A New Semiautomatic PVT Apparatus for Characterizing Vapex Systems
Bibliographic record
Abstract
Abstract Knowledge of fluid properties is critical to the design of Vapex projects and other enhanced oil recovery processes that use solvent vapor extraction, yet very few pertinent data exist in the published literature. This paper describes a new apparatus for the efficient and accurate measurement of the physical and phase behaviour properties of mixtures of heavy oils and solvents such as propane and butane. The apparatus combines advanced capabilities that make it superior to conventional designs: The automated functions improve the speed at which the data are acquired and reduce operator error. Inline density and viscosity measurements add the capability of multi-phase detection. High-pressure filtration permits measurement of asphaltene and wax precipitation at reservoir conditions. Dual gasometers allow accurate measurements of gas solubility over a wide range of solvents and reservoir pressures. Sub-ambient temperature control makes it suitable for Canadian operations. The apparatus was tested against published measurements for the n-hexadecane-carbon dioxide system, and then used to gather a comprehensive suite of data at two isotherms for a Lloydminster heavy oil-propane system. Random scatter in the resulting data was very small. The equipment is well suited to the acquisition of fluid property measurements for both field design and correlation purposes. With the improvements in both the precision of measurements and the speed of operation offered by the new equipment, it was possible to test some assumptions used in measuring vapor-liquid equilibrium in heavy oil-solvent systems. The results suggested that a noticeable uncertainty may be associated with conventional methods used to determine saturation pressures in these systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".