Steam Chamber Development and Production Performance Prediction of Steam Assisted Gravity Drainage
Bibliographic record
Abstract
Abstract Steam assisted gravity drainage (SAGD) is an effective technology to develop heavy oil reservoir, yet with large energy consumption and intense greenhouse emission. Therefore, it is important to predict the steam chamber development process and production performance of SAGD process. In early research, a lot of research has been conducted on the prediction of SAGD productivity analytically under some simplification. According to tens of numerical reservoir simulation results with STARS, we find that oil production rate is greatly linked to the steam injection rate. As to our knowledge, few studies have been published to build a relationship between them. In this paper, we propose a new analytical model to predict steam chamber development process and SAGD production performance under constant steam injection rate simultaneously. On the basis of previous numerical and experimental research, we assume that the steam chamber shape is a combination of two symmetrical parabolas or an inverted triangle. The oil production rate is expressed by the steam chamber expansion rate as a function of reservoir properties and injection parameters. An energy balance equation is employed to connect the steam expansion rate and heat loss rate to surrounding formation. Comparisons have been made between the new model results and STARS results for a specific super-heavy oil reservoir case in Canada and similarity is observed with the parabola-shape assumption. With the new proposed model, production performance, such as oil production rate, water cut and steam oil ratio, can be predicted.
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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".