Conformance Control for SAGD Using Oil-in-Water Emulsions in Heterogeneous Oil Sands Reservoirs
Bibliographic record
Abstract
Steam-Assisted Gravity Drainage (SAGD) is a widely used technology for heavy oil and bitumen recovery in Alberta, Canada. However, a SAGD conformance problem can arise due to the heterogeneity of oil sands reservoirs, such as the presence of high permeability zones and high water saturation zones. In particular, during a geomechanical dilation start-up process that has been developed and applied in SAGD start-up operations, the dilation fluid tends to flow into the high permeability zones, leaving the low permeability zones un-swept. Therefore, the high permeability zones must be temporarily and selectively blocked off so as to more effectively dilate the low permeability zones along a SAGD well-pair. Laboratory permeability reduction tests in sandpacks using oil-in-water (O/W) emulsion injection showed that a permeability reduction of up to 99.97% can be achieved. Results of emulsion injection in parallel-sandpack tests demonstrated that good conformance control can be obtained by a suitable combination of interfacial tension (IFT), emulsion quality, emulsion slug size, and oil phase viscosity of an emulsion system. The reservoir simulation study was conducted to first match the laboratory test results and then to optimize SAGD conformance control operations using emulsion injection in heterogeneous oil sands reservoirs. A field-scale SAGD simulation model was established to show that emulsion injection before the dilation start-up process can build up communication between the injector and producer, resulting in more uniform steam chamber growth, lower cumulative Steam-to-Oil ratio (cSOR), and higher cumulative oil production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".