An Integrated Horizontal- and Vertical-Flow Simulation With Application to Wax Precipitation
Bibliographic record
Abstract
Summary There is a lack of comprehensive simulation tools that (a) accommodate the complexities of advanced completions together with near-wellbore behavior and that (b) have reliable wax-precipitation models for production planning. In this work, these issues are tackled by combining three specific models. First, a steady-state, three-phase, nonisothermal flow model in advanced horizontal completions was implemented to run fluid-specific simulations, thereby calculating field-specific flow conditions. This is useful in situations when fluid-specific temperature calculations are important, such as wax crystallization. Second, a nonisothermal, vertical flow model was developed by combining Hagedorn and Brown's multiphase-flow correlation with Ramey's multiphase-temperature model by solving them in sequence (iteratively). The advanced horizontal-well model and vertical flow model were coupled iteratively at the bottom hole where the two models meet. Third, two different analytical wax-crystallization models were incorporated in the aforementioned coupled flow simulator to calculate the location of wax precipitation along the vertical section of the well. These three simulation models, individually and in combination, were tested and found to be in par with theory, expectations, and published results. In addition, a significant difference was noted between Ramey's analytical temperature profile (which is a widely used approximation) and the complete Ramey's model integrated with the simulator developed in this work.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".