Sedimentology, petrography, and reservoir quality of the Zarga and Ghazal formations in the Keyi oilfield, Muglad Basin, Sudan
Bibliographic record
Abstract
The Zarga and Ghazal formations constitute important reservoirs across the Muglad Basin, Sudan. Nevertheless, the sedimentology and diagenesis of these reservoir intervals have hitherto received insignificant research attention. Detailed understanding of sedimentary facies and diagenesis could enhance geological and geophysical data for better exploration and production and minimize risks. In this study, subsurface reservoir cores representing the Zarga formation (1114.70-1118.50 m and 1118.50-1125.30 m), and the Ghazal formation (91,403.30-1406.83 m) were subjected to sedimentological (lithofacies and grain size), petrographic/mineralogic (thin section, XRD, SEM), and petrophysical (porosity and permeability) analyses to describe their reservoir quality, provenance, and depositional environments. Eight (8) different lithofacies, texturally characterized as moderately to well-sorted, and medium to coarse-grained, sub-feldspathic to feldspathic arenite were distinguished in the cored intervals. Mono-crystalline quartz (19.3-26.2%) predominated over polycrystalline quartz (2.6-13.8%), feldspar (6.6-10.3%), and mica (1.4-7.6%) being the most prominent constituent of the reservoir rocks. Provenance plot indicated the sediments were from a transitional continental provenance setting. The overall vertical sequence, composition, and internal sedimentary structures of the lithofacies suggest a fluvial-to-deltaic depositional environment for the Ghazal formation, while the Zarga formation indicated a dominant deltaic setting. Kaolinite occurs mainly as authigenic mineral, while carbonates quantitatively fluctuate with an insignificant amount of quartz overgrowths in most of the analyzed cores. Integration of XRD, SEM, and thin section analysis highlights that kaolinite, chlorite, illite, and smectite are present as authigenic minerals. Pore-destroying diagenetic processes (e.g. precipitation, cementation, and compaction etc.) generally prevailed over pore-enhancing processes (e.g. dissolution). Point-counted datasets indicate a better reservoir quality for the Ghazal formation (ɸ = 27.7% to 30.7%; K = 9.65 mD to 1196.71 mD) than the Zarga formation (17.9% to 24.5%; K = 1051.09 mD to 1090.45 mD).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".