Wettability alteration using functionalized nanoparticles with tailored adhesion to the rock surface for condensate banking mitigation
Bibliographic record
Abstract
Abstract Accumulation of liquids near the wellbore in gas reservoirs can result in what is known as liquid banking, causing loss of gas and liquid production. One of the techniques that can be used to mitigate liquid banking is changing the rock wettability from liquid wetting to neutral wetting, allowing the gas to carry the liquid out of the pore space. Double‐functionalized wettability altering silica (SiO 2 ) nanoparticles (NPs) were developed. The synthesized NPs were functionalized with both fluoro‐silane (F‐SiO 2 NPs) and a charged functional group to enable coupling of the NPs to the substrate. Surfaces treated with F‐SiO 2 NPs functionalized with coupling functional group showed superior wear and corrosion resistance in high salinity water compared to single‐functionalized F‐SiO 2 NPs. By functionalizing 125 nm SiO 2 NPs first with polyethylenimine (PEI‐) or N‐(2‐Aminoethyl)‐3‐aminopropyltrimethoxysilane (DAMO‐) l group followed by fluorination, a water contact angle of 120°and decane contact angle of 70° was achieved. Core flow testing performed under realistic reservoir conditions of pressure and temperature showed that F‐SiO 2 NPs coated with PEI achieved a 58% improvement in gas and liquid relative permeability, indicating improvement in the surface repellency to liquids and reduction of the trapped liquid phases inside the pore space of the rocks. The developed double‐functionalized NPs achieved similar performance to the commercially used fluorinated surfactants but using less treatment volume. In addition, these double‐functionalized NPs enable one‐step treatment in the field, which results in simpler operations, shorter treatment time, and better treatment economics.
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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) | 0.001 | 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".