Drilling data quality improvement and information extraction with case studies

Abstract

Data analytics is a process of data acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that decision-making, actions executing, events detecting and incidents managing can be handled in an efficient and certain manner. However, data analytics also meets some challenges, for instance, data corruption due to noises, time delays, missing and external disturbances, etc. This paper focuses on data quality improvement to cleanse, improve and interpret the post-well or real-time data to preserve and enhance data features, like accuracy, consistency, reliability and validity. In this study, laboratory data and field data are used to illustrate data issues and show data quality improvements with using different data processing methods. Case study clearly demonstrates that the proper data quality management process and information extraction methods are essential to carry out an intelligent digitalization in oil and gas industry.