Using VSP surveys to bridge the scale gap between well and seismic data
Bibliographic record
Abstract
Abstract This case study was undertaken for a low-porosity fractured carbonate reservoir with a complex fracture network resulting from several phases of tectonic activity. The integration of the image log and seismic-derived interpretations was problematic due to the complexity of the image log signature and the variable quality of the surface seismic data. Earlier experience indicated that VSPs may provide information on faulting and/or fracturing that may otherwise be difficult to determine with confidence from other data sources. Consequently, specialist VSP processing techniques were used to identify and map reflectors in three-dimensional space. Data acquired in two wells were reprocessed to interpret structural features and determine their geometries. The interpreted VSP reflectors were validated and integrated with the analyses of image logs and the interpretation of surface seismic data providing a constrained structural model that allowed the interpretation of seismic data away from well control and provided a starting point for seismic interpretation in areas where structural geometries were poorly imaged on surface seismic. It is shown that VSP, including vertical incidence, data can contribute to the understanding of reservoirs and enables well-derived information to be extrapolated away from the wells.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".