Optimization of closed-cycle oil recovery: a non-thermal process for bitumen and extra heavy oil recovery
Bibliographic record
Abstract
Energy from unconventional resources includes bitumen and extra-heavy oil that represent two-thirds of the known resources in the world. Extra-heavy oil and bitumen are currently recovered using thermal processes having a large carbon footprint and significant environmental impacts on water resources. A novel process is proposed: closed-cycle oil recovery (C-COR). C-COR is a greener alternative to provide energy from these unconventional resources with minimal water consumption. C-COR relies on recovering oil solubilized within a single-phase microemulsion, eliminating the need for viscosity reduction to both mobilize heavy oil or to transport it. Proof-of-concept work was conducted using conventional phase behavior experiments with extracted oil and surfactant formulations to develop a surfactant formulation for oil recovery using C-COR. As a part of process development and scale-up, we conducted flow experiments presented in this paper. We learned that a high degree of surfactant adsorption, which negatively impacted the C-COR process, resulted at low pH levels. These findings required modifying traditional static batch tests (phase behavior studies) using actual oil sand instead of the extracted oil. These unorthodox tests revealed that surfactant adsorption caused low oil solubilization and that alkali can be used to reduce adsorption, improving oil solubilization. In addition, unique flow experiments were designed to optimize the delivery and recovery process and are presented in this paper. The unique batch tests and flow experiments were conducted using oil sands from Canada to optimize the process. The proposed optimized approach would employ intermittent flow (soaking) that would result in the fastest recovery of about one-third of the OOIP, followed by continuous injection to recover an additional 10% OOIP, ending with thermal enhancement to recover another 25% OOIP for a total of 61%.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".