Limitations of diamondoids in the quantitative evaluation of petroleum cracking in ultra-deep carbonate reservoirs of the Shunbei area, Tarim Basin
Bibliographic record
Abstract
Diamondoid hydrocarbons have been widely used to assess the extent of petroleum thermal cracking. In reservoirs subjected to multi-stage petroleum charges, thermal cracking might not occur in situ and, therefore, complicate the accurate evaluation of cracking within a petroleum mixture. The Shunbei area in the Tarim Basin is typical of ultra-deep carbonate reservoirs with multi-stage tectonic movements and petroleum charges where thermal cracking and evaporative fractionation are common. This study takes oil samples from the No.1 and No.5 fault zones in the Shunbei carbonate reservoirs as a case study to investigate the limitations of using diamondoid hydrocarbons for evaluating petroleum thermal cracking in reservoirs where diamondoid-rich gas has intruded. Organic geochemical data reveal that the abundant diamondoids in the No.1 fault zone were allochthonous, while the less abundant diamondoids in the No.5 fault zone are from in situ thermal cracking. These findings suggest that diamondoids may provide misleading indications of oil cracking, as they can be contaminated by allochthonous cracked oils. Therefore, caution should be taken when diamondoid hydrocarbons are applied to evaluate thermal cracking. Novel indicators remain to be proposed to more accurately assess the degrees of in-reservoir oil cracking with petroleum mixing from a secondary, diamondoid-rich charge.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".