Physical properties variation of crude oil under natural laboratory and its geological implications: Dongying Sag, eastern China
Bibliographic record
Abstract
The Dongying Sag in eastern China is a mature exploration area and contains a wide variety of accumulations ranging from extra heavy oil to light oil. This multi-source lacustrine rift basin provides meaningful insights into the relationships between organofacies, maturity, charging, mixing, and oil properties. The Es3 source rocks are in the immature to mid-mature stage, while the Es4 source rocks are in the immature to late mature stage. Among them, Es4 source rocks were deposited in an anoxic, stratified, brackish water environment. Based on the kinetic model, it appears that the oil generation rate of Es4 source rocks is higher than that of Es3 source rocks, and the oil generation time of Es4 source rocks was earlier than that of Es3 source rocks. Whether in Es4 or Es3 members, source rocks with the kerogen type I have the greatest oil yield compared with other kerogens. The variations in oil physical and geochemical properties can be attributed to differences in organofacies and thermal maturity, which is in agreement with the results of source rock geochemistry and macerals analysis. Because the concentrations of maturity-related biomarkers or non-biomarkers show several orders of magnitude variation across maturity windows, these maturity parameters do not reliably characterize the maturity signals in mixed oil scenarios. Maturity parameters based on biomarker ratios can produce biases that overemphasize the contribution of low maturity oils and thus track the signals of low maturity end-member oils. In contrast, maturity parameters based on aromatic isomers are more sensitive to mixing processes and reflect the mixing degree of the accumulated oils. The same series of maturity parameters can track hydrocarbon migration pathways and relative maturity, while multiple series of maturity parameters can reconstruct hydrocarbon charging and mixing histories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 teacher head, 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".