Prediction of maximum slug length considering impact of well trajectories in British Columbia shale gas fields using machine learning

Abstract

In this study, the severity of slugging is assessed by predicting maximum slug lengths (MSL) quickly using the random forest (RF) algorithm based on the geometric features of well trajectories for a shale gas field. Severe slugging is one of the critical issues production engineering-wise because it causes operation shut-down. Thus it should be predicted for proactive measurements. A total of 5033 well trajectories were acquired from the northeastern area of British Columbia, Canada. The well trajectories are described using ten geometric features such as X, Y, and Z lengths in the Cartesian coordinate system, inclination, azimuth, and the other five. The 5033 well trajectories are grouped using the k-medoids clustering algorithm. The well trajectories in each group and the groups are compared visually to see if the ten features are representative enough to describe the geometric features of the well trajectories. The ten geometric features of the well trajectories are used as the input for RF, and MSL, which represents the severity of slugging, is used as the output for RF. The output data is simulation results by a pipe flow simulator, OLGA. The trained RF model provides the satisfactory prediction performance of MSL (R values, 0.866 and 0.857 for training and test data, respectively). In the trained RF model, X, Y, and Z lengths have the most significant importance among the ten geometric features. Because it is impractical to simulate all well trajectory scenarios by OLGA, the MSL values are projected onto a 3-dimensional map of which axes are X, Y, and Z lengths to visualize the trend of MSL. The 3-dimensional map showing the relation between MSL and the geometric features of well trajectories can be utilized as a quick reference to avoid severe slugging in designing well trajectories.