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Top1. Introduction
In the petroleum industry, drilling rate of penetration (ROP) is a very important factor which strictly determines the drilling costs. In order to minimize drilling costs, it is required to maximize the rate of penetration. Prediction of ROP is a complicating task because of simultaneous impact of various factors on ROP. Rock strength, depth, rheological properties of mud, bottom hole pressure differential (overbalance or under balance), weight on bit, rotational speed of bottom hole assembly, fluid loss characteristics, wellbore diameter, bit type and hydraulics, and cutting transport efficiency are typical factors which control the penetration rate. As drilling expenditure is highly dependent on the ROP, therefore, reliable and accurate estimation of the ROP is of great importance to perform real time optimization during drilling, and also during well planning.
A literature review shows that several methods have been used for prediction of ROP including analytical models (Maurer, 1962; Galle and Woods, 1963; Motahhari et al., 2009), multiple regression analysis and numerical correlations (Bourgoyne and Young, 1973; Bourgoyne and Young, 1974; Tanseu, 1975; Al-Betairi et al., 1988;Fear, 1999), computer based programs (Mechem and Fullerton, 1965; Maidla and Ohara, 1991; Shirkavand et al., 2010; Hankins et al., 2014, Shishavan et al., 2015), stochastic methods (Ritto et al., 2010), semi analytical models (Alum and Egbon, 2011), evolutionary algorithms (Ping et al., 2014), response surface methodology (Keshavarz Moraveji and Naderi, 2016), and artificial neural network (Wang and Salehi, 2015, Elkatatny et al., 2017, Asadi et al., 2017; Eskandarian et al., 2017), machine learning methods (Hegde and Gray, 2017; Hegde et al., 2017).