Reparameterization of static filtration model of aqueous-based drilling fluids for simultaneous estimation of compressible mudcake parameters

Abstract

Abstract To mitigate the fluid invasion and the loss of circulation, drilling fluids are designed to provide a thin mudcake with low permeability. Among the many methods of determining the mudcake properties, the absence of proper procedures related to the estimation of mudcake parameters that also consider the experimental uncertainties is noticed. Besides, the non-Newtonian rheological behavior in filtration and its consideration in resistive force modeling is still lacking in the literature. This paper proposes a new methodology of parameter estimation using the particle swarm optimization technique with a modification of the classic model approaches considering the non-Newtonian fluid filtration theory, including the fluid phase rheological behavior. Static filtrations were performed in an HTHP filtration cell with non-Newtonian suspensions with concentrations like those used in actual drilling processes. The internal structure of the mudcakes was studied by SEM images. A reparameterization of the static filtration model was proposed for the simultaneous estimation of precise and uncorrelated parameters: filter resistance, permeability, and compressibility of the mudcakes, considering two different methodologies, Method I – classic and not advisable - and Method II – rigorous statistical process and pressure-dependent filtration properties. As shown in this research, the confidence intervals for all parameters obtained by Method II were narrow with significative statistical meaning, at a confidence level of 95%, and close to those obtained by other authors. Also, the reparameterization procedure removed the parametric correlation between the mudcake permeability and compressibility, resulting in parameters with null correlation. The numerical solution does not lead to a high computational cost and could be installed in an onboard control device to predict online filtration loss, providing valuable pieces of information in oil drilling processes.