A Real-Time Friction Prediction Model for in Service Drill String Based on Machine Learning Methods Coupling with Mechanical Mechanism Analysis

With highly deviated wells and horizontal wells are widely used in the oil industry. The large slope well sections and long horizontal well sections will lead to a sharp increase of the drill string torque and friction, which may reduce the drilling efficiency, and even lead to accidents. Therefore, real-time and accurate analysis of drill string’s torque and friction is an urgent problem facing by the modern drilling technology. The paper established a real-time friction prediction model that combines machine learning methods with drill string mechanical mechanism analysis model. Based on 84000 sets of field monitoring data obtained on-site, a regular data training set for weight on bit (WOB) and torque prediction was constructed with 23 types of time-series related parameters and 10 types of timing independent parameters. Relationships between time-series related parameters and timing independent parameters with the weight on bit and torque were trained to utilize long and short-term memory (LSTM) neural network and muti-layer back propagation (BP) network respectively. The new developed LSTM-BP neural network achieves high-precision prediction results of WOB and torque with a relative error of less than 14%. Based on derived WOB and torque prediction results, a theoretical mechanical analysis model of the entire drill string was adopted in this paper to develop the quantitative relation between WOB and torque with the friction coefficient of the drill string and oil casing. Suitable friction coefficients along the drill string can be finally obtained by solving the equilibrium function between predicted WOB, torque and measured hook load, rotary-table torque via an iteration algorithm. A case study was performed finally using the proposed intelligent analysis method to calculate the friction coefficients. This proposed methodology can be referenced to decrease the sticking risks and improve the drilling efficiency, which can finally increase the extension limit of horizontal wells in complex strata.