Progress Toward a First Measurement-Based Oil and Gas Sector Methane Inventory for Colombia
Bibliographic record
Abstract
Methane is a critical focus of international efforts to achieve short-term reductions in greenhouse gas emissions. Oil and gas and waste sector methane sources are understood to be the easiest and fastest to mitigate, but require robust measurement-based emissions inventory protocols to identify sources, to prioritize mitigation actions, and to track success or failure in reducing emissions. In particular, the complexity and variability of oil and gas sector sources necessitates combining measurements and data at different scales to accurately define the full distribution of emissions. This presentation describes the use of a hybrid, top-down / bottom-up inventory protocol to inform Colombia’s national methane inventories. The top-down campaign conducted between March and May 2024, comprised aerial LiDAR measurements at 3,826 oil and gas sector sites across 6 different production regions as well as measurements at three landfills. A separate bottom-up campaign is currently scheduled for the first quarter of 2025 and will include OGI surveys at approximately 320 sites, along with targeted measurements of select tanks, compressor engines and flares at a subset of 20 sites. Preliminary results from the top-down approach for both oil and gas and waste sectors will be discussed during the presentation, along with progress toward completing Colombia’s first-ever measurement-based methane inventory for the oil and gas upstream sector. This study, funded by UNEP - International Methane Emissions Observatory (IMEO) is intended to support MMRV development and verified reporting under the International Oil and Gas Methane Partnership (OGMP 2.0).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".