Novel Method for the Classification of Shale Oil Reservoirs Associated with Mobility: Inspiration from Gas Adsorption and Multiple Isothermal Stage Pyrolysis
Bibliographic record
Abstract
The characterization and description of pores in shale oil reservoirs have long been based on the classification of micropores (<2 nm), mesopores (2–50 nm), and macropores (>50 nm) in shale gas reservoirs. However, the great difference between oil and gas molecules leads to its poor applicability, which further results in the relationship between oil properties and pores being rarely understood. To establish an individual pore division method for shale oil reservoirs, N 2 adsorption, Soxhlet extraction, and programmed pyrolysis were performed on the Paleogene Xin’gouzui lacustrine shale in the Jianghan Basin. With the results, a new classification method, i.e., adsorption pores (pore diameter <20 nm), restricted pores (20–100 nm), and movable pores (>100 nm), was proposed. A fractal theory and scanning electron microscopy (SEM) images confirm the rationality of this method. Hydrocarbons in movable pores determine the initial productivity of a shale oil well, while those in restricted pores are closely related to the production time of a well to some extent. Compared to the previous division methods, this novel method reveals the correlation between shale oil attributes as well as movability and pore spaces for the first time. Our work can provide more accurate evaluation results for shale oil recoverable resource potential and is of great significance for optimizing favorable areas and reducing exploration risks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".