ABSTRACT: Seismic Visualization of Subtle Fault/Fracture Zones in Carbonate Reservoirs: Two Case Studies (Fahud - Oman and Waterton - Canada)
Bibliographic record
Abstract
One of the key elements in unravelling the structural geometry of carbonate reservoirs is the analysis of seismic data. This poster is compiled primarily to give an insight in the visualization techniques used to highlight fault/fracture patterns. Secondly a work around is given to solve the problems encountered with data management between the different software packages used in the studies. The two case studies discussed are both part of separate multi-disciplinary asset studies carried out in the Shell International Carbonate Development team in Rijswijk. The first study (carried out in the first half 2000) focussed on the Upper Cretaceous Natih E reservoir of the Fahud oil field in Oman. The shallow onshore seismic data suffered from severe noise and quality loss due to amongst others soft overburden. The second study, which is still under evaluation, is carried out on the pre-stacked imaged seismic data in the foothills of the Rocky Mountains in Alberta. The North Waterton gas reservoirs are situated in the thrusted Lower Carboniferous to Devonian sequence. Due to the large terrain effects the seismic acquisition and processing is a tedious job with results that are difficult to interpret.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".