Effect of Varying Permeability on Inflow Performance Curve of Gas Reservoirs
Bibliographic record
Abstract
Abstract An inflow performance relationship (IPR) curve is a mathematical tool used to predict the production of the well throughout its lifetime. This curve is plotted for bottom hole flowing pressure against production rate. An inflow performance curve is presented considering the gas reservoirs where permeability alteration due to pressure is introduced. Results are compared with conventional curves which are based on constant permeability. The new approach considers the permeability of the reservoir as a function of pressure. In addition, modified Kozeny-Carman equation is employed to take care of the variation in porosity, Cementation factor, Shape factor, Lithology factor. The proposed approach considers the porosity and cementation factor as a function of pressure based on compressibility of the rock matrix. The numerical solution shows the over prediction of the gas flow rate with reducing bottom hole pressure, by conventional IPR based on constant permeability. The pseudo pressure equation is considered and the conventional IPR curves are compared with the two proposed models. The first model considers only the porosity as a function of pore pressure whereas the second model considers both porosity and cementation factor as a function of pore pressure. For validation, two field case studies are presented for vertical wells in a gas reservoir. Gas flow rates predicted by suggested models are same as that of conventional IPR at average reservoir pressure. On contrary, the IPR curves for both models shifted left due to the low gas flow rates obtained. The most significant finding is that porosity has a greater effect on permeability compared to the cementation factor and thus on IPR curve. The first model considers only the effect of porosity on pressure, shows significant deviation in IPR curve due to a decrease in flowing bottom hole pressure resulted due to production from the gas reservoir. The second model considers both porosity and cementation factor as a function of pressure is more accurate and recommended for inflow performance predictions.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".