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

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.