Characterization and methane emissions potential of non-producing oil and gas wells in Colombia and Argentina
Bibliographic record
Abstract
Non-producing oil and gas wells can pose a significant risk to the environment and human health, and contribute to climate change by emitting methane, a potent greenhouse gas. However, the characterization, distribution, and methane emission profile of non-producing wells remain highly uncertain across the world. Here, we present a database analysis of non-producing oil and gas wells across Colombia and Argentina, which are countries with a long history of oil and gas development. By comparing well data from governmental and proprietary databases, we find more than 26,000 oil and gas wells in Colombia, of which about 6,000 (23%) are non-producing and more than 84,000 oil and gas wells in Argentina, of which approximately 51,000 (61%) are non-producing. Using these numbers, we estimate methane emissions from non-producing wells in Colombia and Argentina. In addition, we analyze well attributes such as well depth, well type (e.g., oil and gas), location, and well age and perform a spatial analysis to identify the regions/wells in Colombia and Argentina for field measurements. We find that the provinces of Santa Cruz, Chubut and Neuquén in Argentina, and the departments of Meta, Santander and Casanare in Colombia have the highest number of non-producing wells. Overall, these findings can be used to improve the characterization of existing oil and gas wells and to select representative samples of non-producing wells for methane emission monitoring in Colombia and Argentina.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".