Measurements and Modelling of Phase Behaviour and Viscosity of a Heavy Oil-Butane System
Bibliographic record
Abstract
Abstract Solvent-based heavy oil recovery methods are of interest as environmentally friendly alternatives for thermal techniques. The phase behavior of the heavy oil/solvent system is crucial required information for feasibility studies and numerical simulation of these processes. Currently the scarcity of experimental data for such systems in the literature is a barrier to the numerical simulation studies of solvent based processes. The variety of the solvent/oil mixtures, which are being evaluated within the ongoing research related to the VAPEX process, requires accurate description of the system's PVT properties. In this study, an experimental set-up was designed and experiments were performed to obtain the required PVT information. The results of the PVT experiments conducted with the Frog-Lake heavy oil/butane system are presented. This solvent/oil pair was used in the VAPEX experiments reported previously by the authors (Yazdani and Maini, 2005, 2006). The experimental measurements included the solvent fractions in the oil, mixture density and mixture viscosity at different saturation pressures. The PVT results were modeled using CMG's phase behavior package (WINPROP) and an equation of state (EOS) was tuned for simulating the experimental behavior of the system. The predicted values of EOS for density and saturation pressure are in very good agreement with the obtained experimental data. The viscosity measurements were compared with the predictions of several available correlations. A mixingtype relationship was found to be adequate for describing the viscosity of heavy oil – solvent mixtures. Introduction Solvent based recovery processes have recently gained some attentions. However, numerical simulation studies of these processes are required to investigate the feasibility of these methods to be practically implemented in the oil fields. Numerical simulation of these processes is mostly performed using a compositional simulator due to the potential compositional changes, asphaltene precipitation and presence of diffusion/dispersion mechanisms during the process. One of the most important input data for every compositional simulator is the phase behavior of the heavy oil-solvent system. To build a realistic equation of state model it is necessary to obtain reliable experimental PVT information. However, there are not adequate and currently available experimental data for different heavy oil-solvent pairs. Characterization of the oil, in terms of pseudo-components, is another important task in PVT modeling, which becomes even more challenging when one deals with heavy oil or bitumen. Most of the popular equations of state have been developed and tested with the characteristics of the light and moderate viscosity hydrocarbons and there are only few reported verifications of the EOS with heavy oil or bitumen. Heavy molecules of asphaltene add more complexity to the characterization of these types of oils with the currently used phase behaviour software. The available laboratory characterization methods such as simulated distillation techniques are not able to characterize all of the heavy molecules of the oil and this becomes a serious issue in lumping and/or splitting the oil in different pseudo-components using PVT packages. In this paper, the experimental measurements of the phase behavior data for a heavy oil-solvent system are presented.
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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) | 0.000 | 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".