Identification of Kerogen Type and Recovery of Total Organic Carbon in Prospective Survey on Shale Gas: An Empirical Analysis on Coal-Bearing Blocks in the Junggar Basin
Bibliographic record
Abstract
The US has successfully explored and developed shale gas resources, making shale gas a research hotspot.This paper firstly compares the shale gas research in the US and China, pointing out China should develop a new method for prospective survey on shale gas, rather than copy the American method for shale gas exploration and development.Taking the coalbearing blocks in Junggar Basin as the objects, this paper explores how to classify organic matters, recover the organic matter abundance, and estimate resources in prospective survey areas with weak research foundation and severely weathered outcrop samples.The main findings are as follows: (1) The weathering has an impact on the identification of kerogen type; the type of kerogen should be determined by multiple standards; among the various methods, the maceral method and carbon isotope method are less disturbed by weathering effect.(2) Most of the kerogens of mudstones/shales in Xishanyao Formation, Junggar Basin belong to type II, and only a few belong to type I.The kerogen types are favorable for shale gas generation.(3) The weathering recovery coefficient of the samples in the Junggar Basin was determined, referring to that in regions with similar strata and climate.Besides, the authors also calculated the adsorbed and free gas volumes of East and South Junggar Coalfields.The research results provide a guide for prospective surveys on shale gas in areas with weak research foundation and difficulty in obtaining fresh, low-cost shale samples.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".