Novel geochemical approaches to manage greenhouse gas emissions from thermal oil operations
Bibliographic record
Abstract
Canada has set strategic goals to reduce greenhouse gas (GHG) emissions nationally by 2030, with a particular emphasis on reducing methane emissions. Methane emissions from well integrity issues are a recognized source of GHG in the energy sector. To obtain reliable bottom up GHG emission inventories, accurate molecular compositional analyses of the relevant GHG species must be measured. To achieve this, an in-depth assessment of the goodness-of-fit of analytical calibration data, as it pertains to gas chromatographs, was developed. The results demonstrated a ‘best practice’ for analytical calibration that provides definitive estimates of trueness, precision, and accuracy that improves confidence in the concentration estimates used for regulatory applications and Canada’s GHG inventory. The methane source(s) of surface casing vent flow issues (biogenic or thermogenic origin) play an important role in targeted mitigation strategies. Geochemical methods were developed to improve surface casing vent flow source delineation as either baseline biogenic gases or thermogenic gases related to energy recovery operations. Additionally, challenges were identified in distinguishing different types of well integrity issues, i.e., resulting from a zonal isolation issue or a casing failure. A novel approach using geochemical tracers was developed and successfully implemented to delineate different types of well integrity issues. Together, this dissertation provides a framework to quantify, identify the source of, and, ultimately, improve management of well integrity issues that will lead to reductions in GHG emissions from thermal oil operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".