Monitoring IOR/EOR Onshore with Frequent Time-Lapse Seismic - Status and Survey Adaptations for the Middle East
Bibliographic record
Abstract
Abstract IOR/EOR stimulation is a complex process that requires frequent areal monitoring to optimize recovery. On-demand seismic surveys are a suitable solution if cost, quality, environmental, and efficiency conditions are met. In this paper, we discuss the state of the art. We show that frequent buried seismic has the necessary quality and was able to add value to steam injection operations at a heavy oil field in Canada. However, in its current form, buried seismic is too expensive and intrusive. That is why, for future deployments, we seek cost and footprint reductions through judicious reduction of survey frequency (from days to weeks or months apart) and novel survey designs such as a buried cross-spread with Distributed Acoustic Sensing (DAS) receivers and movable subsurface sources. We are in the process of maturing the buried cross-spread solution. Its application to some extreme settings found in the Middle East (deep reservoirs with weak seismic expression under thick and complex overburden) is expected to be challenging. A promising alternative already available is 4D VSP with DAS in multiple wells. This solution requires sufficient surface access, fiber optic cables in wells, and skillful seismic processing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".