A Method to Calculate In–situ Water Relative-Permeability Using the Response at Observation Well Adjacent To Steam-Assisted-Gravity-Drainage Well-Pairs
Bibliographic record
Abstract
Abstract Although convective heat flux is neglected in classic steam-assisted gravity-drainage (SAGD) models such as the one derived by Butler, heat convection through the condensate flow into porous media can be very important. A key control on the condensate flow is the in-situ relative-permeability to water within the reservoir. To date there is no methodology that can calculate the in-situ relative-permeability to water in SAGD reservoirs during steam injection and progression of the steam chamber. The in-situ relative-permeability to water during steam injection can be different from laboratory and well-test results. The problem with the results from lab test is that they are mostly from disturbed cores or remolded samples. And the issue with results from both lab and well testing is that at the edge of the steam chamber, bitumen undergoes thermal expansion of some 15%; therefore, unless the water saturation increases by a similar amount, water mobility is quickly decreased by the expanding bitumen phase. A method is provided to calculate in-situ relative-permeability in oil sand reservoirs using the distance between pressure and temperature response at the same depth. The implementation of the concepts is tested for Underground Test Facility (UTF) project. The results suggest that the in-situ relative-permeability to water during steam in most SAGD projects is limited to 10-4 to 10-5 and having higher relative permeabilities is only possible at lean zones. This paper describes a new method for reservoir characterization based on water mobility for different oil sand projects.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".