Prediction of Effective Diffusion and Dispersion Coefficients, Considering Different Flow and Heterogeneity Properties of Porous Media
Bibliographic record
Abstract
Abstract Heavy oil and bitumen are important hydrocarbon resources that are destined to play an increasingly significant role in the oil supply of the world. The total heavy oil resources are estimated to be about 10 trillion barrels, nearly three times the conventional oil in place in the world. The efficient and economic recovery of heavy oil and bitumen reserves is a crucial technical challenge. Most of these reserves lie deep inside the earth's crust and are not easily recoverable owing to their very high viscosity. Hydrocarbon solvent-based processes are thought to be effective EOR technologies for heavy oil and bitumen production. An important feature of the miscible fluid displacement is the mixing of the solvent and oil. This mixing occurs on the microscopic scale and result from the diffusion and dispersion process. Mixing effect has positive and negative effects on the miscible process. It could exerts a considerable damping effect on the growth of viscous and gravity fingers as an advantage of the dispersion and diffusion and on the other hand mixing of the solvent with oil in a reservoir decreases the effective strength of the solvent, which can have annoying effect on the miscibility and recovery efficiency. So, correct treatment of this mixing effect within the simulation of the miscible recovery process is very important. Mixing of the solvent and oil is governed by the diffusion and dispersion coefficients. For measuring these coefficients, we must depend largely on experimental measurements of them, because no universal theory permits their accurate a priori calculation. There are many influencing parameters on the mixing process during solvent injection into porous media. Designing an experiment to investigate all the effective parameters is extremely difficult (if not impossible) and time consuming. So, based on the sub-pore scale modeling method, a special program has been developed to investigate the mass transfer process in any porous medium, considering all medium properties. A realistic image of the porous medium without any simplification is directly used in the program. Developed program used the porous medium image directly, so, heterogeneity effect could be investigated. Pore and throat size distributions, as an important heterogeneous parameter, has a unique effect on the diffusion and dispersion coefficients. A comprehensive study of the pore and throat size distribution effect on the mixing coefficients will be presented in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".