A portrait of wellbore leakage in northeastern British Columbia, Canada
Bibliographic record
Abstract
Oil and gas well leakage is of public concern primarily due to the perceived risks of aquifer contamination and greenhouse gas (GHG) emissions. This study examined well leakage data from the British Columbia Oil and Gas Commission (BC OGC) to identify leakage pathways and initially quantify incident rates of leakage and GHG emissions from leaking wells. Three types of leakage are distinguished: "surface casing vent flow" (SCVF), "outside the surface casing leakage" (OSCL), and "cap leakage" (CL). In British Columbia (BC), the majority of reported incidents involve SCVF of gases, which does not pose a risk of aquifer contamination but does contribute to GHG emissions. Reported liquid leakage of brines and hydrocarbons is rarer. OSCL and CL of gas are more serious problems due to the risk of long-term leakage from abandoned wells; some were reported to be leaking gas several decades after they were permanently abandoned. According to the requirements of provincial regulation, 21,525 have been tested for leakage. In total, 2,329 wells in BC have had reported leakage during the lifetime of the well. This represents 10.8% of all wells in the assumed test population. However, it seems likely that wells drilled and/or abandoned before 2010 have unreported leakage. In BC, the total GHG emission from gas SCVF is estimated to reach about 75,000 t/y based on the existing inventory calculation; however, this number is likely higher due to underreporting.
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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 | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".