Saybolt color prediction for condensates and light crude oils
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ORIGINAL PAPER-PRODUCTION ENGINEERING
Saybolt color prediction for condensates and light crude oils Jia Jia Leam1 · Cheng Seong Khor1,2 · Sarat C. Dass3 Received: 11 June 2020 / Accepted: 20 October 2020 © The Author(s) 2020
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. Keywords Forward selection · Backward elimination · Bidirectional elimination · R · Machine learning · Glmulti
Introduction Color observations of petroleum products are standardized through two international standards developed by the American Society for Testing and Materials (ATSM), namely ASTM D 156 and ASTM D 1500. The two standards cover different ranges of color. Highly refined petroleum products use the ASTM D 156 scale, also known as the Saybolt color scale which ranges from −16 (darkest) to +30 (lightest) (ASTM International 2003). For colors darker than −16 of the ASTM
* Cheng Seong Khor [email protected]; [email protected] 1
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
2
Centre for Systems Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
3
School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia Campus, Putrajaya, Malaysia
D 156 scale, the ASTM D 1500 scale is used, ranging from 0.5 (lightest) to 8 (darkest) (ASTM International 2008). Petroleum produc
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