Abstract
Stuck pipe is a common worldwide drilling problem, resulting significant increases in non-productive time and overall well cost. Many oil and gas reservoirs are mature and are becoming increasingly depleted of hydrocarbons which make stuck pipe more severe risks. This is due to the fact that decreasing pore pressure increases the chance of stuck pipe. Minimizing the risks of stuck pipe while drilling has been the goal of many operators recently. This paper describes a robust support vector regression (SVR) methodology that offers superior performance for stuck pipe prediction either mechanically or differentially using available drilling parameters. A new model is developed using drilling parameters such as measured depth, mud weight, plastic viscosity, yield point, gel strengths, PH and solid percent from different wells. 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. The algorithm is applied to classify the stuck types, i.e., differential stuck or mechanical stuck. Performance analysis shows that LSSVM classifier has high accuracy. Using intelligent system would help drilling industry to reduce Non-Productive Time (NPT) during operation in complex zones.
