A refraction method to detect reservoir velocity and anisotropy
Bibliographic record
Abstract
Abstract Since its inception in the early 1980s, detection of fractures and stress using P-wave reflection amplitudes and traveltimes has proved to be challenging. A significant amount of theory has been developed, but convincing and calibrated applications of the theory to field data have been lacking. This is mainly because of the physical limitation that P-wave reflection amplitudes sample only a small areal region (Fresnel zone) of the reservoir. Similarly, P-wave reflection traveltimes sample only a limited thickness (twice near-vertical reservoir thickness) compared with total traveltime from the source to the receiver. As a result, estimation of reservoir anisotropy from P-wave reflection data is inherently limited. A new method is used to detect fracturing and stress in addition to reservoir velocity. When available, the use of refracted P-wave traveltimes from a target of interest can provide robust information on reservoir velocity and anisotropy caused by fractures and horizontal stress variations. This is because refracted waves travel horizontally inside the medium under investigation and sample a large section of the target, integrating the anisotropic variation along different azimuths. Azimuthal variation of refraction traveltimes from the investigated medium can be used to invert for velocity and anisotropy. This traveltime method is applied to the highly fractured Joanne reservoir in the U. K. sector of the Central North Sea.
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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 | 0.001 |
| 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".