Potential Investigation of Shale Gas Reservoirs, Southern China
Bibliographic record
Abstract
Abstract The shale gas development has more than 3 decades of history. In China, the development is still in the early stage due the technology limitation. The estimated technically recoverable shale gas reserve is 882 Tcf in China. The predicted shale gas production in 2020 is 3.5 Tcf which will contribute 26% of natural gas consumption in China. In order to stabilize the gas output to meet the demand of economic development, increasing recoverable shale gas reserve becomes more and more important. In this study, a Qiongzhusi reservoir was taken as an example to investigate the potential of shale gas in southern China. The methodologies involved in this investigation include experimental study on core analysis with Total Organic Content (TOC) and Vitrinite Reflectance, outcrops study, geostatistical analysis, and geo-modeling. Besides these methods, an irreplaceable step was to compare the reservoir conditions of given reservoir with successful developments in Barnett and Marcellus in the United States. By integrating all above technologies and methods, the study forecasted the sedimentary facies, estimated shale gas distribution, and evaluated the petrophysical properties of the shale play. The result indicates that most of the Qiongzhusi reservoirs have promising thickness, favorable kerogen type (type I), high TOC, and encouraging maturity. This paper also predicted favorable zones in the middle and upper Yangtze area with properties of thickness more than 60 meters, TOC not less than 2.0%, and Vitrinite Reflectance from 2.5% to 4.6%. The study would bring the reservoir engineers and geologists with detailed evidence to support the development of Qiongzhusi reservoirs, and furthermore, it offers the methodology to the research of other shale plays with similar properties.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".