Petroleum system evaluation of the world’s deepest (>5000 m) volcanic reservoirs, Sichuan Basin, China
Bibliographic record
Abstract
ABSTRACT Large-scale hydrocarbon accumulations have rarely been found in volcanic reservoirs around the world. This study documents large gas accumulations in Permian reservoirs in two areas of the Sichuan Basin, southwestern China, that include the deepest (>5000 m) volcanic reservoirs yet identified worldwide. Petroleum system elements differ in these deep volcanic accumulations between the Jianyang and Zhougongshan areas within the basin. In the Jianyang area, natural gas was derived mainly by the cracking of oil from Cambrian source rocks. Accumulations are overpressured stratigraphic traps spatially controlled by the distribution of a welded tuff reservoir with high porosity and high permeability. In contrast, low-porosity and low-permeability welded lapilli tuff and welded breccia reservoirs have lower gas saturations and are not productive. In the shallower Zhougongshan area, gas was generated by the cracking of kerogen in middle Permian source rocks. Regional tectonism formed normally pressured structural traps in fractured basaltic reservoirs. Multistage tectonism likely breached seals and allowed oil and gas to escape, reducing the scale of the accumulations in this area. Key factors influencing the large-scale accumulation and preservation of natural gas in the study area include development and preservation of adequate reservoir quality as well as tectonic history favorable to maintain seal integrity in brittle rocks. Our results provide guidance for the exploration for similar reservoirs in other areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".