Drilling data quality improvement and information extraction with case studies
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ORIGINAL PAPER-PRODUCTION ENGINEERING
Drilling data quality improvement and information extraction with case studies Suranga C. H. Geekiyanage1 · Andrzej Tunkiel1 · Dan Sui1 Received: 8 June 2020 / Accepted: 13 October 2020 © The Author(s) 2020
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. Keywords Drilling data · Quality improvement · Information extraction
Introduction While all other industries are aligned with digital evolution, oil and gas operations have also been taking advantage of the importance of digital and automatic technique transformation. Oil and gas industry is arguably in a new wave of digital oilfields, with a growing consensus toward intelligent and digital operations, and predictive maintenance. In recent years, hot topics such as digitalization, automation, artificial intelligence, drilling robots, deep learning, digital twins and big data have evolved from being envisions on the paper to state of the art solutions, expected to revolutionize drilling efficiency and safety. Recently, the growing interests and trends in the oil and gas industry coupled with new intelligent sensing technologies have resulted in an overwhelming amount of data in need of having useful and valuable information in surface and down-hole environment, improving real-time decision support, enabling precise control of drilling processes, mitigating drilling incidents, optimizing drilling processes and * Dan Sui [email protected] 1
Energy and Petroleum Engineering Department, University of Stavanger, Stavanger, Norway
providing visibility of wellbore conditions for real-time drilling operations, see (Thonhauser 2018; Saputelli 2020; Rassenfoss 2020; Donnelly et al. 2020; Dursun et al. 2014; Lu et al. 2017; Aibar et al. 2018). However to realize the full potentials and deal with the challenges/issues of data, as well as to develop digital, automatic and intelligent data management processes, some research questions are raised (Hegde and Gray 2017; Thonhauser 2004; Nybo and Sui 2014; Saptawati and Nata 2015). Among them, two main discussions are: • how to develop p
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