The Mechanical Specific Energy (MSE) and Statistical Analysis Approach (SAA) have been widely implemented in oil and/or gas well drilling industry to enhance the Rate of Penetration (ROP) and reduce the operation cost. This work focuses on predicting and optimizing the drilling efficiency and performance in the production section of Mishrif reservoir in southern Iraq fields. The drilling data from twenty-five wells has analyzed and examined to improve the drilling productivity relied upon MSE and statistical approaches. By using MSE technique, the minimum required energy to drill unit volume of each formation has determined to improve the drilling speed and avoid unnecessary energy consumption that may come out in the form of bit wearing / balling or vibration. The optimum energy is achieved when the MSE value comes close to the unconfined compressive strength (UCS) value that obtained from the empirical formula for limestone and shale rocks. The flounder and threshold points have recognized to optimize drilling data in the offset wells to enhance ROP in the future wells. In the statistical approach, the regression coefficients have obtained from the screened and filtered fields drilling data then the empirical equations to estimate ROP have constructed by using linear regression analysis through a commercial software. The optimization techniques lead to an impressive increase in the rate of penetration in the production section of the Mishrif reservoir. The MSE surveillance provides a reliable tool to maximize the ROP and reduce some drilling problems by using sufficient energy to drill each formation below the flounder point. An excessive energy consumption throughout drilling can be observed in the majority of wells been investigated. Thus, the non-productive time has mitigated considerably by utilizing drilling variables that have induced MSE equal to the unconfined compressive strength of the rocks. On another hand, the statistical analysis of real-time data for twenty-seven wells revealed a remarkable improvement in drilling performance by suggesting an empirical equation that predicts ROP through changing some key parameters such as Flow Rate (FL), Weight On Bit (WOB), Torque (TQ), Revolution Per Minute (RPM), Mud Weight (MWT) and Total Flow Area (TFA). The recommended drilling parameters resulted from this work can be used to reduce the drilling cost and prevent/mitigate the time-dependent failure in the production section.

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