3-D Physical Modeling of Hybrid Steam and Oxygen Injection for In-Situ Recovery of Oil Sands
Bibliographic record
Abstract
Abstract Alberta (Canada) has the third largest proven reserves of oil in the world, mostly comprised of bitumen and heavy oil. To date, Steam Assisted Gravity Drainage (SAGD) has been the only commercially viablein-situ recovery technology for the Athabasca Oil Sands, but the current economic conditions have made the SAGD technology challenging, even for some high quality reservoirs. Moreover, from an environmental perspective, large water usage and high carbon emissions also make SAGD difficult to sustain. Due to the current low oil prices and rising penalties on carbon emissions, there is a need to develop improved recovery technologies that are more thermally efficient, with reduced environmental impact and at the same time economically feasible to develop. One of the technologies that has the potential to overcome these economic and environmental shortcomings is a hybrid steam and in-situ combustion process (ISC) called SAGDOX, as this process offers advantages over pure steam injection such as greater energy efficiency, lower water usage and reduced carbon emissions. The SAGDOX (SAGD and Oxygen Injection) process, which is proprietary to Nexen (Kerr, 2015), combines the benefits of both SAGD and ISC processes. Steam is used to preheat and pre-condition the reservoir, also aiding in carrying heat from the combustion zone towards the rich oil saturation zone. One of the main advantages of SAGDOX is the elimination of most of the nitrogen that otherwise would be injected when using air and this reduces the amount of non-condensable gas (NCG) in the reservoir substantially. Heat delivery to the formation using a combination of steam and enriched air injection is more energy efficient than pure steam injection and reduces the steam-oil ratio compared to SAGD. This also lowers the volumes of steam condensate in the produced fluids and thus reduces the produced water handling and processing requirements. The purpose of this study is to provide data to evaluate the feasibility of the SAGDOX process using a 3-D laboratory physical model. A description of the physical model is provided along with the method of operation. The results from two tests are presented and the findings from the experimental studies are also 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".