Thermal Maturation Regime Revisited in the Dongying Depression, Bohai Bay Basin, East China
Bibliographic record
Abstract
To the accurate reconstruction of the hydrocarbon generation history in the Dongying Depression, Bohai Bay Basin, East China, core samples of the Eocene Shahejie Formation from 3 shale oil boreholes were analyzed using organic petrology and organic geochemistry methods. The shales are enriched in organic matter with good to excellent hydrocarbon generation potential. The maturity indicated by measured vitrinite reflectance (%Ro) falls in the range of 0.5–0.9% and increases with burial depth in each well. Changes in biomarker and aromatic hydrocarbon isomer distributions and biomarker concentrations are also unequivocally correlated with the thermal maturity of the source rocks. Maturity/depth relationships for hopanes, steranes, and aromatic hydrocarbons, constructed from core data indicate different well locations, have different thermal regimes. A systematic variability of maturity with geographical position along the depression has been illustrated, which is a dependence on the distance to the Tanlu Fault. Higher thermal gradient at the southern side of the Dongying Depression results in the same maturity level at shallower depth compared to the northern side. The significant regional thermal regime change from south to north in the Dongying Depression may exert an important impact on the timing of hydrocarbon maturation and expulsion at different locations. Different exploration strategies should be employed accordingly.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".