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Improving Logging-While-Drilling Azimuthal Imaging With Deep Learning Super-Resolution
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DOI：10.1109/TGRS.2024.3513640.png)
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Abstract
En 中文
Logging-while-drilling (LWD) azimuthal imaging is a widely used well-logging technique in modern geological resource exploration. However, due to the measurement principles and data transmission capacity, the circumferential resolution of current techniques is very limited. In this article, we propose a deep convolutional network-based algorithm called azimuthal image super-resolution (AzSR), which is capable of reconstructing high-resolution borehole images with 128 fans from noisy azimuthal responses of 4/8/16 fans. To make the proposed algorithm more suitable for AzSR, techniques such as sample synthesis, circular padding, and special loss terms are introduced. The advantages and effectiveness of the proposed AzSR algorithm are demonstrated through systematic experiments and real-world applications. The results show that the proposed AzSR has significant advantages over existing algorithms in terms of noise robustness, detail reconstruction, and resolution improvement. With the super-resolution results of AzSR, detailed information about lithological interfaces, local structure, and thin layers can be clearly revealed. This will be of great value for decision-making during geosteering drilling and for fine-scale geological interpretation after drilling.
Keywords:
Imaging
Superresolution
Geophysical measurements
Fans
Drilling
Geologic measurements
Convolutional neural networks
Image reconstruction
Geology
Real-time systems
Azimuthal imaging
convolutional network
deep learning
image super-resolution
logging-while-drilling (LWD)
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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