Measurement and Modelling of Asphaltene Flocculation From Athabasca Bitumen
Bibliographic record
Abstract
Introduction Heavy crude oil production is increasing as conventional oil supplies are depleted. However, heavy oils are rich in asphaltenes, which can precipitate, flocculate, and deposit during transportation and processing. Current methods of treating the deposit are often only partially effective. In order to better mitigate asphaltene deposition, a better understanding of asphaltene precipitation and flocculation is required. This project focuses on the formation and flocculation of asphaltene particles in solutions of n-heptane and toluene at 23 °C and atmospheric pressure. Heptane and toluene were selected because toluene is a good solvent for asphaltenes whereas asphaltenes precipitate in heptane. Hence, mixtures with different proportions of precipitated asphaltenes could be investigated at different solvent conditions. EXPERIMENTAL METHOD Athabasca coker-feed bitumen was obtained from Syncrude Canada Ltd. Toluene and n-heptane were obtained from Aldrich chemical Company with 99%+ purity. Asphaltenes were precipitated from the bitumen with the addition of n-heptane at a 40:1 volume ratio of heptane-to-bitumen and non-asphaltenic solids were removed by centrifugation. Details of the precipitation are provided elsewhere [7]. To prepare a solution of asphaltenes in heptane and toluene, the asphaltenes were first added to toluene and sonicated for 1 hr at 23 °C to ensure that all the asphaltene dissolved. Asphaltene precipitation was induced by the addition of n-heptane in 60:40 and 70:30 n-heptane: toluene volume ratios. Asphaltene concentrations of 0.05, 0.08, and 0.1 kg/m_ were considered. The growth of asphaltene floccs was observed over 6 hours using a Brinkmann 2010 particle size analyzer. The particle size distribution is determined from the time of transition of the particles (or flocs) through a laser [5]. Samples were placed in standard 1 cm × 1 cm square optical-glass cuvettes obtained from Hellma cells Inc. A three-speed magnetic stirrer was employed to disperse the asphaltene particles within the cuvette. FLOCCULATION MODEL The probability of flocculation is a combination of the probability of a collision (characterized by diffusion time, τdiff) and the probability of a collision resulting in flocculation (characterized by reaction time, τrxn). In well mixed systems, τdiffdiff < < τrxn and flocculation is reaction-limited. In this limit, particles may collide with each other numerous times before they actually react (flocculate). The problem can then be approached using classical rate equations of the form: Equation (1) (Available in full paper) where Ni denotes a floc consisting of i individual particles. Cluster-cluster addition is the dominant mechanism in reaction-controlled flocculation. Flocculation is opposed by fragmentation, which can be a combination of surface erosion and shattering [3]. For a monodisperse distribution of individual particles, the derivative with respect to time of the number concentration, nk, of flocs of diameter dk is then given by: Equation (2) (Available in full paper) where Fi, j, Si and Ei are the number of reactions per unit volume per unit time that result in flocculation, shattering or surface erosion processes, respectively. The reaction terms are defined as follows: Equation (3) (Available in full paper)
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".