Current MMRV solutions have the potential to quickly survey entire oilfields or detect methane leaks down to the component level, but also carry high price tags or, indirectly, high implementation costs. The Stanford/EDF Mobile Monitoring Challenge (MMC) conducted in 2018 was the first study to systematically evaluate methane mitigation technologies for incorporation into LDAR programs at the operator level. Three vehicle-based solutions tested in the MMC utilized a fence-line screening pattern that encompassed a production site and equipment, which we refer to as a “drive-around survey,” and showed promising results of greater than or equal to 88% true positive source identification rates for controlled releases in the 0-26 kg CH4/hr range. In this work, we evaluate a similar on-site drive-around survey as an alternative methane leak detection method under the EPA’s recent update to the Standards of Performance for New, Reconstructed, and Modified Sources and Emissions: Oil and Natural Gas Sector (NSPS). We find that a simple methane enhancement threshold binary classification system performs well with true positive rates > 0.8, though the precision of this classifier is inversely related to the magnitude of the emission rates for each class. We also describe a heuristic approach to estimating dispersion without source distance information. Incorporating this information into a linear model of emission rates regressed on survey data, we improve the model fit to R^2 > 0.9.