Data Inversion of Azimuthal Electromagnetic Wave Logging While Drilling Based on LSTM Neural Network

Azimuthal electromagnetic wave logging while drilling (LWD) tool plays an important role in geosteering and reservoir evaluation, but its measurement response is not intuitive. So inversion method is needed to obtain formation information. Traditional inversion methods (i.e., Gauss-Newton method, random inversion method, etc.) are difficult to meet the requirements of real-time inversion due to the slow calculation speed. In this paper, a new inversion method based on a long and short-term memory (LSTM) artificial neural network was proposed to obtain formation resistivity. Firstly, the forward algorithm was established based on the method of generalized reflection coefficient to produce the sample set. Then, the LSTM neural network model was built, and it was trained and tested on the sample set. The appropriate network parameters were optimized by the traversal method. Finally, the resistivity inversion was completed on the test set. The inverted resistivity was compared with the forward resistivity, and the inversion time and relative error were compared as well. Meanwhile, the anti-noise property of the model is verified by adding white noise to the test set. The results show that the model can accurately and rapidly invert formation resistivity and can invert data containing noise, indicating that the model has good robustness. This inversion method can provide a new idea and direction for logging data processing.