Coupling Immiscible CO2 Technology and Polymer Injection to Maximize EOR Performance for Heavy Oils
Bibliographic record
Abstract
Summary With approximately 90% of Saskatchewan's original heavy oil in place remaining in the ground, there is excellent potential for the application of enhanced oil recovery (EOR) methods and new technologies. The goal of the study discussed in this paper was to investigate if a new proposed process--coupling CO2 and polymer injection--can increase EOR performance for heavy oil reservoirs. The oil recovery performance of three EOR modes [water-alternating-gas (CO2 WAG) injection, polymer-alone flood and coupled CO2 and polymer injection] was compared in laboratory-scale linear coreflood tests in waterflooded cores. The immiscible CO2 WAG process recovered 15.3% original oil in place (OOIP) with 6.16 MSCF/stb gas utilization. Under a controlled maximum pressure drop across the core, the polymer-alone (0.4 wt%) flood produced an additional 12.93% OOIP above the initial waterflood recovery. However, the coupled CO2 and polymer injection process (using a polymer concentration of only 0.2 wt%) gave better recovery efficiency (18.7% OOIP) than the polymer-alone flood. Moreover, it had much better gas utilization than the CO2 WAG run, consuming only 2.0 MSCF/stb, or one-third of the amount of CO2 used in that run to recover the same amount of oil. This performance comparison demonstrates two of the biggest advantages of coupled CO2 and polymer injection: it can effectively reduce the pressure drop across the core and can obtain encouraging recovery if the optimal polymer concentration is added to the water.
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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 | 0.000 | 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".