Maximizing VOI of Frequent Seismic Monitoring of Heavy Oil Fields: Case Study at Peace River Pad 31
Bibliographic record
Abstract
Abstract In May 2014 a permanent seismic monitoring system was deployed in Peace River Pad 31 to monitor areal steam conformance during pad re-development in this heavy oil field in Alberta, Canada. The dataset comprises a two-year monitoring period and was used within the Well and Reservoir Management framework to optimize production performance and increase field recovery. With seismic snapshots available daily, one area of investigation was to determine the monitoring frequency that maximizes Value of Information (VOI). A lookback study with a panel of independent experts showed that for the particular case of Pad 31, with very limited control due to the existing well completions, quarterly monitoring would be sufficient. Additionally, guidelines were developed for defining an effective seismic monitoring program (e.g., align monitoring with major field interventions, allow enough time for changes to be actionable, etc.). Although based on observations from the Pad 31 trial, these guidelines should be applicable to other thermal developments in heavy oil fields.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".