The signature of shear-wave splitting: Theory and observations on heavy oil data
Bibliographic record
Abstract
The use of shear-wave splitting analysis as a tool for fracture analysis is well established. In this article, we discuss the analysis of shear-wave splitting in a relatively new context—shallow heavy oil plays where we believe stress is the dominant cause of the shear-wave splitting, rather than macroscale fracturing. There is clear laboratory evidence in the literature for shear-wave splitting caused by differential stress, which we believe supports this viewpoint. We are particularly interested in the use of shear-wave splitting technology for monitoring reservoir stress changes which correlate with thermal production for heavy oil reservoirs. This article also takes a fresh look at some well-established characteristics of split shear waves as they appear in wide-azimuth multicomponent data, and in particular the relative merits of the radial and transverse amplitude signatures. We describe a recently developed method, which combines both radial and transverse analysis to improve the effective azimuthal coverage. This approach is beneficial when the survey has been coarsely acquired, as we demonstrate on a heavy oil example. The article concludes with a case study at Kerrobert, a reservoir in the Canadian heavy oil region where thermal recovery methods are in use, and where shear-wave splitting is being utilized to help characterize the resulting stress changes in the reservoir.
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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 | 0.002 | 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".