The technique of continuously recording various physical, chemical, electrical, or other properties of rock or fluid combinations penetrated by digging a well into the earth's mantle is known as well logging. Although well logging arose from the petroleum industry's unique need to assess hydrocarbon accumulations, it has applications in a variety of other fields of interest to earth scientists. The exploration of these applications begins with a detailed examination of measurement principles. Well logging is considered as requiring the integration of a variety of physical sciences, including physics, chemistry, electrochemistry, geochemistry, acoustics, nuclear, and geology. However, due to the further requirement of stability, accuracy and quick response in the development of well logging technology, sensing techniques are meeting a series of new challenges.

Complex downhole subjects motivated the development of new sensor structures. For example, ferromagnetic downhole casing defects evaluation based on electromagnetic eddy current testing technology, and electromagnetic testing methods for formation resistivity measurement, the evaluation of rock fractures by ultrasonic testing techniques. It can also be difficult to rapidly process massive amounts of data. Fortunately, many well logging systems can receive enormous data in a short amount of time. Traditional signal processing methods tackle the issue based on a designed feature project and are computed on PC, which means the results cannot be obtained timely. Another challenge includes the fusion of different sensing. Every well logging technique employs its advantages and drawbacks, which has driven us to explore the approaches of fusion between different sensing techniques. The transmission rate of logging while drilling data is very low, and only a small amount of bit condition information can be transmitted. A large amount of logging data is stored in downhole circuits, which cannot meet the real-time transmission requirements of logging while drilling. The acoustic transmission of data while drilling uses the acoustic wave as the carrier and the drill string periodically connected between the drill pipe and the coupling as the channel. However, the multipath delay of the drill string channel and the strong noise interference of the downhole bit and the ground environment seriously affect the acoustic transmission and detection, and limit the research and development of acoustic transmission technology while drilling. The acquisition of real-time logging information is of great significance to the efficient exploitation of oil in the oilfield. The logging data transmission system realizes the cable communication from the downhole logging instrument to the ground host computer. With the continuous development of sensing technology in the field of multiphase flow, increasing types of sensing instruments and sensing arrays put forward higher requirements for data transmission rate. In addition, the unique single core cable channel of the logging system and the external environment of high temperature and high pressure make the system have higher requirements for frequency band utilization and anti-interference performance.

This Special Issue aims to present innovations and improvements related to advanced sensing methods for well logging, including but not limited to the development of new sensors for well logging, signal processing, parameter estimation and feature extraction, imaging processing for well logging sensing systems, deep learning for well logging and its edge deployment, multi-sensor information fusion, etc. The target audiences are researchers and engineers who need to apply state-of-the-art and reliable well logging technologies. We welcome both original research and review articles, especially the research from authors who are working closely with well logging.

Potential topics include but are not limited to the following:

• Rapid signal processing of different well logging systems
• Data-driven methods for well logging
• New mathematical theories in well logging systems
• Artificial intelligence-based signal processing algorithms
• Development of automatic detection devices
• Algorithm deployment on FPGA or other embedded chips
• New development of instrumentation for well logging
• New or improved sensing structure for well logging
• Fusion of different sensing information
• New communication methods of logging data