Application of long short-term memory network for wellbore trajectory prediction

Abstract

For improving the accuracy of wellbore trajectory prediction, especially in the build section, a new prediction model based on long short-term memory (LSTM) network was proposed. At the same time, the model was built by Python language and TensorFlow library. The well inclination and azimuth angle were predicted by the LSTM model. Moreover, the prediction accuracy of the LSTM was compared with that of the minimum curvature method. The results show that the average prediction error of the LSTM is much 50% lower than the minimum curvature method, and the proposed model in this article is more consistent with the actual drilling data. This method does not rely on any assumption of the path shapes and geometries. In addition, the model proposed in this article is easy to use and convenient for the engineering field application without the derivation of mathematical formulas.