A near infrared-based downhole water-cut meter using neural network technique

M. Meribout

doi:10.1117/12.2072194

12 September 2014 A near infrared-based downhole water-cut meter using neural network

M. Meribout

Proceedings Volume 9219, Infrared Remote Sensing and Instrumentation XXII; 92190O (2014) https://doi.org/10.1117/12.2072194
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

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.

Citation Download Citation

M. Meribout "A near infrared-based downhole water-cut meter using neural network", Proc. SPIE 9219, Infrared Remote Sensing and Instrumentation XXII, 92190O (12 September 2014); https://doi.org/10.1117/12.2072194

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available