Fracture Network Characterization of the Lower Cretaceous Shu’aiba Outcrops in Central Oman, Wadi Baw
Bibliographic record
Abstract
Summary Fractured carbonate reservoirs contain over 60% of the world’s proven oil reserves ( Schlumberger, 2007 ). Accurate descriptions of such reservoirs are one of the fundamental challenges in reservoir modeling. While reservoir architecture has been extensively investigated in terms of depositional facies and diagenesis, the integration of fractures based on reservoir data is much more difficult. However, the presence of fractures introduces variability in fluid flow properties and creates complex flow paths within the reservoir. Inadequate structural reservoir characterization can result in undesirable consequences leading to decreased production rates, increased operational costs and in some cases, early well abandonment ( Bourbiaux, 2010 ). Since direct observations of fracture networks under the reservoir’s conditions are impossible due to the limited resolution of commonly used subsurface investigation methods, field observations and measurements conducted on wellexposed outcrops can help bridge the scale gap between well and seismic data ( Ramdani, 2022 ). In this study, we utilize carefully selected outcrops for quantitative fracture characterization as an analog for the Lower Cretaceous Shu’aiba Formation, which is part of one of the most prolific petroleum systems in the Middle East ( van Buchem et al., 1996 ).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".