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Automatic Case Capturing for Problematic Drilling Situations

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Case-Based Reasoning Research and Development (ICCBR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8765))

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Abstract

Building a high quality case base for knowledge intensive Case-Based Reasoning (CBR) applications is expensive and time consuming, especially when it requires manual work from experienced knowledge engineers. This paper presents a clustering-based method for capturing cases in time series data within the oil well drilling domain. We present a novel method for automatically detecting and capturing predictive cases originally created by domain experts. The research presented is evaluated within Verdande’s DrillEdge, in which until today case capturing is an experience-driven and thoroughly manual process. Our findings show that this process can be partially automated and customizing an individual CBR application in a complex domain can be further developed.

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Author information

Authors and Affiliations

  1. Verdande Technology AS, Trondheim, Norway

    Kerstin Bach & Odd Erik Gundersen

  2. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

    Odd Erik Gundersen, Christian Knappskog & Pinar Öztürk

Authors
  1. Kerstin Bach
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  2. Odd Erik Gundersen
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  3. Christian Knappskog
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  4. Pinar Öztürk
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Software Engineering, Université Laval, G1K 7P4, Québec, Canada

    Luc Lamontagne

  2. IIIA, Artificial Intelligence Research Institute CSIC, Spanish Council for Scientific Research Campus UAB, 08193, Bellaterra, Catalonia, Spain

    Enric Plaza

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© 2014 Springer International Publishing Switzerland

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Bach, K., Gundersen, O.E., Knappskog, C., Öztürk, P. (2014). Automatic Case Capturing for Problematic Drilling Situations. In: Lamontagne, L., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 2014. Lecture Notes in Computer Science(), vol 8765. Springer, Cham. https://doi.org/10.1007/978-3-319-11209-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-11209-1_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11208-4

  • Online ISBN: 978-3-319-11209-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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