Uncovering Forgotten Oil and Gas Wells: How AI is Helping to Mitigate Environmental Risks
Summary
Across the United States, hundreds of thousands of undocumented orphaned oil and gas wells pose significant environmental and climate risks. These wells, often left unplugged and unmonitored, can leak toxic chemicals and greenhouse gases into the environment. Researchers have developed an AI model to identify these forgotten wells using historical topographic maps. This breakthrough could play a crucial role in mitigating environmental risks associated with leaking wells.
The Challenge of Undocumented Orphaned Wells
The United States is home to an estimated 800,000 undocumented orphaned wells (UOWs), which are not listed in official records and lack a responsible legal entity for their maintenance. Many of these wells, drilled since the mid-1800s, pose a risk as they may leak toxic chemicals and greenhouse gases, such as methane, into the environment.
AI Model Development
Researchers from the Lawrence Berkeley National Laboratory (LBNL) have developed an AI model capable of identifying forgotten oil and gas wells across the United States. The team trained a vision-language model known as U-Net on digitized maps of the U.S. from 1947 to 1992. These maps, aggregated and digitized by the U.S. Geological Survey, are consistent in their symbols and georeferencing, allowing for precise location identification of wellheads.
Implementation and Testing
The researchers trained their model on maps of Los Angeles and Kern counties in California, historically significant for oil and gas production. The model’s accuracy in identifying UOWs was tested through satellite imagery and on-site visits, showing a detection accuracy varying between 31% and 98%. The AI model demonstrated strong transferability by successfully identifying potential UOWs in Oklahoma’s Osage and Oklahoma counties, despite not being specifically trained on maps from those areas.
Verification Process
To verify the AI’s findings, researchers use a combination of satellite imagery, field surveys, and sensors like magnetometers. Once a potential well is identified, researchers look for any surface well structures. If there aren’t any, they walk in a grid or spiral pattern carrying a magnetometer, which measures magnetic fields. Buried metal well casings disturb the magnetic field, allowing researchers to home in on the well.
Future Prospects
This study is part of a Department of Energy initiative aimed at assisting states in locating UOWs. Researchers plan to refine the AI model further, expanding its application to additional regions and collaborating with state authorities interested in adopting this technology to manage environmental risks associated with orphaned wells.
Multi-Layered Approach
The AI mapping and verification effort is part of a much larger project to address UOWs: the Consortium Advancing Technology for Assessment of Lost Oil & Gas Wells (CATALOG). This program is led by Los Alamos National Laboratory and includes research teams from Berkeley Lab, Lawrence Livermore National Laboratory, the National Energy Technology Laboratory, and Sandia National Laboratories. CATALOG aims to improve ways to find wells, detect and measure methane, rapidly screen wells for their condition, unite information from different sources, and prioritize wells for plugging.
Drones and Sensors
Researchers are also investigating ways to scale up undocumented well detection and verification using drones equipped with different sensors. Preprogrammed with set fly routes, the drones can semi-autonomously survey a larger area than researchers could easily access on the ground. Groups are pursuing several different kinds of sensors, each with their own challenges and benefits.
Scaling Up
CATALOG’s efforts aim to scale these methods nationwide. By integrating diverse datasets and tools, the project seeks to minimize environmental harm from orphan wells, reducing methane emissions and protecting communities from the legacy of early oil and gas exploration.
Table: AI Model Performance
| Location | Detection Accuracy |
|---|---|
| Los Angeles County, CA | 31% - 98% |
| Kern County, CA | 31% - 98% |
| Osage County, OK | 31% - 98% |
| Oklahoma County, OK | 31% - 98% |
Table: Verification Process
| Step | Description |
|---|---|
| Satellite Imagery | Verify potential well locations using satellite images. |
| Field Surveys | Conduct on-site visits to confirm well existence. |
| Magnetometers | Use sensors to detect buried metal well casings. |
| GPS Coordinates | Record precise locations of confirmed wells. |
| Methane Emissions | Measure methane leaks to prioritize wells for plugging. |
Table: CATALOG Objectives
| Objective | Description |
|---|---|
| Find Wells | Improve methods to locate undocumented orphaned wells. |
| Detect Methane | Develop tools to detect and measure methane emissions. |
| Screen Wells | Rapidly assess well conditions to prioritize plugging. |
| Unite Information | Integrate data from various sources to enhance well management. |
| Prioritize Wells | Identify high-priority wells for plugging to minimize environmental harm. |
Conclusion
The use of AI to identify forgotten oil and gas wells is a significant step forward in mitigating environmental risks associated with leaking wells. By combining historical topographic maps with modern tools like drones and sensors, researchers can locate and assess these wells more efficiently. This multi-layered approach, part of the CATALOG initiative, aims to improve ways to find wells, detect and measure methane, and prioritize wells for plugging, ultimately reducing environmental harm and protecting communities.