Not All Unconventional Reservoirs Are Similar: MENA Regional vs. Global Anisotropic Rock Index and Mechanical Characterization – Part 1
Bibliographic record
Abstract
Abstract Unconventional formations require advanced mechanical and index assessments to improve their understanding under different geomechanical processes. However, difficulties associated with obtaining cores from the target formations and the challenges with sample preparation increase the assessment complexity. This research compiles data from unconventional index properties and rock mechanical test results from published articles and reports. The parameters include rock mineralogy, rock mechanical properties (compressive strength and elastic properties), and petrophysical properties (porosity, TOC, and permeability). The study showcases the main differences between the global and regional (Middle East and North Africa) rock formations and presents the best analogs for the regional reservoirs. These findings supplement the scarce and complex procurement of the needed rock specimens and reduce the number of core samples required for detailed evaluations. These outcomes help reduce the costs (equipment, sample preparation, measurement time, and the number of specimens tested) associated with the unconventional rock experimental evaluation. In addition, this study explores the successful development strategy implemented in the unconventional reservoirs in China to accomplish commercial production and recommends appropriate rock analogs for detailed experimental evaluations. This paper is part of an in-depth literature data compilation of MENA regional and global unconventional formations. This section of the study focuses on the target unconventional formations of Saudi Arabia and the unconventional formations in China, the USA, and Canada.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".