Evaluation of Polymer-Assisted Carbonated Water Injection in Sandstone Reservoir: Absorption Kinetics, Rheology, and Oil Recovery Results
Bibliographic record
Abstract
The efficacy of carbonated water injection (CWI) is associated with weak CO2 absorption in water, and the resulting CWI does not meet the requirements of controlled CO2 mobility and enhanced oil recovery (EOR). High molecular weight oilfield polymer, e.g., polyacrylamide (PAM), is water-soluble and often used in water shut-off and mobility control applications. Thus, in this study, PAM, with concentrations (0.5, 1, and 2 g/L), as a viscosifier is used to improve the CO2 absorption capacity of water for possible implementation in CWI applications. PAM with intermediate concentration (1 g/L) was favorable for enhanced CO2 absorption with a retention period of 4 days in water. With high PAM concentration (2.0 g/L), CO2 absorption reduced and gas in the form of globules moderately absorbed in the upper layers of P-1 solution, resulting in a significant amount of aqueous phase left unabsorbed by CO2. At high PAM concentration, enough number of PAM chains were available to interact with CO2 and, as a result, solution received premature gelling that resisted further entry of CO2. The effect of stirring on CO2 absorption showed that the rate of stirring increases the amount of CO2 absorbed in P-1 solution. These observations were supported by rheological data which showed that CO2 absorption reduced the viscosity of the P-1 solution, and the decrease in viscosity is directly proportional to the amount of CO2 absorbed. Finally, oil recovery experiments were performed using P-1 solution with and without CO2 and compared with water. The oil recovery was found to be higher for P-1 solution prepared with 600 rpm. Thus, this study highlights the application of relatively common oilfield polymer PAM (⩽ 1 g/L) for enhanced CO2 absorption and improved oil recovery than conventional CWI, which recommends use of polymer-enhanced carbonated water injection (PE-CWI) in oil and gas industry.
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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 | 0.001 | 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 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".