JOURNAL ARTICLE

Pore-scale insights into the effect of surface-modified nanosilica on invert-emulsion drilling-fluid and formation damage.

  • Published In: Physics of Fluids, 2025, v. 37, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Madanchi, Parham; Mahani, Hassan 3 of 3

Abstract

This article investigates formation damage mechanisms caused by the invasion of invert-emulsion oil-based drilling fluids (OBMs) at the pore scale and evaluates damage remediation using silica nanoparticles with varied surface chemistries. Using microfluidic glass micromodels, the study identifies two damage types: primary damage from emulsion breakage, water blockage, and particulate deposition immediately after drilling fluid invasion, and secondary damage during acid treatment and oil flowback, including acid trapping, sludge, and acid-in-oil emulsions. The research demonstrates that nanoparticle additives, particularly hydrophobic silica nanoparticles such as PGPTS (propyl silane combined with 3-glycidoxypropyl-triethoxy silane), improve drilling fluid stability, reduce formation damage, and optimize rheological properties, with optimal nanoparticle concentrations decreasing as hydrophobicity increases. The 0.5 wt. % PGPTS nanofluid showed the least formation damage, indicating that surface chemistry modification has a greater impact on fluid stability and damage mitigation than nanoparticle concentration alone. These findings provide pore-scale insights into OBM-induced formation damage and suggest that nanoparticle-enhanced drilling fluids can effectively reduce damage during the well life cycle.

Additional Information

  • Source:Physics of Fluids. 2025/03, Vol. 37, Issue 3, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2025
  • ISSN:1070-6631
  • DOI:10.1063/5.0250242
  • Accession Number:184176184
  • Copyright Statement:Copyright of Physics of Fluids is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.