Geology, resource potentials, and properties of emerging and potential China shale gas and shale oil plays
Bibliographic record
Abstract
Abstract This paper describes the geology of organic-rich shales in China, their resource potentials, and properties of emerging and potential China shale gas and shale oil plays. Marine, lacustrine, and coastal swamp transitional shales were estimated to have the largest technically recoverable shale gas resource (25.08 trillion cubic meters or 886 trillion cubic feet) and 25 to 50 billion barrels of technically recoverable shale oil resource. The Precambrian Sinian Doushantuo Formation to Silurian Longmaxi black marine shales mainly accumulated in the intrashelf low to slope environments in the Yangtze Platform in South China and in the Tarim Platform in northwest China. The marine shales in the Yangtze Platform have high maturity (Ro of 1.3%–5%), high total organic carbon (mainly >2%), high brittle-mineral content, and have been identified as emerging shale gas plays. The Lower Paleozoic marine shales in the Upper Yangtze area have the largest shale gas potential and currently top the list as exploration targets. The Carboniferous to Permian shales associated with coal and sandstones were mainly formed in transitional depositional settings in north China, northwest China, and the Yangtze Platform in south China. These transitional shales are generally rich in clay with a medium level of shale gas potential. The Middle Permian to Cenozoic organic-rich lacustrine shales interbedded with thin sandstone and carbonate beds are sporadically distributed in rifted basins across China. Their main potentials are as hybrid plays (tight and shale oil). China shales are heterogeneous across time and space, and high-quality shale reservoirs are usually positioned within transgressive systems tract to early highstand systems tract intervals that were deposited in an anoxic depositional setting. For China’s shale plays, tectonic movements have affected and disrupted the early oil and gas accumulation, making tectonically stable areas more favorable prospects for the exploration and development of shale plays.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".