Research on preprocessing methods for monitoring drilling data
To address challenges associated with processing multi-dimensional heterogeneous monitoring drilling data,this study conducts an analysis of the characteristics of multi-borehole drilling data and introduces a data pre-processing method leveraging statistical analysis and machine learning techniques.This methodology begins with an examination of the original drilling data's features,establishes criteria for extracting stable drilling stage data,de-fines parameters for identifying abnormal drilling data,and assesses the effectiveness of various methods for repai-ring missing drilling data and reducing noise.The results indicate that the two-point linear interpolation method a-chieved the lowest error index and the most effective missing data interpolation.Additionally,the Butterworth filter demonstrated optimal filtering performance across various types of noise.Subsequently,a lightweight automatic preprocessing software for drilling data is developed,and its efficacy in swiftly completing tasks such as data extrac-tion,classification,repair,and noise reduction is validated through engineering case studies.This endeavor fur-nishes a dependable theoretical framework and empirical data support for practical applications in engineering.
digital drillingdata preprocessingmachine learningmissing data interpolationnoise reduction
万方数据
维普
