Characterization of Organic Pores within High-Maturation Shale Gas Reservoirs
Bibliographic record
Abstract
Abstract Shale gas is a very important natural resource which can significantly affect the natural gas production of the world (e.g., in United States, Canada, and China). The organic matter within shale is the material that generates hydrocarbons, such as natural gas, under high thermal maturity. Throughout this process, a significant amount of pores within organic matter, called organic pore, are also generated, as the organic matter itself becomes a porous solid. These organic pores contribute significantly to the overall gas storage and flow of the reservoir as a whole. These organic pores are especially important to the process of gas adsorption and desorption within organic pores, which makes the storage and flow of shale gas very different from conventional reservoirs. The structure of organic pores, including pore volume, surface area, geometry, size distribution, etc., is significant to understanding their influence upon gas adsorption and desorption and gas flow. Thus, it is necessary to quantitatively analyze these organic pores; however, studies that quantitatively analyze organic pore structure in detail are rare. By better understanding characteristics and relationships of these pores through statistical analysis, it is possible to be able to ascertain more characteristics of the reservoir as a whole. Therefore, this research aims to develop the quantitative method to characterize pore structure and discuss the feature of pore structure of high-maturity shale samples from the Sichuan Basin.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".