eprintid: 13703
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/37/03
datestamp: 2023-11-10 03:28:16
lastmod: 2023-11-10 03:28:16
status_changed: 2023-11-10 01:51:48
type: article
metadata_visibility: show
creators_name: Leam, J.J.
creators_name: Khor, C.S.
creators_name: Dass, S.C.
title: Saybolt color prediction for condensates and light crude oils
ispublished: pub
keywords: Colorimeters; Crude oil; Gas oils; Linear regression; Petroleum industry; Quality control, Color measurements; Correlation modeling; Laboratory analysis; Multiple linear regressions; Oil and gas fields; Physical and chemical properties; Stepwise regression; Visual observations, Color
note: cited By 0
abstract: Saybolt color determination is one of the techniques used to evaluate the quality of petroleum products as an indicator of the degree of refinement. As color is a property readily observed by operators, conventional procedures require operators to determine Saybolt color either through direct visual observation or through Saybolt chromometers. These methods are subjective due to the variability in perception of colors across different observers and may be influenced by external factors such as the level of illuminance. Digital oil color analyzers, on the other hand, cost almost four times as much as Saybolt chromometers. An alternative approach to color measurement is to develop a correlation model between Saybolt color with the physical and chemical properties of condensates and light crude oils from Malaysian oil and gas fields. This work applies several multiple linear regression techniques (such as stepwise regression) performed both manually and using the R software (version 3.6.1) to obtain statistically significant results. The step, regsubsets and glmulti functions from R are explored to develop the correlation model which predicts Saybolt color using only identified key properties, overcoming the possible drawbacks associated with conventional laboratory analysis. The models developed through these different techniques are analyzed and compared based on criteria indicated through the coefficient of multiple determination, R2 and F-tests to infer on suitable regression approaches. Results obtained from these regression methods for models with and without interaction terms report deviations of less than 5 for 75 of the samples used for validation. © 2020, The Author(s).
date: 2020
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096022766&doi=10.1007%2fs13202-020-01031-y&partnerID=40&md5=b7b06a692f4284d65ddaf37ef6ba23bf
id_number: 10.1007/s13202-020-01031-y
full_text_status: none
publication: Journal of Petroleum Exploration and Production Technology
refereed: TRUE
issn: 21900558
citation:   Leam, J.J. and Khor, C.S. and Dass, S.C.  (2020) Saybolt color prediction for condensates and light crude oils.  Journal of Petroleum Exploration and Production Technology.   ISSN 21900558