## 4 Prediction of the Orphan Wells Locations  
To locate orphan wells, several methods are employed, each tailored to different scenarios as shown in Table 3. These include a review of historical records (such as ownership, production, and land use data), conversations with residents, and observational studies (such as soil and bedrock analysis, mapping of anthropogenic features, and nearby water well histories). Ground-based geophysical methods, including ground-penetrating radar, electrical resistivity tomography, seismic refraction tomography, and magnetometry, are utilized alongside aerial surveys, such as drone-based magnetometry, LiDAR scanning for surface anomalies, and methane detection. Visual methods, like aerial photographic interpretation and excavation, are also used for direct verification of well locations. Additional techniques, such as electromagnetic surveys, pressure transient analysis, stress disturbance assessments, and unmanned aerial vehicles (UAVs) equipped with advanced sensors, further enhance the ability to accurately locate wells. Satellite surveys add a layer, enabling wide-area coverage and high-resolution monitoring of surface conditions associated with orphan wells. These methods vary in cost, accuracy, and applicability, requiring careful selection based on specific conditions [29; 30]. Therefore, this paper introduces a machine-learning method to predict the location of the orphan wells based on the available data and using a Random Forst regressor. The resulting predictions were visualized on a scatter plot overlaid on a U.S. map, with well depths represented by a color gradient as shown in Figure 6. This method provides a scalable and efficient tool for identifying regions at risk of methane emissions and environmental hazards due to undocumented orphan wells, enabling more targeted monitoring and remediation efforts. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Description** & **Limitations** & **References** \\ \hline Flux Chambers & Provides high precision for measuring methane emissions in small areas (\(<\)1 m\({}^{2}\)). & Not suitable for large-scale operations like oil and gas fields and cannot capture emissions from well infrastructure. & [22] \\ \hline Eddy-Flux Covariance & Measures diffuse methane emissions using high-precision sensors mounted on tall towers. & Limited in identifying specific emission sources, costly, time-consuming, and not easily scalable to a large number of wells. & [22] \\ \hline Optical Gas Imaging (OGI) & Handheld devices used to detect significant methane leaks in pipelines and oil and gas facilities. & Limited sensitivity, prone to false negatives, and detects only larger emissions (\(\geq\)10 kg CH\({}_{4}\)/h). & [27; 28] \\ \hline Open Path Laser & Utilized on satellites and aircraft for detecting large methane emissions from oil and gas infrastructure. & Low sensitivity, susceptible to false negatives, with detection thresholds of 200 kg CH\({}_{4}\)/h for satellites and 10 kg CH\({}_{4}\)/h for aircraft. & [27; 28] \\ \hline Hyperspectral Cameras & Deployed on satellites, aircraft, or handheld units to identify large methane emissions. & Prone to false negatives and primarily effective for detecting large-scale emissions. & [27; 28] \\ \hline Gaussian Plume Models & Estimates methane emissions by combining ambient methane measurements with meteorological data, often using well-established plume models. & Limited by assumptions of discrete atmospheric stability classes, less effective at short distances (\(<\)50 m) and under calm wind conditions. & [27; 28] \\ \hline Stationary Ground-Based & Conducts field measurements near orphan wells to assess methane emissions during venting operations, validated by direct flow and & Limited to specific locations and conditions, requiring validation through additional methods like UAS data. & [20] \\ Measurements & Unmanned Aircraft System (UAS) data. & like UAS data. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Methane emission measurement techniques.