Abstarct: Drilling oil and gas wells are among the most challenging and expensive rock works that affecting parameters include: geology formation, drilling technology & human factor. So it is essential to find a way to predict rock properties and analyze operation performance. Formation is the place where bit encounter rock so it is necessary to know it better by measure/estimate geomechanical properties of rock. Calculation of Mechanical Specific Energy (MSE) is the method that is used in this research to measure operation performance. MSE is energy required to remove one unit volume of rock that has to be minimized to increase performance. MSE value is calculated using weight on bit, penetration rate, torque and string rotation speed. In this research initially made comprehensive data baxse by collecting drilling data, petrophysical logs and results of core lab tests. Using logs dynamic 1-D geomechanical model built and calibrated using core data. Testing model to predict breakout observed in borehole confirmed its applicability. MSE is calculated for interval. Quantitative analysis of data showed in performance drilling interval specific energy has good correlation with UCS, Poisson’s Ratio and depth. Also main parameter affecting MSE in coring operation is UCS. So by changing plan found that it is better to correlate MSE with petrophysical logs including density, shear wave slowness and GR for coring. In all stages analysis made using statistical regression and Neural Networks (NN) that NN gives better results. Optimized drilling condition occurred in ROP greater than about 17 ft. /hr. & MSE less than 2 GPa. So using N.N. to predict UCS baxsed on inputs (ROP, WOB and TQ.) can give real-time estimation of UCS and enable driller to adjust drilling parameters to UCS and reach optimum drilling condition.