Research on Ultrasonic Image Compression in Logging while Drilling: An Asymmetric Convolutional Autoencoder
Wei Zhang, Жипенг Ли, Ao Qiu, Bin Luo, Yibing Shi, Hu Sun, Yanjun Li · 2023
Logging while drilling (LWD) is an efficient technique that provides real-time information about the reservoir while drilling. However, due to the limited transmission rate of the cables, only low-dimensional information can be transferred rather than high-dimensional logging images. This paper proposes an image compression method called the asymmetric convolutional autoencoder (ACAE) that can map raw logging data into low-dimensional embedding vectors for real-time transmission through the cables. The trained ACAE consists of two primary components: an encoder deployed onto the downhole computing module and a decoder deployed onto the ground computer. To decode low-dimensional embedded information with higher quality, attention modules are utilized in the decoder to suppress noise and attain smoother logging images. Experimental results demonstrate that our method can effectively compress original logging data with a small compression rate and reconstruct logging images.