A Semi-Analytical Model for Predicting Horizontal Well Performances in Fractured Gas Reservoirs With Bottom-Water and Different Fracture Intensities
Bibliographic record
Abstract
Numerical simulation and prediction studies on horizontal well performances in gas reservoir are foundation for optimizing horizontal well completion process. To gain more understanding on this theory, a steady-state reservoir model coupling with wellbore is developed in the fractured gas reservoirs with bottom-water and different fracture intensities to predict the horizontal well performances. Based on the equivalent flow assumption, the fractured porous medium is transformed into anisotropic porous medium so that the gas reservoir flow model can be developed as a new model that incorporates formation permeability heterogeneity, reservoir anisotropy, and gas reservoir damage. The wellbore flow model which considers pressure drops in the tubing is applied. We compare this paper model solutions for inflow profile along the well to the numerical solutions obtained from a commercial simulator (ECLIPSE 2011), and the result shows a very good agreement. Moreover, sensitive analysis, in terms of various linear densities of fractures, matrix permeability, fracture width, and wellbore pressure drop, is implemented. The results show that the new model developed in this study can obtain a more practical representation to simulate the horizontal wells performance in fractured gas reservoir with different fracture intensities and bottom-water, thus can be used to optimize the parameters in horizontal well completion of fractured gas reservoirs with different fracture intensities and bottom-water.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".