IOR/EOR Monitoring Onshore with Frequent Time-Lapse Seismic
Bibliographic record
Abstract
Abstract With the recognized cost and complexity of IOR and EOR projects onshore, there is a significant risk of projects not meeting their NPV or production targets, unless cost-effective well and reservoir management plans are in place to mitigate such risks. Time-lapse seismic could provide important and timely information to diagnose and remediate areal conformance issues, if the time-lapse data is of high enough quality and low enough cost to be acquired on a frequent basis. We review the business case, expected value, and affordability of seismic monitoring in IOR/EOR projects and the new technologies that may be used for this purpose. We illustrate these ideas with a case study from the Peace River heavy oil field in Canada, as it is implemented today to monitor steam injection in a relatively shallow reservoir and how it could be implemented in the future to reduce cost and preserve value by judicious adjustment of the frequency of seismic monitoring. We also comment on the challenges and technology options for monitoring of deeper reservoirs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".