Preconditioning Methods to Improve SAGD Performance in Heavy Oil and Bitumen Reservoirs with Variable Oil Phase Viscosity
Bibliographic record
Abstract
Abstract The majority of the world's petroleum resources are contained in heavy oil and oil sand reservoirs. Average recoveries from heavy oil and oil sand reservoirs are typically low ranging from 5 to 15 percent for cold heavy oil production and from 30 to 85 percent for steam-based in situ processes. There are two reasons for this: first, geological heterogeneity in the form of variable porosity and permeability properties and secondly, fluid heterogeneities in the form of variable saturations, fluid compositions and thus viscosity. Geological heterogeneities refer to spatial variations of porosity, permeability, relative-permeability curves, shale and mud layers, etc. Fluid heterogeneities refer to spatial variations of the fluid composition and properties such as viscosity and density. Given that the permeability often varies by less than an order of magnitude whereas the oil viscosity varies by up to two orders of magnitude in a bitumen reservoir, the controlling variable on recovery of these resources is often fluid compositional variations. Due to the large viscosity contrast between oil and water at native reservoir conditions water is often the most mobile phase within a bitumen reservoir. This research identifies preconditioning techniques that can be used to alter reservoir or fluid (oil or water) properties prior to thermal recovery reducing adverse reservoir factors and improving recovery, environmental impact and process economics. We describe here a simulation study of one application related to modifying the variation of oil viscosity in the reservoir prior to steam injection. The methods make use of mobile water within the reservoir, to distribute viscosity-reducing agents before steam injection, and represent another means of geotailoring recovery processes to the features of the reservoir. The main benefit is that recovery process performance, both in terms of oil production rate and thermal efficiency, is improved.
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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) | 0.000 | 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".