Combining Paleomagnetic and Re‐Os Isotope Data to Date Hydrocarbon Generation and Accumulation Processes
Bibliographic record
Abstract
Abstract Unraveling the complex relationship between orogenesis and hydrocarbon formation and accumulation is challenging and is often hampered by physical and chemical overprints of younger events. The Permian reservoir in the Longmen Shan orogen, South China, is such an example, and its evolution has been hotly debated. In this study, we use a new combination of paleomagnetic dating analysis and Re–Os isotope dating to try to resolve this. Paleomagnetic dating of the hydrocarbon‐host carbonate indicates two remagnetization events during: (a) the Late Triassic, and (b) the Middle Jurassic–Cretaceous. These two remagnetization events are shown to represent two distinct stages of hydrocarbon accumulation. The paleomagnetic estimates are supported by Re–Os dating of bitumen (∼264 Ma) and oil (∼94 Ma). The two different Re–Os ages are associated with two periods of oil generation. We interpret these data in terms of known geological processes: (a) the ∼260 Ma Dongwu large igneous province caused oil generation, and the Indosinian tectonic event caused the migration and accumulation; and (b) the Late Cretaceous Yanshan orogenic events promoted another generation and entrapment of oil in the same reservoir. This combined approach reliably tracks the sequence of oil generation and accumulation, even when the source rock is uncertain, and multi‐phase accumulation and complex tectonism has occurred. Given that paleomagnetic and Re–Os dating are independent methods which can constrain multiple geological processes, when used together they have the potential to be universally applied.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".