Transference of Reservoir Uncertainty in Multi SAGD Well Pairs
Bibliographic record
Abstract
Abstract Extensive computational time is required to predict SAGD production performance by thermal reservoir simulation. The common practice is to evaluate the performance of a single well pair and combine the results to pads and projects. Production forecasts for multiple SAGD well pairs simultaneously are computational expensive. At the same time, there is a need to make reservoir management decision in presence of reservoir uncertainty. Accounting for joint uncertainty across the reservoir requires simultaneous management of multiple realizations. Thermal simulators cannot be applied to the transference of such uncertainty to SAGD performance of many well pairs. A proxy model based on the Butler's SAGD theory has been developed to overcome this challenge. The proxy is able to account for reservoir heterogeneity. Modifying factors are included to fit the proxy results to simulation outcomes. The methodology for the transference of the reservoir uncertainty includes: generation of the reservoir uncertainty model using geostatistical tools; ranking the geologic realizations using the Cumulative Steam Oil Ratio (CSOR) calculated by the unfitted proxy. The ranking process is done in a reservoir volume representative of a single well pair: reservoir simulation for the P10 to P90 realizations, fitting the proxy model to simulation results, and assessment of the uncertainty of the SAGD performance by Monte Carlo Simulation (MCS). This methodology was applied to a synthetic example of a pad of eight SAGD well pairs, using 3D geometry and based on hard data from an Athabasca bitumen deposit. The results show the uncertainty in the dynamic performance of SAGD production variables for the entire pad. The results permit decision making under the uncertainty. It is shown that approximate models accounting for uncertainty are better than precise models that cannot be used in stochastic calculations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".