Thermal Techniques for the Recovery of Heavy Oil and Bitumen
Bibliographic record
Abstract
Abstract Over 90% of the world's heavy oil and bitumen (oil sands) are deposited in Canada and Venezuela. Alberta holds the world's largest reserves of bitumen and the reserves are of the same order of magnitude as reserves of conventional oil in Saudi Arabia. Up to 80% of estimated reserves could be recovered by in-situ thermal operation. As the resources available for conventional crude in Canada continue to decline, further development of heavy oil and oil sands in-situ recovery technologies is critical to meeting Canada's present and future energy requirements. Sophisticated technologies have been required to economically develop Canada's complex and varying oil fields. Various existing in-situ technologies such as hot water injection, steam flooding, cyclic steaming and combustion processes have been successfully applied in Venezuela and California. Most recently, advances made in directional drilling and measuring while drilling (MWD) technologies have facilitated development of new in-situ production technologies such as the steam assisted gravity drainage (SAGD), expanding solvent-SAGD (ES-SAGD) and solvent vapor extraction (VAPEX) that have significantly improved well-bore reservoir contact, sweep efficiencies, produced oil rates and reduced production costs. This paper provides an overview of existing and new thermal in-situ technologies and current projects. Potential of new technologies are assessed and compared to various existing in-situ thermal processes. Critical issues affecting production performance are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".