Combining Frequent 4D Seismic and Mechanistic Reservoir Modeling to Improve the Effectiveness of Steam Injection Operations
Bibliographic record
Abstract
Abstract 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 with seismic snapshots of the reservoir state available daily. These data were used within the Well and Reservoir Management framework to optimize production performance and increase field recovery (reported elsewhere). The field operations included, among other actions, a Cyclic Steam Stimulation (CSS) cycle carried out in an area with little connection to surrounding zones to promote communication with the rest of the pad. In this setting it was possible to perform a detailed mechanistic reservoir modeling study (described in this paper) aimed at explaining the unexpected well behavior observed during CSS. Insights from the combination of detailed reservoir modeling and frequent seismic data resulted in an improved understanding of reservoir dynamics including a pressure response strongly constrained by prior operations on the pad, the injection mainly in the vertical section of a horizontal well and the possible existence of a "patchy" saturation distribution. These learnings facilitated a better management of the pad and illuminate the issues to be expected in similar developments in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".