Synthesis and Characterization of Magnetic Nanodroplets for Flowback Analysis in Fractured Reservoirs
Bibliographic record
Abstract
Abstract Hydraulic fracturing has facilitated the rapid development of tight reservoirs throughout the world in the past decades. A common challenge arising from multistage hydraulic fracturing is the accurate characterization of the complexity and conductivity of the fracture networks, for optimizing the well performance as well as the subsequent production. Prevalent operations carried out on hydraulically fractured wells are tracer injections along with the fracturing fluid and flowback analysis, which are indispensable to condition the well for long-term performance and decrease the operational time. In this paper, a new polymer-coated iron oxide (Fe3O4) nanoparticle (NP) has been synthesized, which can emulsify and stabilize nano-oil-droplets in a continuous water phase and these Pickering nanodroplets provide potential applications for the characterization of fractures by flowback analysis in tight reservoirs due to their pivotal properties, including being superparamagnetic with ability to detect in-situ, easily synthesized, size controllable, strong stability, minimal retention in fractures and environmental benign features. To apply these magnetic nanodroplets for fracture characterization, two concerns should be considered, including the long-term stability and transport behavior of these Pickering nanodroplets, which is demonstrated in this study. Herein, iron oxide nanoparticles were firstly functionalized to improve their hydrophilicity, and then nanoemulsion samples were emulsified utilizing these engineered nanoparticles. Two different factors, including different hydrocarbons and emulsification energy, were considered to investigate their impact on the stability of the nanoemulsion. This is because they are extremely important for the stabilization of the Pickering nanoemulsion. As a result, some characterization tests were performed to recognize the stability behaviour of the systems and structure of nanoemulsion through nanodroplet size distribution, z-potential, bulk rheology, and screening tests. Moreover, the nanoemulsion stability is examined through low-field nuclear magnetic resonance (NMR) relaxometry and X-ray CT imaging. Experimental results reveal that carefully synthesized polymer-coated Fe3O4 NPs can emulsify the oil and water to form a sufficiently stable oil-in-water (O/W) Pickering nanoemulsion. The optimized composition to have a more stable emulsion is using hexadecane as the oil phase because of its high density and low solubility in water to reduce the Ostwald Ripening. An emulsification energy of 40 kJ is found to generate optimum droplet size distribution, thus providing the best nanoemulsion stability.
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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".