The work presented in this paper focuses on development of a dual modality sensor, for deployment within an oil and gas extraction plant to measure the composition of oil-water mixtures. The sensors combine ultrasonic and electrical measurement techniques. These are of course non-destructive, rapid, and can potentially provide an on-line industrial measurement. In addition, the combination of two techniques could potentially be reliable in a wider range of process conditions and could contain self-calibration features. The sensors used in the current study were manufactured using thick-film technology, which enables construction of multilayered structures of both conductive and non-conductive layers, some of which may exhibit piezoelectric properties for ultrasonic measurement purposes. These are later fired on a ceramic substrate to provide rugged sensors, capable of working in aggressive industrial environments. Experiments were conducted for mixtures of vegetable oil and saline water to investigate the feasibility of such dual dual-modality sensors. The time of flight of ultrasonic wave in pure liquids and heterogeneous mixtures was measured. It has been shown that the signal obtained from the transducers is sufficiently strong to warrant the measurement of the speed of sound in heterogeneous mixtures of oil and water. A study of the effects of oil concentration and temperature on the speed of sound has been conducted. A mathematical model has been tested, which relates the speed of sound to the volume fraction taking into account the reflection and refraction on the droplet interfaces. The experimental results subjected to linear regression agree very well with the theoretical predictions. The electrical measurement was conducted at three different frequencies. In general, the values of capacitance and conductance decrease with increasing oil percentage. In the middle oil percentages a discontinuity occurs in the decreasing trend. In the high oil percentages, the experimental results agree very well with theoretical predictions.

This content is only available via PDF.
You do not currently have access to this content.