Recent Developments in the CO2-Cyclic Solvent Injection Process to Improve Oil Recovery from Poorly Cemented Heavy Oil Reservoirs: The Case of Canadian Reservoirs
Bibliographic record
Abstract
One of the limitations of Cold Heavy Oil Production with Sand (CHOPS) is the low recovery factor (5–15%). To target the remaining 85–95% heavy oil resources, several enhanced oil recovery (EOR) techniques, such as cyclic solvent injection (CSI), have been proposed. Due to its potential success in Canada and elsewhere, this paper reviews the technical and efficiency requirements of CSI EOR in post-CHOPS heavy oil reservoirs. We explain the dominant driving mechanisms of CSI with a focus on the application of CO2 as a solvent. Limitations of current thermal and non-thermal EOR methods were compared to the CSI in thin oil reservoirs. To complete the assessment, several case studies and lessons learned were included based on the latest laboratory experiments, numerical studies, and CSI pilot/field tests. Specific to thin and shallow heavy oil reservoirs with sand production (e.g., CHOPS), the key to recover incremental oil was found to re-energize depleted reservoirs in a cyclic manner with unexpensive solvents (e.g., CO2). Regarding the solvent use, laboratory experiences have not been conclusive about what solvent stream could improve oil recovery. To this end, successful field scale CO2 EOR applications have been reported in several post-CHOPS reservoirs indicating that highly productive wells during primary production might also outperform during a follow up CSI process. Numerical modeling still faces challenges to properly model the main CSI driving mechanisms, including fluid–solvent interaction and the deformation of subsurface reservoirs.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".