CFD VALIDATION FOR ASSESSING THE REPERCUSSIONS OF FILTER CAKE BREAKERS; EDTA AND SIO2 ON FILTER CAKE RETURN PERMEABILITY
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Date
2022
Authors
Wayo, Dennis Delali Kwesi
Irawan, Sonny
Khan, Javed
Fitrianti, N. H.
Journal Title
Journal ISSN
Volume Title
Publisher
Applied Artificial Intelligence
Abstract
Drill-in-fluids create what are known as filter cakes. Filter cakes, in some cases, lead to well abandonment because they prevent hydrocarbons from flowing freely from the formation into the wellbore. Cake removal is essential to avoid formation damage. A previous study on filter cake breakers was considered for computational fluid dynamic (CFD) validation. Matlab-CFD and Navier-Stokes equations aimed at predicting and validating visual, multiphase flow under finite element analysis (FEA). The interactions of separate chemical breakers and drill-in-fluid such as ethylenediaminetetraacetic acid (EDTA), silica-nanoparticle (SiO2), and biodegradable synthetic-based mud drill-in-fluid (BSBMDIF) were monitored under a particle size distribution, viscosity, density, and pressure. Predicting return permeability of filter cake was considered under a simple filtration process. The particles’ deposition created pore spaces between them; barite 74 μm, nano-silica 150 nm, and EDTA 10 μm generally closed up the pores of the filtration medium. Under extreme drilling conditions, barite formed thicker regions, and EDTA chemical properties easily disjointed these particles, while SiO2 entirely did not. The experimented results of (EDTA) and SiO2 for return permeability were in full force agreeable with the 2D simulation. A hybrid computational analysis considering CFD under discrete element analysis and neural network can be employed for further research validations.
Description
Keywords
Type of access: Open Access, CFD Validation, Filter Cake Breakers
Citation
Wayo, D. D. K., Irawan, S., Khan, J., & Fitrianti, N. H. (2022). CFD Validation for Assessing the Repercussions of Filter Cake Breakers; EDTA and SiO2 on Filter Cake Return Permeability. Applied Artificial Intelligence, 36(1). https://doi.org/10.1080/08839514.2022.2112551