Application of Least Square Support Vector Machine as a Mathematical Algorithm for Diagnosing Drilling Effectivity in Shaly Formations

The problem of slow drilling in deep shale formations occurs worldwide causing significant expenses to the oil industry. Bit balling which is widely considered as the main cause of poor bit performance in shales, especially deep shales, is being drilled with water-based mud. Therefore, efforts have been made to develop a model to diagnose drilling effectivity. Hence, we arrived at graphical correlations which utilized the rate of penetration, depth of cut, specific energy, and cation exchange capacity in order to provide a tool for the prediction of drilling classes. This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important drilling engineering problems. Using the amount of cation exchange capacity of the shaly formations and correlating them to drilling parameters such as the normalized rate of penetration, depth of cut, and specific energy, the model was developed. The method incorporates hybrid least square support vector regression into the coupled simulated annealing (CSA) optimization technique (LSSVM-CSA) for the efficient tuning of SVR hyper parameters. Also, we performed receiver operating characteristic as a performance indicator used for the evaluation of classifiers. The performance analysis shows that LSSVM classifier noticeably performs with high accuracy, and adapting such intelligence system will help petroleum industries deal with the well drilling consciously. The problem of slow drilling in deep shale formations occur worldwide causing significant expense to the oil industry. Bit balling is widely considered as the main cause of poor bit performance in shale, especially deep shale are being drilled with water-based mud .Therefore, efforts have been made to develop a model to diagnose drilling ineffectivity/effectivity. Hence we arrived to graphical correlations which utilized rate of penetration, depth of cut, specific energy, and cation exchange capacity in order to provide a tool for prediction of drilling classes. This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important drilling engineering problems. Using the amount of cation exchange capacity of the shaly formations and also correlating them to drilling parameters, such as normalized rate of penetration, depth of cut, and specific energy, model was developed. The method incorporates hybrid least square support vector regression and Coupled Simulated Annealing (CSA) optimization technique (LSSVM-CSA) for efficient tuning of SVR hyper parameters. Also, we performed Receiver Operating Characteristic as a performance indicator which used for evaluation of classifiers. Performance analysis shows that LSSVM classifier noticeably perform with high accuracy and adapting such intelligence system will help petroleum industry to dealing the well drilling consciously.