Multi-Scale Evaluation of Nanoparticle-Stabilized CO2-in-Water Foams: From the Benchtop to the Field
Bibliographic record
Abstract
Abstract Although EOR with CO2 is practiced domestically on large scale, the potential for advancement is enormous. The ongoing search for better solutions has motivated extensive research on alternatives to surfactant-stabilized CO2 foams for CO2 mobility control. The formation of CO2-in-water foams lowers the CO2 mobility, resulting in improvement in sweep efficiency in field tests. The crucial unmet challenge in employing CO2 foams is to maintain long-term stability of foam to achieve high sweep efficiency for the duration of the flooding process. Surfactant-stabilized foams are inherently unstable so that maintenance of the low mobility requires continuous regeneration of lamellae in the small pores of the rock. Nanoparticles can potentially be used to provide much higher foam stability and thus long-term mobility control for CO2 floods. They can act like a foaming surfactant without some of the surfactant drawbacks. Here we present a turnkey approach for using surface treated nanoparticles in reservoirs. This involves: tests for stability in brines, transportability through cores, foam generation in beadpacks and cores when co-injected with CO2, quantification of CO2 viscosity enhancement, and finally modeling of field-scale effects. In this paper, we will outline the key details of nanoparticle design for CO2 EOR.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".