Three Dimensional Visualisation of Solvent Chamber Growth in Solvent Injection Processes: An Experimental Approach
Bibliographic record
Abstract
Solvent based methods have been recommended as an alternative to thermal techniques for heavy oil and bitumen recovery. However, the cost of solvent injection processes is high and therefore requires a careful design of field scale applications. Yet, the efforts have been limited to numerical or analytical models in clarifying the complex physics of solvent processes due to difficulties in experimentation. Vapor extraction (VAPEX) is one of the solvent applications suggested and tested for heavy-oil/bitumen recovery from unconsolidated oilsands. In this method, solvents are injected into the reservoir through a horizontal well and gravity drainage takes place resulting in the production from the lower production well. Theoretical analyses suggest that a solvent chamber develops and the displacement is controlled by its growth during this process. Previous studies, however, showed the possibility of unusual behavior at the reservoir scale reservoirs such as stopping, shrinking, and even disappearing of chambers associated with severe fingering phenomena. These kinds of anomalies can only be identified through experimental designs and, so far, have been limited to 2-D models with minimal visual support. In this paper, a three dimensional imaging technique using laser is introduced. Refractive index matching of solvent, oil, and glass beads pack enabled imaging the progress of solvent displacement. 2-D images of slices with little spacing in the scaled VAPEX model were recorded within a significantly short time using laser sheets. Then, 3-D images were generated by integrating the 2-D images. In simulating the VAPEX model, different injection rates were applied and different sizes of glass beads were used to create different permeability media to also study the physics of the process parametrically. 3-D images were analyzed to understand the chamber growth process during VAPEX. The observations are expected to be useful in clarifying many uncertainties of the VAPEX process and also provide data for further computational studies.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".