Low-Temperature Bitumen Recovery from Oil-Sand Reservoirs Using Ionic Liquids
Bibliographic record
Abstract
Summary Steam injection is widely used for bitumen recovery. However, steam is not efficient for shallow or thin reservoirs because of heat loss in the wellbore or to surrounding formations. Numerous alternatives have been proposed, including the addition of solvents and replacement of steam with volatile solvents. Here, we describe a new technology that combines nonvolatile ionic liquids (ILs) and waterflooding for bitumen recovery that can deliver high recovery at ambient temperature. Different ILs were designed for complete dispersal/dissolution of bitumen at ambient temperature. The designed ILs were tested in coreflood experiments with high–grade oil–sand ore from Alberta. Two different scenarios were tested: continuous injection of ILs at different injection rates and injection of a slug of ILs followed by water injection. Different slug volumes were tested at a constant injection rate. After ILs injection, the oil sand was removed from the column, and the remaining bitumen was quantified using a modified Dean–Stark method. Viscosity and solid–content measurements of the recovered samples at breakthrough were conducted. Bitumen recovery by the designed ILs can be thought of as a solution mining process. Tuning the physical and chemical properties of the ILs is the most important aspect of achieving the desired interaction with the oil–sand system. Properties of the designed IL depend on the selected cation and anion, and the strength of their intermolecular interaction. Primary amines mixed with the oleic acid chosen for IL1 form a viscous IL that can recover bitumen, leaving a slight amount of bitumen behind, but a large pressure gradient. Changing the cation to tertiary amines produces significantly less–viscous ILs, which completely recover the bitumen in the oil–sand column. Moreover, the cation can be tailored to significantly minimize the fines (clay) migration and viscosity of the recovered bitumen and to provide compatibility with an aqueous phase. In all cases, these recoveries are significant, compared with the currently used technologies. This work proves that bitumen recovery from oil sand is possible at low temperatures by means of a process analogous to solution mining with the design of the proper ILs, in contrast to viscosity–reduction processes achieved by thermal methods. The properties of these ILs can be tuned for different recovery mechanisms. Thus, this work establishes the basis for developing a new class of in–situ recovery processes with high recovery efficiencies and low environmental impact.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".