Critical Evaluation of Existing Methods for Accounting for Multiphase Effects Around Producers in Depleting Gas Condensate Reservoirs
Bibliographic record
Abstract
Abstract Well test analysis techniques use variations of the pseudopressure concept to account for multiphase effects due to condensate drop-out in near-well regions (e.g. Jones and Raghavan1 approach). In this work, an evaluation of the accuracy and applicability of these techniques was carried out using data from the discovery well of the Cupiagua field, including well testing, relative permeabilities (Kr) measured at low capillary number and a tuned PREOS. Henderson et al's capillary number dependent relative permeability and effective inertial resistance correlations2, as implemented in a commercial simulator, were used to history match the well test data. To investigate the assumptions in the analytical models, numerical simulations were run, first with constant Kr, and then with the effect of capillary number and inertial resistance. The main conclusions are: For the constant Kr simulation, a constant composition expansion (CCE) data of the flowing composition accurately reproduces the pressure-saturation relationship in the region where both oil and gas are mobile. However, contrary to Fevang and Whitson's assumption3, the constant volume depletion (CVD) data did not match the saturation distribution of the region where only gas flows.When Kr dependence on capillary number and inertial resistance was considered, the immobile oil region practically disappeared. In consequence, the CCE data of the original fluid composition can reproduce the pressure-saturation relationship in the two phase region.Al-Hussainy et al's total skin versus rate plots4 to obtain mechanical skin figures were found to give results similar to those obtained by numerical simulation. In summary, this work identifies clearly the conditions under which the analytical models are not correct, which data should be used for analysis, and when the numerical instead of an analytical approach is required.
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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 | 0.004 | 0.014 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".