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Proceedings Paper

A near infrared-based downhole water-cut meter using neural network technique
Author(s): M. Meribout
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Paper Abstract

In this paper, near infrared-based technique of oil-water mixture for water-cut measurement using neural network technique is presented. It uses a multivariate (MDA) algorithm which comprises the Partial least Square regression (PLS), Polynomial PLS, and an Artificial Neural Network (ANN) for spectrum analysis. The NIR spectra is postprocessed using the principal component analysis (PCA).Experimental results indicate that an accurate water-cut measurement can be achieved with less than 0.5% error in the range of [90 to 100%] water-cut. This interesting result, in addition to the fact that the NIR array device is non-invasive, non-intrusive and can be easily inserted into deep oil wells using optical fiber would lead to concluded that near-infrared spectroscopy can be a good candidate for downhole accurate water-cut measurement.

Paper Details

Date Published: 12 September 2014
PDF: 7 pages
Proc. SPIE 9219, Infrared Remote Sensing and Instrumentation XXII, 92190O (12 September 2014); doi: 10.1117/12.2072194
Show Author Affiliations
M. Meribout, The Petroleum Institute (United Arab Emirates)


Published in SPIE Proceedings Vol. 9219:
Infrared Remote Sensing and Instrumentation XXII
Marija Strojnik Scholl; Gonzalo Páez, Editor(s)

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