Methane Source Identification in Gas Valve Wells Using Methane–Temperature Correlation

Methane leakage in gas valve wells poses significant safety hazards; however, existing monitoring technologies are susceptible to biogas interference, leading to high false alarm rates and insufficient leakage identification accuracy. Traditional methods rely on a single methane concentration threshold, which fails to distinguish between leakage methane and ambient biogas, resulting in low operational efficiency and potential risks. To address the safety issues of high false alarm rates and low leakage monitoring accuracy caused by biogas interference in gas valve wells, this study proposes a leakage tracing method based on the dynamic correlation between methane concentration and temperature. By integrating data from methane sensors and temperature sensors, and using quantitative analysis using the Spearman correlation coefficient and polarity rate, a methane source discrimination model is established. This method is ultimately applied to two sample instances of gas leakage and biogas production. The findings suggest that the Spearman correlation coefficient for the biogas production sample data is 0.97, indicating a high degree of correlation and falling within the determination interval of biogas. In contrast, the Spearman correlation coefficient for the gas leakage sample data is 0.58, suggesting a potential gas leakage. Both samples are correctly identified. The findings provide a quantifiable and cost-effective solution for safety monitoring in gas valve wells.