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Application of Optimized Least Square Support Vector Machine and Genetic Programming for Accurate Estimation of Drilling Rate of Penetration

Application of Optimized Least Square Support Vector Machine and Genetic Programming for Accurate Estimation of Drilling Rate of Penetration

Meysam Naderi, Ehsan Khamehchi
Copyright: © 2018 |Volume: 7 |Issue: 4 |Pages: 17
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781522546542|DOI: 10.4018/IJEOE.2018100105
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MLA

Naderi, Meysam, and Ehsan Khamehchi. "Application of Optimized Least Square Support Vector Machine and Genetic Programming for Accurate Estimation of Drilling Rate of Penetration." IJEOE vol.7, no.4 2018: pp.92-108. http://doi.org/10.4018/IJEOE.2018100105

APA

Naderi, M. & Khamehchi, E. (2018). Application of Optimized Least Square Support Vector Machine and Genetic Programming for Accurate Estimation of Drilling Rate of Penetration. International Journal of Energy Optimization and Engineering (IJEOE), 7(4), 92-108. http://doi.org/10.4018/IJEOE.2018100105

Chicago

Naderi, Meysam, and Ehsan Khamehchi. "Application of Optimized Least Square Support Vector Machine and Genetic Programming for Accurate Estimation of Drilling Rate of Penetration," International Journal of Energy Optimization and Engineering (IJEOE) 7, no.4: 92-108. http://doi.org/10.4018/IJEOE.2018100105

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Abstract

This article describes how the accurate estimation of the rate of penetration (ROP) is essential to minimize drilling costs. There are various factors influencing ROP such as formation rock, drilling fluid properties, wellbore geometry, type of bit, hydraulics, weight on bit, flow rate and bit rotation speed. This paper presents two novel methods based on least square support vector machine (LSSVM) and genetic programming (GP). Models are a function of depth, weight on bit, rotation speed, stand pipe pressure, flow rate, mud weight, bit rotational hours, plastic viscosity, yield point, 10 second gel strength, 10 minute gel strength, and fluid loss. Results show that LSSVM estimates 92% of field data with average absolute relative error of less than 6%. In addition, sensitivity analysis showed that factors of depth, weight on bit, stand pipe pressure, flow rate and bit rotation speed account for 93% of total variation of ROP. Finally, results indicate that LSSVM is superior over GP in terms of average relative error, average absolute relative error, root mean square error, and the coefficient of determination.

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