Study on the Production Decline Laws for Vertical Fracture Well in Low-Permeability Gas Reservoirs
Bibliographic record
Abstract
The vertical fracture was asymmetrical about the wellbore or two wings of a fracture are not certainly in a line for the complex geo-stress in the possession of fracturing of the gas reservoirs. In view of the low permeability reservoir after fracturing developing the asymmetrical vertical fracture and non-coplanar fractures, based on the non-steady seepage theory, using the potential function theory, superimposition principle and numerical analysis method, a performance prediction model for the vertical fracture in low-permeability gas reservoirs was deduced with pressure drop superposition principle. The production decline laws were analyzed by practical cases. The result shows that the initial production of the vertical fracture is relatively high but soon followed by a sharp decline. Then, the production keeps in a relatively stable state and declines slowly in the middle and later. The fracture asymmetry factor has little effect on the gas well productivity. The non-coplanar angles have greater effect on the oil well productivity in the initial stage. The more the fracture length and the bigger the flow conductivity, the higher the oil well production and the faster the decline rate will be. However, the increase amplitude will be getting smaller and smaller along with the fracture length and flow conductivity.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".