Modelling Viscosity and Mass Fraction of Bitumen-Diluent Mixtures
Bibliographic record
Abstract
Abstract The viscosity model is an important component in enhanced oil recovery packages and, for pure bitumen, several accurate models are available. In this study, a simple correlation presented in an earlier publication is extended to predict the viscosity of bitumen-diluent mixtures, as well as the mass fraction required to reduce bitumen viscosity to pumping viscosity. In developing the viscosity model, viscosities of pure bitumen and diluent were used as the endpoints, and the diluent mass fraction was raised to a power of "n" (a viscosity reduction parameter) to account for the sharp drop in bitumen viscosity with ncrease in diluent mass fraction. The model was developed with 99 data points from three different bitumens and five diluents; spanning a viscosity range of 10 -1−1 to 106 mm2/s. The model was used to recalculate the viscosity and mass fraction values, and results compared with similar correlations by Cragoe and Chirinos. The best match was obtained with our correlation, with overall average absolute deviations of 12﹪ and 5﹪ for viscosity and mass fraction predictions, respectively. Predictions on data not used in developing the model showed an excellent match between experimental and predicted values, with an overall average absolute deviation of below 10﹪ for viscosities of mixtures at 25 °CDATA[C, 60.3 °CDATA[C, and 82.6 °CDATA[C. Introduction In the recovery of bitumen, viscosity reduction becomes important, both below and above the ground. The addition of a liquid diluent is thought to break down or weaken the intermolecular forces which create high viscosity in bitumen(1). The effect is so dramatic that the addition of even 5﹪ diluent can cause a viscosity reduction in excess of 80%; thus, facilitating the in situ recovery and pipe line transportation of bitumen. The knowledge of the bitumen-diluent viscosity is highly important, since without it, calculations in upgrading process, in situ recovery, well simulation, heat transfer, fluid flow, and a variety of other engineering problems would be difficult or impossible to solve. This paper presents the development of a simple correlation to predict the viscosity of binary mixtures of bitumen-diluent in any proportion. Experimental The data used for the development of the correlation was TABLE 1: Bitumen data at 30 °CDATA[C. Available In Full Paper. TABLE 2: Diluent data at 30 °CDATA[C. Available In Full Paper. obtained from Wallace et al.(2) and Wallace and Henry(3). The data consisted of a total of 99 points obtained from three bitumens and five diluents, respectively, listed in Tables 1 and 2. Each of these bitumen samples was diluted at 30 °CDATA[C to 5, 10, 25, 50 and 75 weight ﹪ diluent with each of the diluents. After mixing, the samples were reweighed, and any weight loss was attributed to solvent evaporation. The diluent weight fractions were adjusted accordingly, and the viscosities of the mixtures measured. For a detailed account of experimental procedures, refer to Wallace and Henry(3). Correlation Development Many correlations have been developed to predict the viscosity characteristics of bitumen-diluent mixtures(1 -6). While several have been successful in making these predictions, most are cumbersome to use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 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".