Well log prediction while drilling using seismic impedance with an improved LSTM artificial neural networks - Wang, Heng; Xu, Yungui; Tang, Shuhang; Wu, Lei; Cao, Weiping; Huang, Xuri (Frontiers in Earth Science, 2023)
Well log prediction while drilling estimates the rock properties ahead of drilling bits. A reliable well log prediction assists reservoir engineers in updating the geological models and adjusting the drilling strategy if necessary, which is of great significance for reducing the drilling risk and saving costs. Conventional interactive integration of geophysical data and geological understanding is the main approach to realize well log prediction while drilling. In this paper, we have proposed a new artificial intelligence approach to make the well log prediction while drilling by integrating seismic impedance with three neural networks, i.e., LSTM, Bidirectional LSTM (Bi-LSTM) and Double Chain LSTM (DC-LSTM). The DC-LSTM is a new LSTM network proposed in this study and the other two are exsiting ones.These three networks are thoroughly adapted, compared and tested to fit the artificial intelligent prediction process. The prediction approach is able to integrate not only seismic information of the sedimentary formation around the drilling bit, but also the rock property changing trend through the upper and lower formations. The Bi-LSTM and the DC-LSTM networks get higher prediction accuracy than that of the LSTM network, and additionally, the DC-LSTM approach significantly promote prediction efficiency by reducing the number of training parameters and saving computational time without compromising prediction accuracy. The field data application of the three networks, LSTM, Bi-LSTM and DC-LSTM, demonstrates that prediction accuracy based on the Bi-LSTM and DC-LSTM is higher than that of the LSTM, and DC-LSTM has the highest efficiency overall.