Experimental Design to Optimize Operating Conditions for SAGD Process, Peace River Oilsands, Alberta.
Bibliographic record
Abstract
Abstract The SAGD process is a promising recovery method for producing heavy oils and bitumen resources. However, SAGD process has several economic risks including the high capital cost of initial investment for building ground facilities and uncertainties related to oil and gas prices. These risks may be critical in SAGD operation if the design for initial operating conditions is unsuitable. In order to ensure maximize profitability, optimal operation conditions should be evaluated by reservoir simulations. In this study, central composite design and response surface methodology (RSM) were applied to determining optimal conditions for SAGD process. It was aimed to mitigate the risk of incomprehensive economic assessment on the process operation. The study started with the central composite face-centered (CCF) design to screen variables, and then insignificant variables were excluded from the study before developing the optimal design by response surface method. A two-stage approach was employed based on the efficient local optimization. At first, an initial sample of design was obtained using design of experiment technique. Simulation runs for design points were used to estimate oil recovery as well as NPV for each case. Based on the standard of CCF design, total 28 cases was run to optimize the parameters of operating conditions and the NPV responses during 10 years of simulation period. Second, RSM was used to search for promising designs in contour plots and response surface map. The best choice of operating conditions for maximizing the NPV correspond to well pattern spacing of 78m, steam rate of 640 m3/d, injector producer spacing of 14m, injection pressure of 6330 kPa, subcool 8°C, respectively. Simulation results showed that cumulative oil for Fast-SAGD process does not significantly increase and even NPV is the lowest among the mentioned SAGD cases. In addition, cumulative oil recovery of SAGD1 base case is higher than those of SAGD2 and Fast-SAGD cases, as well as the lowest CSOR. However, in the economic point of view recognized that the case SAGD2 achieves the highest NPV, with the predicted values matched the experimental values reasonably well with R2 of 0.99 and Q2 of 0.88 for NPV response, while the NPV of Fast-SAGD process is the lowest because of the increasing capital cost for additional offset wells. Actually, the difference of 10kPa between steam injection pressure and reservoir pressure is not sufficient to increase the NPV for both Fast-SAGD and SAGD1 base case operations. The high cumulative oil is favorable conditions for accelerating profit, but oil and gas prices at that time is crucial to decide for operation conditions in heavy oil projects.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".