Value of Information of Frequent Time-Lapse Seismic for Thermal EOR Monitoring at Peace River
Bibliographic record
Abstract
Abstract Time-lapse seismic surveillance is a proven technology for areal conformance monitoring offshore, but not onshore due to its high cost and typically poor data quality in that environment. Yet a number of examples in the industry show that non-uniform reservoir sweep is common in IOR and EOR projects and, if not addressed, it can significantly reduce ultimate recovery. In such projects the efficacy of injectants such as water, steam, gas, and solvents needs to be maximized to reduce cost and environmental footprint. This requires that we know what happens in-between wells, and for this purpose we conducted a pilot of high fidelity frequent seismic monitoring of thermal EOR re-development in one of the production pads in the bitumen deposits in Peace River, Canada. We detected patterns that can be directly linked to dynamic reservoir changes on a weekly or more frequent basis, such as pressure increase during injection, fluid phase changes, and connection to previously stimulated zones. The data also highlight the imprint of previous operations on the reservoir state prior to the current re-development, stressing the challenges faced when managing steam conformance. Our observations indicate that frequent time-lapse seismic images significantly contribute to determining injection/production strategy adjustments aimed at improved areal steam conformance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".