A multiple model framework based on time series clustering for shale gas well pressure prediction

Abstract Production performance analysis of shale gas based on dynamic parameters is now playing a more important role as a scientific basis for gas well development plan, and describes the structural features, evolution and predict the future production trends. Owing to multi-gradient of gas demand and high fluctuation of the yield adjustment period, it is difficult to identify whether the change of production parameters of gas well, especially the pressure is caused by natural resource consumption or manual production adjustment. Therefore, time-series prediction for adjustable yield wells is an extraordinarily important and challenging task. In this paper, a prediction framework with a multiple model is proposed. Specifically, the weighted warped K -means clustering (WWKM) algorithm is first presented to partition the dataset into a series of clusters considering the significantly different influence of each variable. Thereafter, a multiple prediction model based on sequence information (MMP-SI) is designed to improve the prediction precision by integrating the overall decreasing trends and local fluctuation features of the dataset. Subsequently, the proposed framework is applied to pressure prediction of real time-series data of three shale gas adjustable yield wells for the Fuling region in China. The experimental results show that the proposed framework provides good prediction precision over other state-of-the-art models in terms of different evaluation criteria. The main benefits of this research study are to better simulate shale gas wells in the future for engineers and academic researchers.