A Step-Change Improvement in Fluid Diversion, Oil Sweep Efficiency, and CO2 Storage Using Novel Nanoparticle-Based Foam
Bibliographic record
Abstract
Abstract A significant amount of hydrocarbon in reservoirs is inaccessible even after deploying enhanced oil recovery methods such as gas, water, and chemical injections. Foams have been used for mobility control and fluid diversion for gas-based enhanced oil recovery, but they often lack stability in reservoir conditions. This study introduces the application of highly stable nanoparticle-based foam (nanofoam) for gas and water diversion and improving sweep efficiency and CO2 storage. A series of flow experiments in uniquely designed dual porous media were performed to investigate the performance of nanofoam in fluid diversion, sweep improvement, and CO2 storage. A permeability contrast of 5 was created to mimic the heterogeneity and fluid diversion capability of different fluids including CO2 gas, water, surfactant-based CO2 foam, and nanofoam. High permeability and low permeability porous media were saturated with water and oil (viscosity of 20 cp) respectively, mimicking a swept thief zone and bypassed oil zone. Two different types of nanoparticles were used to stabilize the nanofoam (silica-based and cellulose-based nanoparticles). These nanofoams were compared with a conventional foam stabilized only by surfactant. Due to high mobility contrast, injecting CO2 and water resulted only in displacement of water from the high permeability core, with negligible flow into the oil-saturated core. Foam was then injected with the intention of preferentially filling the high permeability core, so that subsequent CO2/water injection would be diverted into the oil-saturated core. Although surfactant foam generated relatively strong foam, it failed to divert subsequent water/CO2 into the oil-saturated core. The amount of oil recovery and additional CO2 storage was minimal. On the other hand, nanofoam (made with either type of nanoparticles) diverted both water and CO2 to the low permeability media improving oil recovery and increasing CO2 storage. Compared to pure CO2/water injection, nanofoam enhanced the incremental oil recovery by 40% of original oil in place with additional CO2 storage. This study reveals that an engineered designed nanofoam could result in step-change improvement of conventional foams performance hence delivering the results desired in field applications. A highly stable foam can play an important role to access more pore space for CO2 storage and which is inaccessible otherwise without drilling new wells.
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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) | 0.000 | 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 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".