Recent Advances in Seismic Monitoring of Thermal EOR
Bibliographic record
Abstract
Well-planned and executed reservoir surveillance has proven to add significantly to the production and ultimate recovery of hydrocarbons, notably in areas of Improved and Enhanced Oil Recovery (IOR/EOR). Recent technological advances in the area of data acquisition and integration have led to increased use of well and reservoir surveillance data to optimize such processes. In the case of thermal EOR, one of the most important subsurface uncertainties impacting performance is heat and steam front conformance, both vertically and arealy. This paper illustrates new geophysical technologies used for monitoring various thermal EOR recovery strategies in The Netherlands, Canada, and Oman. We focus on permanently buried seismic sources and receivers, refraction seismic, down-hole seismic, and the newly developed Distributed Acoustic Sensing (DAS) to enable low-cost and non-intrusive seismic surveillance. These technologies are not without challenges, but our field trials indicate they have the potential to broaden the successful application of reservoir monitoring onshore.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".