Hybrid cycle reservoir with jumps for multivariate time series prediction: industrial application in oil drilling process

Abstract

Industrial oil drilling processes usually produce high-dimensional multivariate time series data, in which the significant data changes associated with key variables possibly indicate potential drilling accidents. Therefore, it is of great significance to establish a multivariate time series prediction model for the safety of drilling operations. Recently, echo state networks (ESNs) have been widely used in multitime series predictions. However, traditional ESNs use randomly generated sparse network structures and single neuron models, which makes it difficult to achieve a satisfactory performance for complex multivariate time series predictions. In response to this problem, this study proposes a novel hybrid cycle reservoir with jumps (HCRJ) which combines the hybrid wavelet neuron model with the cycle reservoir with jumps (CRJ) structure. In the face of high-dimensional data sets generated by oil drilling, this paper selects a principal component analysis algorithm combined with certain process knowledge to find key variables before related variables are specified by means of Gray correlation analysis methods. The HCRJ networks are used to realize temporal predictions based on data of related variables. To confirm the validity of the model, the HCRJ networks are applied to an oil drilling process and compared to traditional ESNs and CRJ methods. The results show that the HCRJ networks enrich the dynamic characteristics of networks without increasing the complexity of the reserve pool, which helps improve the prediction accuracy of the oil drilling sequence and reduce the occurrence of drilling accidents.