Experimental Investigation of Heat and Oil Droplet Size Effects on Nanoemulsion Propagation in Porous Media
Bibliographic record
Abstract
Abstract Hydraulic fracturing is a key technique for enhancing production from low-permeability, organic-rich shale oil and gas reservoirs by increasing rock permeability. Accurate characterization and imaging of hydraulically induced fractures are essential for predicting production performance and estimating the stimulated reservoir volume (SRV). Tracer concentrations measured during flowback and historical production data provide valuable insights into fracture and matrix properties, such as fracture geometry, hydraulic conductivity, and natural fracture density. However, the inherent complexity and uncertainty in fracture and reservoir characterization, combined with limited data availability, pose significant challenges to the accurate estimation of these properties. This study aims to address this challenge by introducing magnetic Pickering nanoemulsions as tracers. We investigate how heat and oil droplet size affect the transportation and retention behavior of these engineered nanoemulsions in porous media. Through tracer injection and flowback analysis, we provide insights into their performance and potential for improving subsurface characterization and reservoir management. Polymer-coated iron oxide (Fe3O4) nanoparticles were synthesized and utilized as stabilizers to produce stable oil-in-water (O/W) nanoemulsions. Four distinct nanoemulsions were formulated by applying varying emulsification energies (54, 59, 64, and 72 kJ) to achieve controlled oil droplet sizes. A series of core flooding experiments were performed in a sandpack at 70°C to evaluate the transport behavior of these nanoemulsions in porous media. To simulate reservoir conditions, an overburden pressure of 1000 psi was applied during nanoemulsion flooding. Subsequently, the overburden pressure was incrementally increased from 1000 psi to 2000 psi at a rate of 2.5 psi/min during chase water flooding. X-ray CT scanning was used to monitor nanoemulsion saturation profiles. Additionally, the oil droplet size distribution, effluent sample density and susceptibility, and pressure drop throughout the flooding process were measured to determine the most effective nanoemulsion formulation with minimal retention in porous media. The results demonstrated that the most stable nanoemulsion formulation, created by applying 64 kJ of emulsification energy, corresponding to a droplet size of 850 nm, exhibited efficient transport through the sandpack with minimal retention. Pressure-drop measurements revealed a steady increase during nanoemulsion flooding, which can be attributed to the higher viscosity and increased drag forces exerted by the nanoemulsion compared to water. This behavior highlights the significant influence of nanoemulsion properties on flow resistance within the porous medium. During nanoemulsion flooding across all experiments, the nanoemulsion exhibited piston-like displacement. However, during subsequent chase water flooding, bypassing of the nanoemulsion was observed, attributed to the lower viscosity of water compared to the nanoemulsion. This phase transition was marked by a noticeable reduction in pressure drop. The results indicate that the optimized emulsification energy effectively enhances the stability and mobility of the nanoemulsion, minimizing retention and ensuring efficient transport through the heated porous medium.
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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 | 0.001 |
| 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".