Textural Control on the Development of Dolomite Reservoir: A Study from the Cambrian-Ordovician Dolomite,Central Tarim Basin,NW China
Bibliographic record
Abstract
The study of dolomite texture not only indicates the origin of the dolomites but controls the quality of dolomite reservoirs significantly.The main objectives of this paper are to investigate the differences of the dolomite reservoirs with distinct textures and the relationship between dolomite texture and reservoir development of the Cambrian-Ordovician carbonate rocks in central Tarim Basin,based on observation of core,thin-section and SEM,combined with the analyses of petrophysical properties.The results show that:(1)Dolomite texture can be used to predict the reservoir quality due to the strong interdependency between petrophysical properties of the reservoir and dolomite texture,the reservoir qulity of the fine-crystalline. Planar-e(euhedral)dolomite is the best of all the dolomite reservoirs,and the very-fine to fine crystalline planar-s(subhedral)dolomite reservoir is mediate,but the medium to coarse crystalline,non-planar-a(anhedral)dolomite and precursor lithologic fabric preserved dolomite are poor in porostiy;(2)Dolomite texture also affects porosity type.With the shape of dolomite crystal changing from planar-e to non-planar-a,the porosity of dolomite reservoir transforms intercrystalline pore to vug and fracture porosity;(3)The formation and modification process of dolomite reservoir are controlled by dolomite texture,resulting in the shortage of cave porosity and abundance of thin-bedded,porous intervals with intercrystalline/vug porosity in the dolomite 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 | 0.000 |
| 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 | 0.000 |
| 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".