Applications of digital outcrop models: two fluvial case studies from the Triassic Wolfville Fm., Canada and Oukaimeden Sandstone Fm., Morocco
Bibliographic record
Abstract
Abstract The application and benefits of employing digital outcrop models (DOMs) are discussed using two Triassic fluvial case studies to demonstrate data collection and integration methods. Developments in data analysis techniques are examined to demonstrate their utility for collecting meaningful and reliable statistical information needed to build realistic stochastic reservoir models. To establish a significant geostatistical dataset a large number of accurate observations are required. It is difficult to get the necessary statistics using subsurface data alone, due to the limited resolution and/or areal coverage of respectively seismic and well data. Outcrop studies are, therefore, commonly utilized to provide analogue statistical information (e.g. channel width, length, thickness and thickness vs. width ratio). Traditional data collection methods used in the field are however largely restricted to areas with (easy) physical access, or using remote observations with limited accuracy, such as photographic methods. Digital data collection techniques such as LiDAR (Light Detection and Ranging) and differential GPS allow more accurate measurements, as well as from previously inaccessible locations, to be taken of sedimentary architecture. The technique generates much larger volumes of measurements, as the area from which accurate data can be extracted is increased. This offers a more meaningful statistical dataset, hence reducing uncertainty in the final reservoir model. Both case studies, the Oukaimeden Sandstone Formation (OSF), Morocco and Wolfville Formation, Canada, are from Late Triassic braided fluvial systems. The OSF dataset has been used to illustrate how geometric information of channel width versus thickness relationships (W:T) are collected using a projection plane technique. The results show W:T variations between 3.49:1 in the Lower Oukaimeden member, 1.54:1 in the Middle Oukaimeden member and 3.75:1 in the Upper Oukaimeden member, demonstrating the observed architectural evolution of the fluvial system. The Wolfville Formation case study shows how DGPS in combination with LiDAR data has been used to more accurately map faults to obtain statistical information on fault orientation (NE–SW) and length (mean = 38.3 m and median = 18.2 m). Another applied analysis technique utilizes a facies classified point‐cloud to aid surface correlations between sedimentary logs and construct a log based correlation panel from which estimates of facies frequencies are derived. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".