Bibliographic record
Abstract
Abstract Steam-Assisted Gravity Drainage (SAGD) is a complex process governed strongly by geology and steam injection conditions, among other parameters under normal circumstances. At times it may become necessary to shut down a project temporarily for a variety of reasons, e.g. low bitumen price, high fuel cost, etc. Later, when steam injection is re-started, the performance may be considerably inferior to that for continuous operation. It is the intent of this work to examine the effect of steam stoppage for a certain period on SAGD performance, for a selected set of conditions. Also presented is an analytical approach. Two different geological models were employed in SAGD simulations - one was that of an actual reservoir, the other one consisted of a homogeneous reservoir. Steam injection conditions were similar for the two cases. The principal variables were steam injection time, the point of shut-off and the time of shut-off, and the steam injection rate. The simulator utilized a thermal compositional approach, treating the fluids as a three-component system. It was found that for a given steam injection time, the stoppage time (after which steam injection is restarted) lowered SAGD performance, as measured by oil recovery and steam-oil ratio, for the same volume of steam injected. The performance was lowered to a greater degree in the case if a real reservoir than for the homogeneous case. Steam injection rate reduction further adversely impacted performance. Another variable is the point of steam injection stoppage. An analytical model was developed to examine steam condensation during the shut-off period. The results of this work provide insight into the complex behaviour of SAGD when the process is interrupted. It provides guidelines on the interruption point and the length of interruption for minimizing the adverse effects on performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".