Wax Formation from Synthetic Oil Systems and Reservoir Fluids
Bibliographic record
Abstract
In this work, an effort has been made to further improve the predictions of the wax formation conditions based on the previously proposed wax model ( Zuo, J. Y.; Zhang, D.; Ng, H.-J. An improved thermodynamic model for wax precipitation from petroleum fluids. Chem. Eng. Sci. 2001, 56 ( 24), 6941− 6947). The model framework consists of the three-parameter Peng−Robinson equation of state for describing the nonideality of the vapor and liquid phases and the predictive universal quasi-chemical (UNIQUAC) model proposed by Coutinho for the solid (wax) phase. The characterization procedure for plus fractions proposed by Zuo and Zhang ( Zuo, J. Y.; Zhang, D. Plus fraction characterization and PVT data regression for reservoir fluids near critical conditions. SPE 64520, 2000 . ) has been modified and extended to reservoir fluids using high-temperature gas chromatography (HTGC) data. The wax model developed in this study has been applied to predict wax appearance temperature (WAT) and wax cut curves for a number of defined component systems, diesel fuels, and reservoir fluids. The average deviation of the predicted WAT is within 1.5 K at low and high pressures for defined component systems. For reservoir fluids, the predicted thermodynamic WAT locus tends to be much higher than the measured WAT. It is observed that a very small amount of wax precipitated in the fluids could shift the WAT to the measured WAT. The prediction of wax compositions in the solid phase is in good agreement with the experimental data. The results indicate that the proposed wax model is a useful tool to the flow assurance industry.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".