Computed Tomography Study of VAPEX Process in Laboratory 3D Model
Bibliographic record
Abstract
Abstract The vapour extraction (VAPEX) process has been an intense research topic in recent years as an alternative technology to thermal recovery methods for heavy oil and bitumen resources. Most previous 2D transparent models had simulated the vapour chamber evolution behaviour of a vertical slice of the reservoir; however, the longitudinal vapour chamber evolution characteristic in 3D geometry could not be detected. This paper presents the results of 3D monitoring of the VAPEX process in a laboratory model, using computed tomography (CT) technology to investigate the vapour chamber expansion behaviour in both radial and longitudinal directions. The results show that in 3D geometry, "V?? shape vapour chamber expansion was a localized phenomenon. The dominant characteristic was that solvent gas first broke through upward to the top, progressing through the high-permeability zone by gravity segregation, forming a vapour chamber at the top. It then expanded downward from the top as the experiment progressed. From the numerical analysis of the CT images, the in-situ porous medium's porosity, density and oil saturation profiles were obtained. The results further imply that contained gravity drainage may be the key for the success of the VAPEX process. Introduction For the more than 400 billion m3 heavy oil and bitumen deposits in Canada, only 10% is surface minable. The major part of the deposits has to be relied on in-situ recovery processes(1). However, because of their high viscosities and low-degree API gravities in native state(2), these reservoirs can only be recovered with low recovery efficiency by conventional methods. For example, primary recovery in the best of these heavy oil reservoirs is approximately 6% of the original oil in place (OOIP). Subsequent waterflooding can improve the recovery to an extent of 1% ~ 2% incremental of OOIP(3). In order to more effectively recover these reserves, enhanced oil recovery (EOR) or improved oil recovery (IOR) methods have to be directly applied(4). The main technology challenge is to reduce the heavy oil viscosity in-situ(5). As the oil viscosity is very sensitive to temperature, thermal recovery methods seem to be very effective and have been widely researched and piloted(6), including cyclic steam simulation (CSS), in-situ combustion (ISC), steam assisted gravity drainage (SAGD) and steamflooding(7). The SAGD process has been commercially used by several oil companies in Canada. However, the SAGD process is not always applicable for all heavy oil reservoirs; some economic constraints arise if the high cost of steam generation and excessive heat losses in some thin oil reservoirs are considered(8). As alternatives, non-thermal processes such as solvent-based processes are still a logical choice to recover such heavy oil/bitumen reservoirs(9).
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".