Physical characteristics and controlling factors of tight sandstone gas reservoir: A case study of upper Palaeozoic tight sandstone in Shenmu Mizhi area
Bibliographic record
Abstract
Abstract The Shenmu Mizhi area has multi‐layered gas bearing formations, but the pore structure of the tight reservoirs in the area is complex. In this paper, first, the source characteristics of the reservoir in the study area were analyzed, and the reservoir petrological characteristics such as clastic characteristics, rock fragment characteristics, and reservoir filling material characteristics were explored. Second, the pore structure and throat characteristics of the study area were analyzed based on the genesis and distribution of pores, combined with morphology and structure. Third, the pore structure characteristics were elucidated, and the frequency distribution of porosity and permeability of typical rock cores were characterized. Results show the following: (a) The main types of pores are rock debris dissolution pores, followed by intergranular pores. The average facial recognition rate for a Qian 5 section is 2.42%. (b) Except for the Qian 5 section, the average mercury removal efficiency of all other layers is greater than 30%. (c) The porosity distribution range of the Qian 5 section of the Shiqianfeng Formation is 2.8% to 17.3%, with an average value of 8.5% and a main value range of >12%; The distribution range of permeability is 0.001–287.5 × 10 −3 μm 2 , with an average value of 3.31 × 10 −3 μm 2 and a main value range of >1 × 10 −3 μm 2 .
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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 | 0.001 |
| 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".