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Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, realtime data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models. KeywordsNeural network, Rate of penetration, Optimization, Drilling. Nomenclature ROP Rate of Penetration (ft/hr) h Bit wear fraction Ab Area of bit (in ) q Flow rate (gal/min) D Hole Depth (ft) DOC Depth of cut (ft/min) WOB Weight on Bit (klbs) bo − b6 Regression constants RPM Rotation per minute d Diameter of bit (in) SPP Stand Pipe Pressure (psi) s Standard Deviation gp Pore pressure gradient (lb/gal) n Number of samples dn Diameter of nozzle (in) μ Apparent viscosity (Centipoise) ρc ECD at the bottom (lb/gal) N Number of rotation DIFFPRESS Differential pressure (psi) a1−8 Constants of Bourgyne model

Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics