Estimation of concentration‐dependent diffusion coefficients of gases in heavy oils/bitumen using experimental pressure‐decay data
Bibliographic record
Abstract
Abstract The diffusion coefficient (D) of gases in heavy oils is an important mass transfer parameter to model and design gas injection processes for oil recovery. The pressure‐decay technique (PDT) is one of the widely used experimental methods available to infer this coefficient. PDT records the declining gas phase pressure resulting from the diffusion of gas into heavy oil inside a pressure/volume/temperature (PVT) cell. Commonly, the gas phase pressure decay is modelled by use of Fick's second law along with gas‐phase mass balance equations and assuming a constant diffusion coefficient. In this work, we evaluate two concentration‐dependent diffusion coefficient functions, power‐law and exponential. A simple history matching technique is used to estimate the apparent diffusion coefficient (Do) and concentration dependency factor (ma) from pressure‐decay data. Extensive application of our method to experimental pressure‐decay tests shows that in addition to constant diffusion coefficients, both power‐law and exponential functions are capable of predicting the experimental data. This implies that other experimental techniques are required to extract the functionality of diffusion coefficients with gas concentrations in heavy oil.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".