Thermal Techniques for the Recovery of Heavy Oil and Bitumen
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it