Mass Diffusion Into Bitumen: A Sub-Pore Scale Modeling Approach
Bibliographic record
Abstract
Abstract The increased interest in the production of heavy oil and bitumen has amplified attention paid to solvent-based methods for heavy oil recovery, such as vapor extraction (VAPEX) or miscible flooding. The diffusion of solvent into oil plays a major role for all the solvent-based recovery methods. Since the diffusion process is governed by the diffusion coefficient, the accurate prediction of mass transfer of the solvent in heavy oil and bitumen is extremely important. The concentration dependency of the diffusion coefficient differs from sample to sample and is determined experimentally in the laboratory. What is measured in the lab as the diffusion coefficient is influenced by different porous medium properties during the solvent injection. Therefore, an effective diffusion coefficient is defined for porous media, which has a dependency on the medium properties. The main goal of this paper is the prediction of an accurate value for the effective diffusion coefficient from experimentally measured values of diffusion considering the properties of the porous medium. The medium in this paper can be a micro model pattern, thin section, tomographic image or microscopic picture. The picture is analyzed by an image processing program to distinguish the pore and grain sections. After gridding the pore regions of the picture, virtual porous medium properties are extracted by applying the Navier-Stoke and continuity equations as the governing equations. The diffusion equation is applied to the medium to find the concentration profile of the solvent in the porous medium, and the effective diffusion coefficient of the system is computed from the concentration profile. An extensive investigation of the effects of medium properties on the diffusion coefficient will lead to the capability of predicting the effective diffusion coefficient for other media with different patterns and properties.
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".