The Data Supplement Method of Azimuthal EM LWD Based on Deep Learning

Abstract

The data of azimuthal electromagnetic (EM) Logging-While-Drilling (LWD) tool is crucial for controlling and optimizing the trajectory of the wellbore, making it a key technology in geosteering. However, the measurement of the tool involves multiple frequencies, spaces, and sectors, leading to a significant volume of measured data that can’t be uploaded in real-time. Attempting to invert formation resistivity and boundaries based solely on the limited data that transmitted to the surface may not accurately reflect the true formation model. Therefore, this paper proposes a method for supplementing the measurement curves of the tool based on deep learning. The intelligent method can predict the missing logging information according to limited data and improve the utilization efficiency of logging data. Firstly, the database of azimuthal EM LWD is generated using various synthetic formation models and numerical forward modeling techniques, and the complete logging data is artificially separated into known logging data and missing logging data. Then, three deep learning models are established based on LSTM, GRU, and UNET networks respectively, and use the above sample database for training and testing them. The results demonstrate that missing curves of the tool’s measurement can be accurately and efficiently predicted using deep learning techniques. Finally, the original logging data and the complete logging data after supplementing are used for inverting the formation information. The result shows that the latter yields higher inversion accuracy. Moreover, the difference in inversion accuracy will grow as the complexity of the formation model increases after data supplementing. Therefore, the data supplement of azimuthal EM LWD by deep learning is very important for the accurate inversion of complex formation models.

Keywords

• Data supplementing
• azimuthal EM LWD
• deep learning
• inversion
• geosteering