Geochemical characteristics and analysis of crude-oil source in the deep-water area of the Baiyun Sag, South China Sea
Bibliographic record
Abstract
Abstract In the Baiyun Sag, the oil relationship with possible source rocks is complicated, and little research on oil sources in the area has been performed. In this paper, geochemical studies of carbon isotopes, biomarkers, paleoenvironmental deposition, and crude oil maturity, carried out with the use of oil samples from the Baiyun Sag, demonstrate that the organic matter in crude oil has an oxidizing depositional environment and primarily lacustrine facies conditions, with a significantly higher plant input. The oil samples used are mature to highly mature. Crude oil in the northern area of the Baiyun Sag is more mature than samples in the eastern part of the sag. Despite limited source rock data, we were able to conclude that the crude oil samples were derived mainly from the Enping Formation; other potential source rocks of the Wenchang and Zhuhai Formations may also have contributed to the reservoirs in different parts of the sag. Furthermore, the source of crude oil in each structure in the Baiyun Sag was analyzed; for example, crude oil in the LH19-5 structure was generated from the organic matter of the Enping Formation, and crude oil in the LW3-1 structure was derived from the mixed organic matter of the Enping, Wenchang, and Zhuhai Formations. © 2018, V.S. Sobolev IGM, Siberian Branch of the RAS. Published by Elsevier B.V. All rights reserved.
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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 | 0.000 |
| 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".