@inproceedings{00556ff25fd04ae5b36154890f6f4523,
title = "A near infrared-based downwhole water-cut meter using neural network",
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.",
keywords = "Downhole measurement, Near infrared instrument, Neural network",
author = "M. Meribout",
note = "Publisher Copyright: {\textcopyright} 2014 SPIE.; Infrared Remote Sensing and Instrumentation XXII ; Conference date: 18-08-2014",
year = "2014",
doi = "10.1117/12.2072194",
language = "British English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Gonzalo Paez and Scholl, {Marija Strojnik}",
booktitle = "Infrared Remote Sensing and Instrumentation XXII",
address = "United States",
}