A geospatial method to assess site suitability for static vehicle-based measurements of methane plumes
Bibliographic record
Abstract
Vehicle-based methane-sensing systems are gaining popularity as tools for monitoring site-level methane emissions from oil and gas (O&G) sites. To measure emissions, vehicles equipped with methane sensors intersect plumes along roads downwind of target sites and acquire measurements in static (parked) or mobile modes. The downwind distance between the emissions source and the measurement location is one of several factors that must be considered in planning these types of surveys. Here we present a method to estimate the suitability of O&G facilities for vehicle-based measurements using downwind distances recommended in OTM 33A and the tracer technique. We present two types of analyses: (1) a historical analysis using weather reanalysis data and (2) an operational analysis using forecast data. The method uses modeled wind direction and geospatial data to identify O&G facilities that have roads between 20 and 200 m downwind for OTM 33A and between 500 m and 3000 m downwind for the tracer technique. We apply the method to O&G facilities in Alberta that will soon require annual or triannual LDAR surveys. For the historical analysis we use ERA-Interim wind data and calculate the vectorial average (resultant) of modeled winds for the period 2009-2018. Of the 35047 O&G facilities examined, we find that 7% are, on average, suitable for OTM 33A and 69% are, on average, suitable for the tracer technique, based solely on downwind distance. We surmise that other factors like landcover, weather conditions (e.g., stability), and topography would likely reduce the candidate pool from these estimates. We demonstrate the operational utility of the method by examining a subset of 100 O&G facilities in southern Alberta and using forecast wind direction from the Canadian High Resolution Deterministic Prediction System (HRDPS), which has a 2.5 km grid spacing. We propose that the method can be used as a screening tool to estimate site suitability for static vehicle-based surveys and that it will likely translate to mobile surveys once the effect of downwind distance is clarified. Other factors can be incorporated in the method once test results are available.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".