Not All Unconventional Reservoirs are Similar MENA Regional Vs Global Anisotropic Rock Index and Mechanical Characterization – Part 2
Bibliographic record
Abstract
Abstract Improved understanding of unconventional formations requires advanced mechanical and index assessments to explore their complex geology, fissility, and anisotropic behavior. This publication is an extension of the work presented in (Gramajo and Rached 2022), which presented comprehensive datasets of unconventional rocks from China, the United States of America (USA), Canada, and Saudi Arabia. The datasets include the mineral composition, petrophysical parameters (Total Organic Carbon (TOC), porosity, and permeability), and mechanical properties (elastic parameters and compressive strength values). This paper extends the analysis to include unconventional formations from the Middle East and North Africa (MENA) datasets, specifically from Bahrain and the United Arab Emirates (UAE). The study enhances our understanding of the newly added rock formations and defines the rock analogs and initial parameters needed to tailor down-hole tools, fracturing fluids, and engineering processes. The results will help reduce the costs (equipment, sample preparation, and measurement time) associated with the initial experimental assessments and achieve higher production outcomes in the emerging reservoirs.
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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 | 0.000 | 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".