Pattern Recognition of the Vertical Hydraulic Fracture Shapes in Coalbed Methane Reservoirs Based on Hierarchical Bi-LSTM Network

Abstract

Pattern recognition of the hydraulic fracture shapes is very important and complex for the refracturing design of coalbed methane (CBM) wells. In this paper, we explore a new idea by regarding the pattern recognition process as understanding what a CBM reservoir “says” during hydraulic fracturing. Then we present a hierarchical Bidirectional LSTM (Bi-LSTM) network to recognize the pattern of hydraulic fracture geometry in CBM reservoirs. Inputting the wavelet denoised sequences of data to the presented network, we can extract the implicit features of the hydraulic sand fracturing operation curves and automatically combine them to make the classification of the fracture shapes. With this method, we can cope with the problems happened in early stage of the CBM field development such as the lack of monitoring wells and the information of rock mechanics. Moreover, the experiences of the engineers and the measured data are combinationally used, which can efficiently reduce the subjectivity and assist the engineers to make the refracturing design. The validity of this method is verified by the testing data and comparing with the simulated results of Fracpro PT software.