Analysis of Microscopic Main Controlling Factors for Occurrence of Movable Fluid in Tight Sandstone Gas Reservoirs Based on Improved Grey Correlation Theory
Bibliographic record
Abstract
Tight sandstone reservoirs have the characteristics of poor physical properties, fine pore throats, and strong microheterogeneity compared with conventional reservoirs, which results in complicated movable fluid occurrence laws and difficult mining. Taking the tight sandstone gas reservoir of He 8 formation in Sulige gas field as an example, based on physical property test analysis, constant velocity mercury injection, and nuclear magnetic resonance experiments, an optimized gray correlation calculation model is established by improved gray correlation theory, which quantitatively characterizes the influence of microscopic pore structure parameters of different types of tight sandstone gas reservoirs on the occurrence of movable fluids, and the main controlling microgeological factors for the occurrence of movable fluid in tight sandstone gas reservoirs with close/similar physical properties are selected. The results show that the occurrence of movable fluid in Type I reservoirs is mainly affected by the effective pore-throat radius ratio, the saturation of mercury in the total throat, and the effective pore radius, and the occurrence of movable fluid in Type II reservoirs is mainly affected by the effective throat radius per unit volume and total throat mercury saturation and mainstream throat radius. Moreover, the occurrence state of movable fluids in Type II reservoirs is controlled by the throat radius stronger than that of Type I reservoirs. It has important guiding significance for the efficient development of tight sandstone gas reservoirs.
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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 | 0.001 |
| 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".