Evaluation of Electroassisted Carbonated Water Injection (ECWI) in a Tight Reservoir: Outstanding Performance of Enhancing Oil Recovery and CO2 Storage Capacity
Bibliographic record
Abstract
Summary Traditionally, carbonated water injection (CWI) finds its low injectivity in a tight reservoir with a low efficiency and slow effectiveness. By combining a direct current (DC) electric field with the CWI, i.e., the electroassisted CWI (ECWI), we conducted a series of experiments to evaluate the ECWI performance in a tight reservoir and identify the key underlying recovery mechanisms. Experimental results show that early adopting the ECWI in a tight sandstone reservoir results in the highest oil recovery up to 61.9%, compared to those of 51.2% for the CWI and 41.3% for the conventional waterflooding. During an ECWI process, a voltage of 10 V achieves the highest oil recovery, but that of 15 V has the best water injectivity and CO2 storage capacity. It is found that the underlying recovery mechanisms result from both electroosmosis and enhancement of carbonized water-rock reactions induced by a DC electric field. For a tight reservoir, the ECWI has the advantages of significantly increasing water injectivity, oil production rate, and CO2 storage capacity. By introducing two new indicators to respectively evaluate the water injection performance and energy consumption, the ECWI is found to perform moderately well at a low voltage in an energy-saving and financially viable manner.
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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 | 0.001 |
| 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".