Non-Linearity Seepage Productivity Model and Influential Factors' Analysis in Tight Sandstone Gas Reservoir
Bibliographic record
Abstract
The tight sandstone gas is the most precious unconventional natural gas resource which has massive reserves all over the world. However, poor formation physical properties, extremely lower permeability, and complex pore-throat structure make it difficult to effective displacement in the tight gas formation. As a result, fracturing of horizontal wells is an effective technique for the tight gas. Based on the natural gas non-linearity unsteady seepage theory, the pseudo-pressure pattern and the overlay principle, this paper sets up the fractured horizontal well productivity model in the tight sandstone gas reservoir, which takes fracture interferences into consideration. Combined with the productivity model above, the relation curves between cumulative gas production and different factors have been drawn, and the sensitivity analysis of productivity influential factors has been carried on as well. Research shows that: the best length of horizontal well is 900 m and the corresponding optimal number of fractures is 6, while the optimal half-length of the fracture is 80 m. The length of horizontal well is the most sensitive influential factor to the productivity, while other factors are half-length of the fracture and the fracture conductivity in turn. Seeing from the sensitivity analysis curve, the fractured horizontal well productivity is not sensitive to fracture conductivity in tight gas formation. The study has an important guiding significance to productivity prediction and parameters optimization of fractured horizontal wells in the tight sandstone gas reservoir. Key words: Tight gas; Non-linearity seepage; Productivity prediction; Fractured horizontal well
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".