Real time optimization of drilling parameters during drilling operations aims to optimize weight on bit, bit rotation speed for obtaining maximum drilling rate as well as minimizing the drilling cost. The process is formation specific. A Statistical method namely multiple linear regression technique is used for the drilling optimization methodology. An extensive literature survey on drilling optimization was conducted for this research study. A model is developed for this purpose using actual field data collected through modern well monitoring and data recording systems, which will be predicting the rate of drilling penetration as a function of available parameters. The rate of penetration general equation is optimized for effective functions at each data point. In order to optimize the parameters in the field, a computer network is required to be developed. The computer network will keep the piped data directly from the data source, and continuously be collecting the new data to be piped. A database present at the central computer will be continuously calculating the developed model parameters by means of multiple regression technique and inform the team at the field. The field engineer will transmit the current drilling parameters back to the central computer, and the headquarters will determine the new model parameters and optimum drilling parameters by including the recently received information. Therefore, there will be a real-time-optimization process. It is considered that this technique is going to be widely used in future drilling activities since it could reduce drilling costs and minimize probability of encountering problems due to working with optimized parameters.

It has been found that drilling rate of penetration could be modeled in real-time environment as function of independent drilling variables such as weight on bit, rotation speed of the string, mud weight, and formation characteristics. The ability to have the drilling rate of penetration with respect to depth characteristically with certain parameters for specific formations on real-time basis could bring new insights to the nature of drilling operations. Any departure from the trend could have significant reasons. The study has also achieved one of its objectives, giving the optimized independent drilling parameters found following statistical synthesis.

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