Impact of Oil-Water Relative Permeability Curves on SAGD Behaviour
Bibliographic record
Abstract
Abstract Today, heavy oil and oil sands are starting to play a more important role. To manage and develop these resources, especially for thermal recovery processes, numerical modeling is often used to design the operating and well placement strategies. Relative permeability curves are one of the most important parameters for modelling these systems. This is especially important in systems where the saturation of each phase changes over a wide range as is the case in steam-based recovery processes such as Steam-Assisted Gravity Drainage (SAGD). Initially, before SAGD, the pore space of the oil sands reservoir is mainly occupied by bitumen with oil saturation typically between 80 and 90%. After steam is injected into the reservoir, the oil is heated and mobilized and drains under gravity and is replaced by first steam condensate and then, as the chamber propagates further into the reservoir, steam and solution gas. This means that the reservoir undergoes a series of large changes in phase saturations as the recovery process evolves in the reservoir. Thus, the interactions of the phases and their flow characteristics, that is, the relative permeability curves, are an essential component of the physics of SAGD. However, for modelling SAGD, due to limited relative permeability curve data, it is often adopted from analogs or previously history-matched curves. Given the heterogeneity of oil sands reservoirs, careless adoption of relative permeability curves will lead to serious risk of unexpected performance. The objective of this study is to investigate the impact of the endpoints of the oil-water relative permeability curves on SAGD performance by using numerical reservoir simulation. The results reveal that SAGD performance is sensitive to the values of the endpoints of the oil-water relative permeability curves. Given the range of variability of the results, it is recommended that relative permeability uncertainty analysis is always done during the simulation assessment of a targeted oil sands resource. Also, it is recommended that curves are obtained from multiple core samples of the target reservoir to reduce uncertainty and to assess the degree of heterogeneity of the endpoints.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".