Hydrocarbon migration in the multiple-sourced petroleum system in the northwestern Junggar Basin (northwestern China): Constraint from geochemical and phase fractionation analysis
Bibliographic record
Abstract
ABSTRACT Oil mixing and absence of source-rock samples make it difficult to identify the hydrocarbon migration routes in a petroleum system. We provide a case study in the northwestern Junggar Basin to show that combined geochemical and phase fractionation analyses are robust tools to unravel the complex hydrocarbon migration processes. The study rebuilds a migration history that multiple-sourced hydrocarbons migrated, mixed, accumulated, and fractionated along the evolution of regional tectonics. In detail, the Shawan and Mahu sags expelled the early-stage hydrocarbons during the Late Triassic and Late Jurassic, respectively, because of their variable subsidence. These hydrocarbons charged the entire area based on the evidence from bitumen and oil inclusions. During the Early Cretaceous, both sags subsided rapidly and expelled their late-stage hydrocarbons. These hydrocarbons first mixed along unconformities in the sags, which generated mixed-source oils and induced gas washing. Subsequently, they further mixed with or displaced the encountered early-stage oils during migration along the basal unconformity of the upper Permian into the area, causing a horizontal distribution of oil maturity zones. In addition, gases flowing through the early-stage oils induced gas washing again, creating heavy oils, condensate oils, and mixed gases. After the late-stage oils finally accumulated in fractured volcanics, migration fractionation caused the remigration of light-end compositions. This study also shows the strong control of structures on hydrocarbon migration: the unconformity network provided opportunities for long-distance migration and widespread mixing of multiple-sourced hydrocarbons, whereas the paleoridge line of the Zhongguai high defined the boundary of regional migration.
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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 | 0.001 | 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".