A New Experimental Technique for Studying Gas Mass Transfer in the Crude Oilby Analysis of the Measured Dynamic and Equilibrium Interfacial Tensions
Bibliographic record
Abstract
This paper presents a new experimental technique and its computational scheme for studying gas mass transfer in the crude oil at high pressures and elevated temperatures by analysis of the measured dynamic and equilibrium interfacial tensions. In the experiment, a see-through windowed high-pressure cell is prefilled with a test gas at a prespecified pressure and a constant temperature. Then, a crude oil sample is introduced by using a specially designed syringe delivery system to form a pendant oil drop inside the pressure cell. Due to the dissolution of the gas into the pendant oil drop, the dynamic interfacial tension between the test gas and the crude oil keeps reducing and eventually reaches its equilibrium value when the saturation state is achieved. 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 dynamic interfacial tensions at different times. Theoretically, a mathematical mass transfer model is formulated to describe the diffusion process of the gas in the pendant oil drop. This model is numerically solved by applying the semi-discrete Galerkin finite element method to obtain the transient gas concentration distribution inside the pendant oil drop. With a pre-determined calibration curve of the equilibrium interfacial tension versus the equilibrium gas concentration in the crude oil, the dynamic interfacial tension at any time is calculated. The mass transfer Biot number and the gas diffusion coefficient are thus determined by finding the best fit of the theoretically calculated dynamic interfacial tensions to the experimentally measured data. This newly developed experimental technique is applied to measure the mass transfer Biot number, the diffusion coefficient, and the interface mass transfer coefficient of CO2 in a reservoir oil sample at P=0.1~5.0 MPa and T =27°C.
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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".