Large-scale Application of (Anisotropic) Pre-stack Depth Migration in the Foothills
Bibliographic record
Abstract
Abstract Introduction Shell Canada Limited is routinely applying full (anisotropic) Pre-Stack Depth Migration (PSDM) to 2D and 3D seismic surveys in the Foothills of Western Canada. In this paper we will show data examples which illustrate the practical application of these techniques. This technology can contribute to drilling success though proper well positioning and thus has important business implications. Anisotropic 3D PSDM In the highly structured fold and thrust belt of the Foothills, anisotropic clastic beds which are steeply dipping often overlay deeper carbonate targets. This can result in poor focussing and mispositioning of structures on the seismic section unless these effects are correctly accounted for in processing. The Shell Group has developed algorithms for anisotropic prestack depth migration which are being effectively applied by Shell Canada on both 2D and 3D Foothills data. Structures show improved focussing and, in some cases, lateral shifts on the order of 100 - 300 metres. The formulation of the Shell proprietary anisotropic PSDM is such that it is able to handle any symmetry of anisotropy. However, current experience is limited to working with the TI assumption, though also accommodating a laterally varying tilted axis of symmetry. Practical considerations for PSDM Shell's proprietary state-of-the-art processing management software (used worldwide by the Shell Group) deals with many of the logistical requirements for processing large amounts of data on a network of workstations. This has had significant impact on the turnaround achievable for 3D PSDM projects. It also frees up geophysicists to concentrate on geophysical issues, including velocity model building and updating, as opposed to administering jobs through the system. Thus quality PSDM processing is accomplished in a timely, efficient manner which is suitable not only for Development but also for Exploration needs. Though expertise is required to oversee the correct application of the technology, the whole system facilitates a staged progression of learning for personnel new to 3D PSDM, allowing them to quickly become productive. Our PSDM processing software is tightly linked into Shell's proprietary 3D interpretation software (123DI) which provides a communication vehicle for processor/interpreter interaction on an ongoing basis from initial model development to intermediate results and model updates, to final product. This is a crucial link, the impact of which should not be underestimated.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".