Application of Two-phase Pseudo-pressure in Gas-condensate Well-testing With and Without Positive Coupling and Inertia
Bibliographic record
Abstract
Well-test interpretation in gas condensate system is particularly challenging when condensation happens within the reservoir. This is not only due to the natural reduction of near wellbore mobilities, but also the existence of the velocity effects (i.e. positive coupling and inertia), which compete to revamp the relative permeabilities. In this study, a set of realistic build-up tests are generated using a compositional reservoir simulator, where measured (velocity dependent) relative permeabilities and a real gas condensate fluid model are used. The transient build-up tests are analysed using the real-gas and steady-state two-phase pseudo-pressures, and the (velocity dependent) reservoir integral transforms. The results show that the application of steady-state two-phase pseudo-pressure transform can result in a remarkable over-prediction of the reservoir permeability, when the velocity dependent relative permeabilities are in effect. Moreover, the traditional real-gas pseudo-pressure transform fails to estimate the reservoir properties particularly when the reservoir is initially below the dew point pressure. However, in either of situations (i.e. with and without velocity effects), using the reservoir integral transform leads to an excellent liquid analogy solution, where the reservoir properties can be accurately estimated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".