simulations model various leak scenarios to rigorously test and refine the system's responses. By leveraging cloud computing and XGBoost, the system offers scalable, real-time leak detection, accurately identifies leakage, and triggers early warning alarms. The system continuously monitors pipeline pressure and flow data in real-time, enhancing pipeline safety by enabling rapid detection and response to potential leaks. The integration of these technologies ensures a comprehensive, proactive risk management solution, significantly enhancing pipeline safety, optimizing operational efficiency, and minimizing environmental and human impacts while advancing industry standards.