Development and Applications of a Semi-analytical Approximate Thermal Simulator
Bibliographic record
Abstract
Oil sand reservoirs play an important role in the economy of Canada due to their significant recoverable reserves. Due to the high viscosity of the oil in these reservoirs, conventional methods cannot be used for production. The steam-assisted gravity drainage (SAGD) method is an efficient way of producing oil from these reservoirs. Predicting oil production and steam injection rates is required for planning and managing a SAGD operation. This can be done by simulating the fluid flow with flow simulation codes, but this is very time consuming. The run time for a 3D heterogeneous model with one well pair can exceed 2 days. Another important task in SAGD operation is the optimization of the trajectory of the wells; the production forecasts for different well positions would require running the flow simulator multiple times, but that is too expensive. Yet another task is to quantify the uncertainty in steam requirements and bitumen production due to multiple realizations of the geological properties. Another task is to rank the multiple realizations from poor performing to good performing. This ranking could be used to help select a subset of realizations for more careful analysis. Finally, forecasting the location of the steam chamber at different time steps is a very important task for considering geomechanical effects. For these reasons, an approximate model that reasonably predicts oil production and steam injection rates with low computational effort would be valuable. In this dissertation, a reliable SAGD approximate simulator for predicting SAGD performance with 3D heterogeneous models of geologic properties is developed. This approximate simulator can handle different types of operating strategies. The approach is an approximate solution using a semi analytical model based on relevant theories including Butler's SAGD theory. The proxy is much faster than the full simulator and it gives accurate estimated oil production and steam injection rates at different time steps. Theoretical and numerical research has been undertaken to develop the proxy, implement it in fast code, demonstrate the accuracy of prediction and apply to realistic examples.
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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".