# Drilling rate of penetration prediction through committee support vector regression based on imperialist competitive algorithm

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## Abstract

Rate of penetration (ROP) is an important parameter affecting the drilling optimization during well planning. This is particularly important for offshore wells because, offshore rigs contain daily expensive cost and therefore ROP plays a critical role in minimizing time and cost of drilling. There are many factors that affect the ROP such as mud, formation, bit and drilling parameters. In the first step of this study, the best parameters to predict ROP, are selected by error analysis of multivariate regression and then ROP modeling is performed by means of various support vector regression (SVR) methods. Fundamental difference between the individual models is type of kernel function. Finally, a committee machine is constructed in power law framework and it is optimized with imperialist competitive algorithm (ICA). This novel technique is called committee support vector regression based on imperialist competitive algorithm (CSVR-ICA) in this study. Data set are gathered from three jack-up drilling rigs. Results show that CSVR-ICA model improved the results of individual SVR models and it has a good performance in the ROP estimation.

## Keywords

Rate of penetration (ROP) Support vector regression (SVR) Power law committee machine Imperialist competitive algorithm (ICA)## References

- Afshar M, Gholami A, Asoodeh M (2014) Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling. Korean J Chem Eng 31:496–502CrossRefGoogle Scholar
- Al-Anazi AF, Gates ID (2010) Support vector regression for porosity prediction in a heterogeneous reservoir: a comparative study. Comput Geosci 36:1494–1503CrossRefGoogle Scholar
- Ansari HR (2014) Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir. J Appl Geophys 108:61–68CrossRefGoogle Scholar
- Ansari HR, Gholami A (2015) Robust method based on optimized support vector regression for modeling of asphaltene precipitation. J Pet Sci Eng 135:201–205CrossRefGoogle Scholar
- Arabjamaloei R, Shadizadeh SR (2011) Modeling and optimizing rate of penetration using intelligent systems in an iranian southern oil field (Ahwaz Oil Field). Pet Sci Technol 29:1637–1648CrossRefGoogle Scholar
- Arabloo M, Ziaee H, Lee M, Bahadori A (2015) Prediction of the properties of brines using least squares support vector machine (LS-SVM) computational strategy. J Taiwan Inst Chem Eng 50:123–130CrossRefGoogle Scholar
- Asoodeh M, Bagheripour P (2012) Estimation of bubble point pressure from PVT data using a power-law committee with intelligent systems. J Pet Sci Eng 90:1–11CrossRefGoogle Scholar
- Asoodeh M, Gholami A, Bagheripour P (2014) Asphaltene precipitation of titration data modeling through committee machine with stochastically optimized fuzzy logic and optimized neural network. Fluid Phase Equilib 364:67–74CrossRefGoogle Scholar
- Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congress on Evolutionary Computation, Singapore, pp 4661–4666Google Scholar
- Atashpaz-Gargari E, Hashemzadeh F, Rajabioun R, Lucas C (2008) Colonial competitive algorithm, a novel approach for PID controller design in MIMO distillation column process. Int J Intell Computing Cybern 1:337–355CrossRefGoogle Scholar
- Bagheripour P, Gholami A, Asoodeh M, Vaezzadeh-Asadi M (2015) Support vector regression based determination of shear wave velocity. J Pet Sci Eng 125:94–99CrossRefGoogle Scholar
- Bahari A, Baradaran SA (2009) Drilling cost optimization in a hydrocarbon field by combination of comparative and mathematical methods. Pet Sci 6:451–463CrossRefGoogle Scholar
- Bahari MH, Bahari A, Nejati Moharrami F, Naghibi Sistani MB (2008) Determining Bourgoyne and Young model coefficients using genetic algorithm to predict drilling rate. J Appl Sci 8:3050–3054CrossRefGoogle Scholar
- Bilgesu HI, Tetrick LT, Altmis U, Mohaghegh S, Ameri S (1997) A new approach for the prediction of rate of penetration values. Paper presented at SPE Eastern Regional Meeting, Lexington, USAGoogle Scholar
- Boser BE, Guyon IM, Vapnik V (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) Proceedings of the annual workshop on computational learning theory. ACM, New York, pp 144–152Google Scholar
- Bourgoyne AT, Young FS (1974) A multiple regression approach to optimal drilling and abnormal pressure detection. Soc Pet Eng J 14:371–384CrossRefGoogle Scholar
- Bourgoyne AT, Millheim KK, Chenevert ME, Young FS (2003) Applied drilling engineering, 9th edn. SPE, RichardsonGoogle Scholar
- Chen CH, Lin ZS (2006) A committee machine with empirical formulas for permeability prediction. Comput Geosci 32:485–496CrossRefGoogle Scholar
- Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297Google Scholar
- Deng S, Yeh TH (2010) Applying least squares support vector machines to the airframe wing-box structural design cost estimation. Expert Syst Appl 37:8417–8423CrossRefGoogle Scholar
- Fletcher R (1989) Practical methods of optimization. Wiley, New YorkGoogle Scholar
- Flores BE (1986) A pragmatic view of accuracy measurement in forecasting. Omega 14:93–98CrossRefGoogle Scholar
- Gencoglu MT, Uyar M (2009) Prediction of flashover voltage of insulators using least squares support vector machines. Expert Syst Appl 36:10789–10798CrossRefGoogle Scholar
- Ghiasi-Freez J, Kadkhodaie-Ilkachi A, Ziaii M (2012) Improving the accuracy of flow units prediction through two committee machine models: an example from the South Pars Gas Field, Persian Gulf Basin, Iran. Comput Geosci 46:10–23CrossRefGoogle Scholar
- Gholami A, Moradi S, Dabir B (2013) A power law committee scaling equation for quantitative estimation of Asphaltene precipitation. Int J Sci Emerg Technol 6:275–283Google Scholar
- Kadkhodaie-Ilkhchi A, Rezaee MR, Rahimpour-Bonab H, Chehrazi A (2009) Petrophysical data prediction from seismic attributes using committee fuzzy inference system. Comput Geosci 35:2314–2330CrossRefGoogle Scholar
- Kaiser M (2007) A survey of drilling cost and complexity estimation models. Int J Pet Sci Tech 1:1–22Google Scholar
- Lummus JL (1970) Drilling optimization. J Pet Technol 22:1379–1388CrossRefGoogle Scholar
- Na’imi SR, Shadizadeh SR, Riahi MA, Mirzakhanian M (2014) Estimation of reservoir porosity and water saturation based on seismic attributes using support vector regression approach. J Appl Geophys 107:93–101CrossRefGoogle Scholar
- Rajabioun R, Atashpaz-Gargari E, Lucas C (2008) Colonial competitive algorithm as a tool for nash equilibrium point achievement. Lect Notes Comput Sci 5073:680–695CrossRefGoogle Scholar
- Russell BH (2004) The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes. Dissertation, University of CalgarGoogle Scholar
- Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machines. Singapore World Scientific Pub Co, SingaporeCrossRefGoogle Scholar
- Vapnik V (1995) The nature of statistical learning theory, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
- Vapnik V (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
- Vapnik V, Golowich S, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. In: Mozer MC, Jordan MI, Petsche T (eds) Advances in neural information processing systems. MIT Press, Cambridge, pp 281–287Google Scholar