Numerical simulation and three-phase pressure transient analysis considering capillary number effect – case study of a gas condensate reservoir
Bibliographic record
Abstract
When the wellbore pressure drops below the dew point pressure of gas in a gas condensate reservoir, there is the possibility of condensate bank build-up and wellbore blockage. These adverse processes result in development of different mobility zones around the wellbore which complicates the pressure transient test analysis. The focus of this study is on the analytical and numerical analysis of pressure testing data obtained from a well in a target gas condensate reservoir. First, the well test data were analytically interpreted through which several well and reservoir parameters were identified such as condensate bank radius, gas effective permeability, mechanical skin and skin due to non-Darcy flow. A three-phase compositional reservoir model was then built using the analytical solution, for numerical analysis of the pressure transient data as well as validation of the analytical results. This numerical model was then used to estimate well deliverability and predict future reservoir performance. [Received: August 17, 2018; Accepted: March 15, 2019]
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".