Research and development for reservoir monitoring: A contractor's viewpoint
Bibliographic record
Abstract
Many case studies were presented at the SEG Development and Production Forum in Kananaskis, Canada, in July 1999 which demonstrated that 4-D seismic “works.” Example after example presented convincing evidence of the ability of time-lapse measurements to detect production-related changes in the subsurface. The successful analyses which are now emerging will stimulate the development of new technology for seismic reservoir monitoring within oil companies, institutes for research and development, and contractors. Although much of the drive for this technology is coming from the perceived need to image fluids and their movements in the subsurface, the business challenges have probably not been as extensively discussed as the technical challenges. Kananaskis provided much encouragement that time-lapse measurements can supply a technical solution, and we can now focus on converting this technical progress into a business reality. This article explores some challenges from a contractor's viewpoint and invites discussion from all engaged in the development of this fascinating aspect of our industry.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".