Estimation of SARA Fraction Properties With the SRK EOS
Bibliographic record
Abstract
Abstract One approach to modelling asphaltene solubility is regular solution theory. The key parameters for this approach are the molar volume and solubility parameters of each constituent. However, these parameters are largely unknown for crude oils. Some authors have used cubic equations of state (CEOS) to estimate the solubility parameters and molar volumes of solvents and C7+ fractions, but CEOS have yet to be applied in this way to asphaltenes due to their high molar mass and unknown critical properties. In this work, a modified Soave-Redlich-Kwong EOS with the Peneloux correction is used to estimate the molar volumes and solubility parameter of the four solubility classes (saturates, aromatics, esins, and asphaltenes) of bitumens. The EOS is modified for the asphaltenes, which are assumed to be polymeric-like compounds consisting of aggregates of monodisperse asphaltenemonomers. Correlations are developed for the critical properties and acentric factor of each solubility class. The EOS-predicted roperties are tested against density measurements of SARA fractions from several bitumens. The predicted parameters areused to determine the onset of asphaltene precipitation from bitumen upon the addition of heptane and the predictions are compared with measured onsets. Introduction Asphaltenes are defined as the crude oil fraction that precipitates upon the addition of an n-alkane (usually n-pentane or n-heptane) but remains soluble in toluene(1). Asphaltene precipitation can occur upon a change in pressure, temperature, or composition and can be a major problem for oil producers. For example, asphaltene precipitation in the reservoir or wellbore, triggered by a drop in pressure, can significantly reduce production. Asphaltene deposition in surface facilities and pipelines can occur upon the addition of condensate diluent. Treatment to remove the deposits increases operating costs. In order to prevent or mitigate asphaltene deposition, it is necessary to predict asphaltene precipitation. To choose an appropriate precipitation model it is necessary to consider asphaltene chemistry. Asphaltenes are mixtures of many thousands of chemical species but these species share some common features. They are polynuclear aromatics and have the highest molar mass, aromaticity, and heteroatom content of all the crude oil components(1). Asphaltenes are also known to self-associate into aggregates consisting of approximately 2 to 6 molecules per aggregate. The aggregates have been considered as colloidal particles(2) or macromolecules(3). With the colloidal view, the associated asphaltenes are considered to form a stack, which is surrounded and dispersed in the oil by resins. Precipitation is believed to occur when the resins are stripped from the colloid allowing aggregation and phase separation to occur. With the macromolecular view, the associated asphaltenes are considered to be independent molecules along with the resins and other crude oil constituents. Precipitation is considered to be a liquid-liquid or liquid-solid phase transition. Recent molar mass and calorimetry experiments favour the macromolecular view(4, 5). Hence, a traditional thermodynamic approach is recommended for asphaltene precipitation. Of the many thermodynamic approaches to modelling asphaltene precipitation(6), the two most prevalent are equations of state(7-9) and regular solution theory(2, 6, 10-12).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".