Developing a New Scaling Equation for Modelling of Asphaltene Precipitation
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Bibliographic record
Abstract
Abstract Many recent investigations showed that the prevalent thermodynamic models are incapable of predicting asphaltene precipitation without extensive data fitting. This is primarily due to lack of knowledge of the asphaltene properties, its complex nature and the large number of parameters affecting precipitation. Therefore, many authors tried to generate a simple and universe mathematical model in order to predict the amount of asphaltene precipitation. In spite of these efforts, the authors only considered temperature and type of solvents as the effective parameters in generating their scaling equations. The major disadvantage of these models is that they cannot predict the amount of asphaltene precipitation for different crude oils. Therefore, this deficiency contradicts to the universality of these models. In this work by performing experimental activities on different crude oils and analyzing their properties such as live oil GOR, Resin to Asphaltene ratio, mole percent of plus fractions and residual oil density, a new scaling equation developed in order to predict asphaltene precipitation for different oil samples. As far as this scaling equation has been generated using different samples, it can be used to estimate the amount of precipitated asphaltene at different dilution ratios and the onset dilution ratio of precipitation. It should be noted that various literature precipitation data validated the predictive capability of this new scaling equation. Introduction Asphaltene precipitation is one of the most common problems in both oil recovery and refinery processes. In oil recovery, especially in gas injection, formation of asphaltene aggregation, following their deposition causes blocking in the reservoir. This makes the remedial process costly and sometimes uneconomical. The amount of asphaltene precipitated is a crucial factor for determining the degree of permeability reduction of the reservoir rocks. It is essential to know how much asphaltene precipitates as a function of temperature, pressure and liquid phase composition. Equation (1) (Available in full paper) Equation (2) (Available in full paper) Unfortunately, there is no predictive model for asphaltene problem treatment. Hence it is necessary to predict the onset of asphaltene precipitation, as a pre-emptive measure. The major questions in facing such problems are "When" and "How much" heavy organic compounds will precipitate in operational condition. Over the years, many researchers have tried to find the answer. They introduced experimental procedures or even analytical models, but a fully satisfactory interpretation is still lacking. The problem is very difficult mainly because of the fuzzy nature of asphaltene and the large number of parameters affecting precipitation. The existing models fall into three classes: (I) Molecular thermodynamic models in which asphaltenes are dissolved in crude oil and crude oil forms a real solution[3–7]. The validity of such models depends on the reversibility of asphaltene precipitation. In principle, only if this phenomenon is reversible, one can use such models. Reversibility experiments strongly support this type of models [3,8–10]. (II) Colloidal models in which, asphaltene is suspended in crude oil and peptized by resins. The asphaltene precipitation is irreversible in such models [11–13]. Reversibility experiments are strongly against this type of models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it