Recent Developments of Cyclic Solvent Injection Process to Improve Oil Recovery from Unconsolidated Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract One of the practical limitations of cold heavy oil production method in unconsolidated reservoirs is sand production that leads to very 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 heavy oil reservoirs. To have an improved understanding of the conditions that result in a successful CSI process, we reviewed the dominant driving mechanisms of CSI at reservoir conditions such as fluid displacement, pressure gradients, non-equilibrium gas dissolution/exsolution, potential formation collapse, and deformation issues; the interest is on the application of CO2 as a solvent. Limitations of current thermal and non-thermal EOR methods were briefly compared against the CSI in thin oil reservoirs. To complete the assessment, several case studies and lessons learned from CSI applications were included based on the latest laboratory experiments, numerical studies, in addition to the CSI pilot/field tests. Specific to thin heavy oil reservoirs with sand production (e.g. CHOPS), incremental oil recovery requires to re-energize the depleted reservoirs in a cyclic manner, aiming to provide more drive energy by economical gaseous solvents (e.g. CO2). It was realized that other EOR techniques such as waterflooding, gas flooding, and steam injection can face major issues with flow and heat efficiencies, including fingering and significant heat/solvent losses; this makes CSI a feasible EOR alternative. Regarding the solvent use, laboratory experiences have not been conclusive about what solvent stream could result in an improved oil recovery; however, most of the solvents should be designed to either reduce heavy oil viscosity, or strengthen the nucleation and stability of the injected solvent bubbles in the heavy oil reservoir (i.e. foamy oil behavior). To this end, successful field scale CO2 EOR applications have been reported in several oil reservoirs. Although progress has been made, but numerical modelling still faces challenges to properly model the main CSI driving mechanisms, including fluid-solvent interactions and deformation of subsurface reservoirs. Moreover, field implementation indicates that highly productive wells during primary production from unconsolidated reservoirs might also outperform during a follow up CSI process. This work addresses the recent improvements in application of CSI EOR to develop heavy oil reservoirs, especially for thin and poorly consolidated sandstones. The findings of this paper, including the limitations and requirements of different recovery techniques, enable more effective design of field scale CO2 EOR operation in depleted heavy oil reservoirs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".