New Hybrid Steam-Solvent Processes for the Recovery of Heavy Oil and Bitumen
Bibliographic record
Abstract
Abstract Canada has declining reserves of conventional oil, but vast reserves of heavy oil and bitumen. Over 90% of the world's heavy oil and bitumen trapped in sandstones and carbonates are deposited in Canada and Venezuela. Up to 80% of estimated reserves could be recovered by in-situ thermal operation. The current in-situ thermal technologies such as cyclic steam stimulation (CSS), steam flooding and steam-assisted gravity drainage (SAGD) are energy intensive and use large quantities of fresh water. Increasing pressure of environmental concerns and the threat of a carbon tax will make it imperative to find new oil extraction technologies that are less energy intensive and that use less water. Combining technologies in the form of hybrid steam-solvent processes offer the potential of higher oil rates and recoveries, but at less energy and water consumption than processes such as SAGD. At the Alberta Research Council, new hybrid steam-solvent processes have been undergoing development in recent years. The Expanding Solvent-SAGD (ES-SAGD)(1–2), is aimed at improving and extending SAGD performance by solvent addition to steam. The improvements include higher and faster drainage rates, lower energy and water requirements and reduced green house gas (GHG) emissions. The Thermal Solvent Hydrid process focuses on combining solvent with a small amount of steam in a VAPEX (vapour extraction) process(3–4). This process offers the potential of higher rates than cold solvent VAPEX at less energy consumption than SAGD. Hybrid steam-solvent processes, when fully developed, will extract oil at lower cost than SAGD and will also open currently marginal resources for exploitation, increasing oil reserves. This paper presents and discusses the principal concepts and key parameters for the new hybrid steam-solvent processes and compares expected performance to SAGD.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".