A novel downhole sensor to determine fluid viscosity
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abstract
This paper presents the performance evaluation of a novel sensor designed to measure the in situ viscosity of a fluid flowing at downhole conditions. The device provides a mechanism to allow the passage of solid particles (i.e. sand) and has a self-cleaning ability should any build-up of these particles restrict the flowing area. The sensor was assembled in a closed flow loop to prevent measurement error due to partial vaporization of the samples at higher temperatures, and it was tested and calibrated with mixtures of glycerin and water. Differential pressures, flow rates and temperatures were acquired and used to determine the viscosity of two crude oils (and mixtures of those) with viscosities ranging from 0.001 to 0.03 Pa.s (1 to 30 cp ) and temperatures from 37.8 to 71.1C (100 to 160F). Flow rates were controlled to maintain linearity in the differential pressure response to ensure a laminar flow regime. Viscosity measurements were validated with independent measurements using a Brookfield viscometer and the agreement was within 2%. Using data from this sensor, new viscosity mixing rules were developed to allow determination of mixture compositions from viscosity measurements or mixture viscosities for given compositions. This paper also presents a generalized mathematical model to describe the performance of the sensor with Newtonian and non-Newtonian fluids. The model characterizes the response of the sensor as a function of the parameters from a power-law model rheological description and the geometry of the device. The experimental data suggest the validity of this model for predicting the sensor response under realistic operating conditions. The model can be used to calculate optimum dimensions to fabricate a device for customized applications. Potential applications include the estimation of diluent to be added to a more viscous fluid to achieve a target viscosity reduction, fluid identification from wireline formation testers, smart well fluid monitoring, enhanced mud logging, and fracture fluid characterization. 2011 Elsevier Ltd.
