Mahdi Imanian - Department of industrial engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Aazam Ghassemi - Department of industrial engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Mahdi karbasian - Department of industrial engineering, Malek-Ashtar University of Technology, Shahinshahr, Iran

This work used two methods for Monitoring and control of autocorrelated processes based on time series modeling. The first method was the simultaneous monitoring of common and assignable causes. This method included applying five steps of data gathering, normality test, autocorrelation test, model selection and control chart selection on all non-stationary process observations. The second method was a novel one for the separate monitoring and control of common and assignable causes. In this method, the process was divided into the parts with and without assignable causes. The first method was greatly non-stationary due to not separating common and assignable causes. This method also implied that the common causes were hidden in the process. The novel method for the separate monitoring of common and assignable causes could turn the process into a stationary one, leading to identifying, monitoring, and controlling common causes without any interference from the assignable causes. The results showed that, unlike the first method, the second method could be very sensitive to the common causes; it could, therefore, suitably monitor, identify and control both assignable and common causes. The current work was aimed to use control charts to monitor and control the bootomhole pressure during the drilling operation.  

Statistical Process Control, Control Chart, Autocorrelated Process, Bit Pressure, Kick