Improved Heavy Oil Recovery by CO2 Injection Augmented with Chemicals
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Bibliographic record
Abstract
Abstract During water-alternating-gas (WAG) flooding for heavy oil reservoirs, the adverse mobility ratio leads to a considerable amount of injection gas fingering through and overriding the oil zone. To improve the recovery efficiency of the WAG process in a Saskatchewan (Canada) heavy oil reservoir, the Saskatchewan Research Council (SRC) conducted a laboratory feasibility study of augmenting the injection water with chemicals (alkaline/surfactant/polymer). The resulting process is known as CAG, or chemical-alternating-gas (CO2 or flue gas). SRC's integrated approach included interfacial tension (IFT) and rheology measurements, phase behaviour studies, micromodel displacements, and corefloods to evaluate the effectiveness of the CAG process. The results showed that addition of ASP into the injection water could significantly lower IFT (to 10−2 mN/m) and improve mobility. The phase behaviour studies indicated that CO2 could be dissolved readily into reservoir heavy oil at moderate pressures (3.4–6.4 MPa), resulting in dramatic oil expansion (1.2–8.1%) and viscosity reduction (45–88%). It was also demonstrated that the presence of 70% N2 in the CO2 stream (i.e., flue gas) greatly reduced the gas solubility, causing negligible oil swelling and viscosity reduction at the reservoir pressure. It was observed from micromodel displacement tests that CO2 viscous fingering and breakthrough occurred quickly even at a low pressure of 2.3 MPa, indicating the need to lower the capillary pressure between the heavy oil and porous media and add a mobility buffer between the CO2 and heavy oil. The coreflood results showed that a conventional CO2-WAG process recovered more incremental oil than a flue gas-WAG (9.43 vs. 3.58% OOIP), whereas a CO2-CAG and a flue gas-CAG recovered incremental oil of 27.43 and 22.07% OOIP, respectively. The comprehensive studies suggest that the CO2-CAG process holds promise for recovering Saskatchewan's tremendous heavy oil resources.
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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 | 0.000 |
| 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) | 0.000 | 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 it