The Bidirectional Gated Recurrent Unit Network Based on the Inception Module (Inception-BiGRU) Predicts the Missing Data by Well Logging Data

Abstract

As a key bridge between logging and seismic data, acoustic (AC) logging data is of great significance for reservoir lithology, physical property analysis, and quantitative evaluation, and completing AC logging data can help to obtain high-resolution inversion profiles, which can provide a reliable basis for reservoir geological interpretation. However, in the actual mining process, the AC logging data is always missing due to instrument failure and borehole collapse in many areas, and re-logging is not only expensive but also difficult to achieve. However, the AC data can be completed by other obtained logging parameters. In this paper, a bidirectional gated recurrent unit network based on the Inception module is developed to complete the AC logging data. The Inception module extracts the logging data features and inputs the extracted logging data features into the bidirectional gated recurrent unit network, which can fully consider the characteristics of the current data and the data before and after the logging sequence to complete the missing AC logging data. Experimental results show that the hybrid model (Inception-BiGRU) has higher accuracy than traditional and widely used series forecasting models (gated recurrent unit network and long short-term memory network), and this method also provides a new idea for the completion of AC logging data.

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“…For the feature extraction process, the Inception v3 can be employed. The CNN could remove data features layers by layers over the slip function of the convolutional kernel and has been widely used in various fields [27]. The main way to increase the performance of a system is by enlarging the network depth and width, but it results in difficult network training and overfitting.…”