Use of neural networks for the prediction of frictional drag and transmission of axial load in horizontal wellbores

• Published in: International journal for numerical and analytical methods in geomechanics Vol. 27; no. 2; pp. 111 - 131
• Main Authors: Sadiq, Tanvir, B. Gharbi, Ridha, C. Juvkam-Wold, Hans
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01-02-2003; Wiley
• Subjects: Applied sciences; Buildings. Public works; Computation methods. Tables. Charts; contact force; Exact sciences and technology; Geotechnics; horizontal drilling; Miscellaneous; neural networks; porous media; slack-off and axial loads; Structural analysis. Stresses; Road boring technique; Estimation error; Experimental test; slack-off and axial loads; Porous medium; neural networks; Borehole; Neural network; contact force; Forecast model; Friction; Drag; horizontal drilling; Numerical simulation; Axial load; porous media
• ISSN: 0363-9061; 1096-9853
• DOI: 10.1002/nag.265

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 causes additional frictional drag resisting the transmission of axial load (resulting from surface slack‐off force) to the bit. As the magnitude of the frictional drag increases, a buckled pipe may 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 load and radial clearance can help avoid lock‐up conditions and costly failure of the tubular.
In this study a neural network model, for the prediction of frictional drag and axial load transmission in horizontal wellbores, is presented. Several neural network architectures were designed and tested to obtain the most accurate prediction. After cross‐validation of the Back Propagation Neural Network (BPNN) algorithm, a two‐hidden layer model was chosen for simultaneous prediction of frictional drag and axial load transmission. A comparison of results obtained from BPNN and General Regression Neural Network (GRNN) algorithms is also presented. Copyright © 2002 John Wiley & Sons, Ltd.