Calibration Technology and Application of Mud Logging Sensors Based on Artificial Intelligence

Mud logging serves as the “eyes” of exploration and development, acting as a counselor for drilling safety, the center of information transmission, and holding the first-hand data on oil and gas exploration and development. With the rapid development of informatization, digitization, intelligence, and remote support systems, the demand for high-quality mud logging data has continuously risen, where sensor calibration and calibration technology serve as the foundation for ensuring accuracy and reliability. This paper proposes an artificial intelligence-based comprehensive mud logging instrument sensor calibration and calibration technology, targeting the issues of prolonged service life, low precision, and low inspection rate of traditional mud logging instruments. The technology primarily involves collecting and pre-processing sensor output data such as filtering, sampling to eliminate noise, and improve the dataset's quality. Mathematical models of sensors were constructed using machine learning or deep learning algorithms to analyze the relationship between sensor outputs and actual values, which could also compute sensor errors and uncertainties. Algorithm optimization methods such as wavelet transform and adaptive filtering were used to process and analyze sensor data for different types of sensors and environmental conditions. The adaptive control algorithm was then utilized based on the predicted model results and actual measurement results to calibrate the sensor, ultimately helping to avoid errors and uncertainty in the traditional manual calibration process. Experimental results show that this technology has higher accuracy and reliability than traditional calibration techniques while maintaining simple operation, fast speed, and cost-effectiveness. This technology improves the level of detection and evaluation technology of comprehensive mud logging instruments, Standardizes mud logging equipment management, and plays an essential role in timely discovering, evaluating oil and gas layers, and optimizing drilling construction safety.