Characterization and renewable energy potential of abandoned and orphaned oil and gas wells across Canada and the United States
Bibliographic record
Abstract
There are millions of abandoned and orphaned oil and gas wells across Canada and the United States. Abandoned wells no longer produce oil and/or gas, and thus, in general, operators are not financially incentivized to plug and remediate the wells and associated sites. As a result, there are many orphaned wells, which are a subset of abandoned wells that have no responsible party, leaving the financial responsibility to plug and remediate the wells to government agencies and the general public. Abandoned and orphaned wells can degrade ecosystems, contaminate water sources, impact human health, and emit air pollutants including methane, a potent greenhouse gas. However, the risks posed by abandoned and orphaned wells can be reduced by plugging and remediation. Converting abandoned and orphaned wells to solar, wind, or geothermal energy production could provide funding for mitigation, which is needed to repurpose the land, remove and restore existing infrastructures and plug the well, all of which provide environmental benefits and stimulate the economy. However, there is currently a shortfall of available information on abandoned and orphaned wells and their characteristics to mitigate their environmental impacts and determine how they could be converted for clean energy production.First, we analyzed oil and gas well data from state agencies across the United States to estimate the number of documented orphaned wells over time and evaluate their attributes. We found 81,857 documented orphaned wells as of September 2021 and 123,318 as of April 2022, representing 2% and 3%, respectively, of all estimated abandoned wells in the United States. Furthermore, we estimated annual methane emissions to average 0.016 MMt of methane for the 123,318 documented orphaned wells as of April 2022, corresponding to 5% of the total methane emissions estimated by the U.S. Environmental Protection Agency for all abandoned wells. For the attributes of the 81,857 documented orphaned wells as of September 2021, we find that although well type (i.e., oil vs gas) is available for 83% of the wells, about half (51%) of the available well type are reported as “unknown”, meaning that well type is only known for 41% of the 81,857 documented orphaned wells. Furthermore, only 49% and 16% of the documented orphaned wells as of September 2021 have information on depth and last production date, respectively. Our analysis revealed that documented orphaned wells require additional characterization and studies to constrain the uncertainties and optimize mitigation.Second, we analyzed maps of renewable energy potential and oil and gas well data across Canada and the United States to assess the nation-wide potential for wind, solar, and geothermal energy production at abandoned and orphaned well locations. We estimated the total number of abandoned and orphaned wells in Canada and the United States to be 3,746,078, of which 3% are orphaned and in need of government funding. We find that abandoned and orphaned wells differ in spatial patterns and renewable energy potential. We show large wind (> 400 W/m2) and solar (> 1,600 kWh/kWp) energy potential at more than one million abandoned and orphaned well sites. More than 90% of abandoned and orphaned wells with available depth are better suited for hydrothermal systems, yet enhanced geothermal is possible at up to 10% of the wells. Repurposing these wells can help fulfill national energy transition goals and emission reduction targets, while providing additional funding for mitigation.Overall, additional studies are required to further identify and characterize the millions of abandoned and orphaned wells that exist across Canada and the United States. Nonetheless, our identification and analysis of documented orphaned wells in the United States represent the first steps toward characterizing the full set of wells eligible to be plugged and remediated with the federal funding available via the Bipartisan Infrastructure Law in the United States. Furthermore, our assessment of the nation-wide potential for wind, solar, and geothermal energy production at abandoned and orphaned well locations can help fulfill and finance the U.S. and Canada’s energy transition goals – shifting away from polluting fossil fuel resources and providing funding for managing the legacy of the millions of non-producing wells across the two nations and the world
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".