Productivity Model for Water-Producing Gas Well in a Dipping Gas Reservoir With an Aquifer Considering Stress-Sensitive Effect
Bibliographic record
Abstract
The development process of a dipping gas reservoir with an aquifer considering stress sensitivity is complex. With gas development, formation pressure decreases, stress-sensitive effect decreases permeability and porosity, and formation water could flow into the development gas well and gather in the wellbore. The accumulation of water may lead to a lower gas rate. Simultaneously, the gravity action of fluid caused by formation dip angle affects gas well productivity. However, few studies have investigated a deliverability model for a water-producing gas well with a dipping gas reservoir considering stress sensitivity. For this reason, it is important to determine the relationships between gas well productivity and stress sensitivity, formation angle, and water production. In this research, a new mathematical model of deliverability was developed for a water-producing gas well with a dipping gas reservoir considering stress sensitivity. Additionally, a new equation was developed for gas well productivity. By analyzing a typical dipping gas reservoir with an aquifer, the level of influence on gas well productivity was determined for stress sensitivity, formation angle, and water–gas ratio (WGR). The work defined the relationships between gas well productivity and stress sensitivity, formation angle, and WGR. The results indicate that deliverability increases with an increase in formation angle, and growth rate hits its limit at an angle of 40 deg. Due to the influence of formation angle, fluid gravity leads to production pressure differences in gas wells. When bottom-hole flow pressure equaled formation pressure, gas well production was not 0 × 104 m3/d, the angle was large, and gas well production was greater. Deliverability and stress sensitivity hold a linear relationship: the stronger the stress sensitivity, the lower the deliverability of the gas well, with the stress sensitivity index from 0 to 0.06 MPa−1 and the deliverability decrease rate at 37.2%. Deliverability and WGR hold an exponential relationship: when WGR increased from 0.5 to 15.0 m3/104 m3, the deliverability decrease rate was 71.8%. The model and the equations can be used to predict gas deliverability in a dipping gas reservoir with an aquifer considering stress sensitivity. It can also be used to guide the development process for a dipping gas reservoir with an aquifer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".