Sensor Data Analytics for Optimized Methane Leak Detection and Mitigation

Gaurav Kumar Sinha

Abstract: Emissions from methane escaping oil and gas setups significantly contribute to the overall greenhouse gas problem. It is crucial, for both the health of our environment and financial savings, to spot and deal with these emissions quickly and affordably. This study dives into how analyzing data from sensors can enhance the efficiency of finding and fixing methane leaks. By employing state - of - the - art techniques like machine learning, geographical analysis, and forecasting models on sensor - generated data, it's possible to identify infrastructure at high - risk of leaks, precisely locate where leaks are occurring, and calculate the volume of leaked methane. The document presents a strategic method for the allocation of resources to detect leaks and sequence repair work more effectively. Through a practical example, it's shown that applying these advanced sensor data techniques can lead to a reduction of methane emissions by 10 - 20% over traditional methods. This insight offers a valuable strategy for petroleum and natural gas firms to lessen their methane output cost - efficiently. If these advanced sensor data analysis methods were to be widely adopted, they would make a significant dent in the worldwide effort to lower emissions of greenhouse gases.

Keywords: Methane leaks, oil and gas infrastructure, greenhouse gas emissions, leak detection, sensor data, data analytics, machine learning, infrared cameras, drones, methane sensors, ambient methane detectors, sniffers, spatial mapping, predictive modeling, decision making framework, leak prevention, emissions reduction