Data Mining and Unsupervised Machine Learning in Canadian In Situ Oil Sands Database for Knowledge Discovery and Carbon Cost Analysis
Bibliographic record
Abstract
A better understanding of greenhouse gas (GHG) emissions resulting from oil sands (bitumen) extraction can help to meet global oil demands, identify potential mitigation measures, and design effective carbon policies. While several studies have attempted to model GHG emissions from oil sands extractions, these studies have encountered data availability challenges, particularly with respect to actual fuel use data, and have thus struggled to accurately quantify GHG emissions. In this study, we extracted operating data from a public database—Petrinex—containing over 35 million records for 20 in situ oil sands extraction schemes. From 2015 to 2019, the weighted averages of fuel use for schemes employing steam-assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) were 0.21 103m3 fuel to produce 1 m3 bitumen (0.24 103m3/m3) and 0.34 103m3 fuel to produce 1 m3 bitumen (0.34 103m3/m3), respectively. The weighted average emission intensity (EI) for SAGD was 0.39 t CO2e/m3 undiluted bitumen (62 kg CO2e/bbl), and the weighted average EI for CSS was 0.65 t CO2e/m3 undiluted bitumen (103 kg CO2e/bbl). At a carbon price of CAD $30/t CO2e and an undiluted bitumen price of CAD $326/m3 (USD $39/bbl), the average carbon cost accounted for 2% ($ t CO2e/$ m3 undiluted bitumen). A single emission cap for the entire in situ oil sands sector is not appropriate because carbon costs range significantly. To prevent carbon leakage due to competitiveness migration, facility-specific or recovery method-specific emission caps should be considered when designing carbon pricing programs. A single intensity-based emissions cap was ineffective in reducing emissions. The annual average emission intensity for the 20 in situ oil sands operations decreaed by 15%. The absolute emissions, however, increased by 120%. The combination of an intensity-based emissions cap and an absolute emissions cap should be considered as a means of bending the emissions curve.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".