The Distribution and Origin of Carbonate Cements in Deep-Buried Sandstones in the Central Junggar Basin, Northwest China
Bibliographic record
Abstract
Extremely high porosities and permeabilities are commonly discovered in the sandstones of the Xishanyao Formation in the central Junggar Basin with the burial depth greater than 5500 m, from which hydrocarbons are currently being produced. High content of carbonate cements (up to 20%) is also observed in a similar depth range. Our study aimed to improve our understanding on the origin of carbonate cements in the Xishanyao Formation, in order to provide insights into the existence of high porosity sandstones at greater depths. Integrated analyses including petrographic analysis, isotopic analysis, fluid-inclusion, and core analysis were applied to investigate the distribution and origin of carbonate cements and the influence of high fluid pressure on reservoir quality. Textural evidences demonstrate that there are two generations of carbonate cements, precipitated at the temperature of 90°C and 120°C, respectively. The carbonate cements with low δCPDB13 ranging from −19.07 to -8.95‰ dominantly occurred near the overpressure surface and especially accumulated at approximately 100 m below the surface. Our interpretation is that high content of carbonate cements is significantly influenced by early carbonate cements dissolution and migration under overpressure. Dissolution of plagioclase resulted in the development of internal pores and porosities of as much as 10% at 6500 m depth presumably.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".