Prediction of the rate of penetration in offshore large-scale cluster extended reach wells drilling based on machine learning and big-data techniques

Abstract

Large-scale cluster extended reach wells (ERWs) are usually drilled from one platform to target distant multiple reservoirs in offshore oil & gas development. Due to the complicated downhole environments and complex geological conditions in various azimuths, the accurate prediction of the rate of penetration (ROP) in offshore large-scale cluster ERWs drilling is a challenging task. In this work, a ROP prediction method for offshore large-scale cluster ERWs drilling based on machine learning and big-data techniques is presented. 27 input parameters affecting ROP are collected from block N of China Bohai Oilfield, with a total of 9,193,408 data points. The internal relation between various parameters and ROP at the data level is revealed, which verifies the feasibility of the collected data set training. Based on various machine learning algorithms, 12 kinds of ROP prediction models based on support vector regression (SVR), random forest regression (RFR), back propagation neural network (BPNN), and recurrent neural network (RNN) are established respectively. Meanwhile, the prediction accuracy of the ROP prediction models is evaluated based on six performance indicators. It shows that the prediction results of the RNN model with LSTM as neuron structure are the best (all of the 6 indicators are optimal) among the 12 kinds of ROP prediction models, and the mean absolute error is only 6.12 m/h. The RNN model with LSTM as neuron structure is the best machine learning model in the prediction of ROP in offshore large-scale cluster ERWs drilling. It provides a practical method for enhancing cleaner development of offshore oil & gas.