Pore Scale Visualization during Carbonate Heavy Oil Recovery: Surfactant Alternating CO2 Foam/Polymer Enhanced Foam Flooding
Bibliographic record
Abstract
Abstract Carbonate reservoirs hold significant reserves of heavy crude oil that can be recovered by non-thermal processes. Chemical, gas, water, and solvent injections are the main methods for carbonate heavy oil recovery. Chemical enhanced oil recovery (EOR) from oil-wet carbonate reservoirs has been focused on using surfactants to change the wettability and enhance water imbibition into matrix blocks. Due to the fractured nature of carbonate formations, many advantages of these production methods are usually contrasted by their low recovery factor. Alternative processes are therefore needed to increase oil sweep efficiency from the matrix. Foam/polymer enhanced foam (PEF) injection has gained interest in conventional heavy oil recovery in recent times. However, the oil recovery process by foam injection, especially by PEF, is less understood in fracture heavy oil carbonate reservoirs. This paper introduces a new approach to access the heavy oil from fractured carbonate reservoirs. CO2 foam and CO2 PEF were used to decrease oil saturation after surfactant flooding. Three types of surfactants (nonionic, anionic and cationic) were used for both surfactant and foam flooding. A specially designed fractured micromodel was used to visualize the pore scale phenomena during CO2 foam/PEF injection. In addition, the static bulk performances of foam/PEF were analyzed in the presence of heavy crude oil. A high definition camera was utilized to capture high quality images. The results showed that, in both static and dynamic studies, the PEF had higher stability than that of foams. Nonionic surfactant generated the least stable foam in the presence of crude oil. Surfactant flooding slightly increased the oil recovery from matrix after water injection. This was more evident in the case of CTAB surfactant with highest imbibition rate. This study showed that there is an acceptable relationship between the results of foam static stability and its dynamic performance in porous media. DDBS foam had higher static stability, which resulted in the highest oil recovery in dynamic experiments. Observation through this study proved that stable foam/PEF bubbles can significantly push the injected fluid toward untouched parts of the porous media and increase the oil recovery. Due to the liquid viscosity enhancement and bubble stability improvement, the effectiveness of PEF in heavy oil sweep efficiency was much higher than that of conventional foams. PEF bubbles generated an additional force to divert surfactant/polymer into the matrix. The results of pore scale visualization improved understanding of the heavy oil recovery process by CO2 foam/PEF flooding from fractured reservoirs.
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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".