Development of a Steam-Additive Technology to Enhance Thermal Recovery of Heavy Oil
Bibliographic record
Abstract
Abstract Steam assisted gravity drainage (SAGD), cyclic steam stimulation (CSS) and steam floods are widely employed in the industry to extract heavy oil. These techniques use steam to lower the viscosity of bitumen, which translates into improved production. Sustainability of the enhanced production is primarily dependent on the ability to generate large volumes of steam. Steam generation consumes large amounts of natural gas and leads to higher CO2 emissions, which impacts profitability of the SAGD operation and the environment in a negative way. This problem, a technological void, calls for a solution with higher oil production for the same level of steam consumption, and thus higher profit margins. This paper reports how adding a chemical to the steam enhances production rates and recovery factor from the Alberta Oil Sands. The steam pumped into the reservoir serves as a carrier for the chemical additive. The additive allows condensed water to retrieve more bitumen by allowing higher mobilization of the bitumen deposits. Laboratory-scale simulations were conducted using a gravity-driven displacement process. Steam at 200°C and 2000 kPa was passed over an unconsolidated oil sands core sample. The results of the experiment were highly encouraging. An additive dosage rate of 1000 ppm resulted in a 13.5% increase in heavy oil recovery compared to the baseline (no additive, pure steam). This enhanced production at a low dosage rate of 1000 ppm prevents the need for any economically driven recovery of the chemical additive.
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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) | 0.001 | 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".