New Experimental Method for Measuring Gas Diffusivity in Heavy Oil by the Dynamic Pendant Drop Volume Analysis (DPDVA)
Bibliographic record
Abstract
This paper presents a new experimental method and its computational scheme for measuring gas diffusivity in heavy oil at high pressures and elevated temperatures by the dynamic pendant drop volume analysis (DPDVA). In the experiment, a see-through windowed high-pressure cell is first filled with a test gas at a prespecified pressure and temperature. Then, a heavy oil sample is introduced by using a syringe pump to form a pendant drop inside the pressure cell. Due to the oil swelling effect, the subsequent dissolution of the gas into the pendant oil drop causes its volume to increase until the saturation state is reached. The sequential digital images of the dynamic pendant oil drop are acquired and analyzed by applying computer-aided image acquisition and processing techniques to measure the oil drop volumes at different times. A mass-transfer model is developed theoretically to describe the diffusion process of the gas into the pendant heavy oil drop. This model is numerically solved by applying the semidiscrete Galerkin finite element method. The volume of the dynamic pendant oil drop is calculated from the numerically predicted transient gas concentration distribution inside the pendant oil drop. The gas diffusivity in heavy oil and the swelling factor of gas-saturated heavy oil are, thus, determined by finding the best fit of the theoretically calculated volumes of the dynamic pendant oil drop to the experimentally measured data. This novel experimental technique is applied to measure CO 2 diffusivities in a heavy oil sample and the swelling factors of a CO 2 -saturated heavy oil at P = 2, 3, 4, 5, and 6 MPa and T = 23.9 °C.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".