NUMERICAL INVESTIGATION OF NANOPARTICLE-BASED BREAKERS SUITABLE FOR REMOVING SYNTHETIC FILTER CAKES

Abstract

Filter cake formation is an integral part of the drilling process and provides wellbore stability and eliminates mud circulation loss. As drilling fluid containing solids is pumped under high pressure solid particles tend to deposit on wellbore surface forming an impermeable layer between the well and the reservoir. However, it causes a detrimental effect on production. In order to restore the initial permeability, engineers apply chemical treatment to dissolve the filter cake. Acids, oxidizers and chelating agents are usually used for such purpose. The use of conventional techniques may not be feasible in certain instances due to factors such as expensive chemicals, compatibility problems, and environmental considerations. Nano-breakers have drawn much attention in research community as an alternative to conventional methods. Due to unique properties such as small size (nanoscale) and high surface area nanoparticles show high penetration rate and filter cake removal efficiency. This study aims to estimate the efficiency of two nanoparticle-based breakers, namely silica and titanium dioxide, using numerical simulation by studying the size distribution and return permeability. The 500 datasets for the numerical simulation were synthesized from a laboratory data. This synthetic data was governed by Darcy equation to investigate the flow of nanoparticle-based filter cake breakers through ceramic disc. Moreover, the statistical analysis was conducted to predict the return permeability after nano-breaker treatment using MATLAB Machine Learning toolbox. The prediction accuracy of two statistical methods, namely Multiple Linear Regression and Artificial Neural Network, were compared in terms of coefficient of determination (R2). Artificial Neural Network modeling showed a better fitting with synthetic dataset with R2 = 0.99 compared to Multiple Linear Regression analysis, which obtained R2 of 0.82. Additionally, the results of the current study were compared with literature. It was however observed that the optimum return permeability of 46% was reached at 361st iteration which corresponded to the pressure of 21 psi and 6 ml/hr flowrate. Overall, return permeability prediction and optimization is best with ANN (R2 = 0.99) and pressure v value of 21 psi respectively. Extension of the return permeability study could consider the formation damage before and after nano-based breaker treatment.