Bibliographic record
Abstract
Abstract VAPEX (vapour extraction) is an oil recovery process, in which heavy oil or bitumen is mobilized by injection of a low molecular weight hydrocarbon solvent and is drained by gravity to a horizontal production well. It has attracted considerable attention because of its potential applicability to problematic reservoirs and the potential for in-situ upgrading of heavy oil during the process. Oil drainage rate under VAPEX is controlled by the viscosity of solvent diluted oil and can be affected substantially by de-asphalting. In-situ de-asphalting can be advantageous because it reduces the oil viscosity and leads to production of upgraded oil. However, the precipitated asphaltenes can also plug the pores of the formation and cause severe damage to the permeability. The objective of the current work was to determine whether the beneficial effects of asphaltene precipitation would outweigh any formation damage. The effects of in-situ precipitation and deposition of asphaltenes on the rate of oil drainage and the quality of the produced oil under different operating conditions were experimentally evaluated. The experiments were conducted in a physical model, packed with 140 - 200 mesh sand, and propane was used as the solvent. The quality of the produced oil samples was evaluated through the SARA technique and viscosity measurements. The experimental results show that the oil produced at higher injection pressures was substantially upgraded, but the viscosity reduction by asphaltene precipitation did not lead to higher production rates. The effect of viscosity reduction was negated by the accompanying damage to formation permeability. The huff and puff injection of toluene into the production well, to remove damage from the near well zone, was tried but proved to be ineffective. It led to production of oil with higher asphaltene content with no improvement in the rate of oil production compared to the lower pressure operation without asphaltene precipitation. However, co-injection of toluene with propane was successful in increasing the rate of production and the extent of upgrading obtained was encouraging.
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) | 0.000 | 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) | 0.001 | 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".