PVT and Viscosity Measurements for Lloydminster-Aberfeldy and Cold Lake Blended Oil Systems
Bibliographic record
Abstract
Abstract In the Solvent-Assisted Processes Project of the AACI Research Program, many experiments were done to evaluate the feasibility of using light hydrocarbon and other solvents as agents for recovery of heavy oil and bitumen. In order to have a rational basis for designing these experiments, measurements of gas solubility in the oil at operating conditions are needed. To predict the behaviour of the process by numerical simulation, a set of k-values for the relevant gas-liquid systems is needed. Simple analog models of the Vapex process require the viscosity of the oil-solvent blends at equilibrium conditions. A data bank of oil-solvent mixture viscosity and solubility is useful for reference purposes or for developing correlations. Measurements were done on a blended Cold Lake/Lloydminster, and on Lloydminster Aberfeldy oil. The gasses used were CH4, C2H8, C3H8 and CO2. Measurements were done at reservoir temperature. The data were regressed using the Peng-Robinson Equation of State. The equation was then used to generate k-values for the gas-oil systems. Regression was by varying the interaction coefficienst for the various gas-oil systems. These coefficients enabled use of the equation to generate k-value tables for other conditions. Measured viscosity data were used to confirm the usefulness of the Puttagunta equation for calculating the viscosity of oil-solvent mixtures. The work also confirmed the formation of 2 liquid phases in the oil-propane system at high solvent loading. An anomaly in the viscosity curve at high solvent loading indicated possible asphaltene precipitation/deposition in the viscometer tube for propane-oil systems. Measurements confirmed the high viscosity reduction possible (100:1 - 200:1) by saturating light oil with hydrocarbons. The observations confirmed the need for an integrated PVT/viscosity/asphaltene study for oil/solvent systems intended for a Vapex process. The data have been applied to numerical simulations of these experiments and proposed field processes. Introduction Thermal recovery processes have been used successfully on many Alberta bitumen and heavy oil reservoirs. Some reservoirs, however, are not suited to thermal processes. This may be due to depth, unfavourable mineralogy, bottom water, thin pay sections, or a combination of these factors. For these reservoirs, a non-thermal process may be more suitable. The most likely candidate is a Vapex-type process, where oil is contacted by solvent vapour. The vapour dissolves in the oil, and diluted oil drains to a production well. The application of this technology to heavy oil recovery requires confident prediction of the process performance for a field-scale operation. This in turn requires knowledge of the mechanisms active in the process and the magnitude of each of these mechanisms. Mechanisms identified to date include solubilization of the solvent in oil, mass transfer from vapour to liquid phases by diffusion, mixing of diluted and undiluted oil by diffusion and dispersion, reduction of the oil viscosity by solvent dilution, and upgrading of the oil by asphaltene precipitation and deposition. This work measured solubility and viscosity of several oil-solvent systems. DESIGN OF EXPERIMENT The experiment was performed in a PVT apparatus constructed from standard components. Figure 1 illustrates the PVT system and its associated hardware.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".