Prediction of Frictional Drag and Transmission of Slack-off Force in Horizontal Wells Using Neural Networks

Abstract

Abstract The use of mud motors and other tools to accomplish forward motion of the bit in extended reach and horizontal wells allows avoiding large amounts of torque caused by rotation of the whole drill string. The forward motion of the drill string, however, is resisted by excessive amount of friction. In the presence of large compressive axial loads, the drill pipe or coiled tubing tends to buckle into a helix in horizontal boreholes. This will cause additional frictional drag resisting the transmission of slack-off force to the bit. As the magnitude of the frictional drag increases, a buckled pipe can become "locked-up" making it almost impossible to drill further. In case of packers, the frictional drag may inhibit the transmission of set-up load to the packer. A prior knowledge of the magnitude of frictional drag for a given axial force and radial clearance can help avoid lock-up conditions and costly failure of the tubular. In this study, we present a neural network model for the prediction of frictional drag and slack-off load transmission in horizontal wells. Several neural networks with different architecture were designed and tested to obtain the most accurate prediction of these parameters. After cross-validation of the neural network, a two-hidden layer model was chosen for simultaneous prediction of frictional drag and slack-off load transmission.