Advertisement

How to Model Visual Knowledge: A Study of Expertise in Oil-Reservoir Evaluation

  • Mara Abel
  • Laura S. Mastella
  • Luís A. Lima Silva
  • John A. Campbell
  • Luis Fernando De Ros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3180)

Abstract

This work presents a study of the nature of expertise in geology, which demands visual recognition methods to describe and interpret petroleum reservoir rocks. In an experiment using rock ima ges we noted and analyzed how geologists with distinct levels of expertise described them. The study demonstrated that experts develop a wide variety of representations and hierarchies, which differ from those found in the domain literature. They also reta in a large number of symbolic abstractions for images. These abstractions (which we call visual chunks) play an important role in guiding the inference process and integrating collections of tacit knowledge of the geological experts. We infer from our experience that the knowledge acquisition process in this domain should consider that inference and domain objects are parts of distinct ontologies. A special representation formalism, kgraphs+, is proposed as a tool to model the objects that support the infer ence and how they are related to the domain ontology.

Keywords

Knowledge acquisition knowledge representation expertise visual knowledge petroleum exploration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Nonaka, I., Takeuchi, H., Takeuchi, H.: The knowledge-creating company: how Japanese companies create the dynamics of innovation, vol. xi, p. 284. Oxford University Press, New York (1995)Google Scholar
  2. 2.
    Shadbolt, N.R., O’Hara, K., Crow, L.: The Experimental Evaluation of Knowledge Acquisition Techniques and Methods: History, Problems and New Directions. International Journal of Human-Computer Studies 51(4), 729–755 (1999)CrossRefGoogle Scholar
  3. 3.
    Gaines, B.R., Shaw, M.L.G.: Personal Construct Psychology and the Cognitive Revolution, p. 30. University of Calgary - Knowledge Science Institute, Cobble Hill (2003)Google Scholar
  4. 4.
    Abel, M., Castilho, J.M.V., Campbell, J.: Analysis of expertise for implementing geological expert systems. In: World Conference on Expert Systems, Cognizant Communication Offices, Mexico City (1998)Google Scholar
  5. 5.
    Leão, B.F., Rocha, A.F.: Proposed methodology for knowledge acquisition: a study on congenital heart disease diagnosis. Methods of Information in Medicine (29), 30–40 (1990)Google Scholar
  6. 6.
    Sowa, J.F.: Conceptual structures: information processing in mind and machine. Addison Wesley, Reading (1984)zbMATHGoogle Scholar
  7. 7.
    Benjamins, V.R., Fensel, D.: Editorial: problem-solving methods. International Journal of Human-Computer Studies 49(4), 305–313 (1998)CrossRefGoogle Scholar
  8. 8.
    Schreiber, G., Akkermans, H., Anjewierden, A., Hoog, R.d., Shadbolt, N., Velde, W.v.d., Wielinga, B.: Knowledge engineering and management - The CommonKADS methodology, p. 104. The MIT Press, Cambridge (2000)Google Scholar
  9. 9.
    Duda, R.O., Hart, P.E., Barret, P., Gaschnig, J., Konolige, K., Reboh, R., Slocum, J.: Development of the PROSPECTOR consultation system for mineral exploration. Stanford Research Institute International, Menlo Park (1978)Google Scholar
  10. 10.
    Schultz, A.W., Fang, J.H., Burston, M.R., Chen, H.C., Reynolds, S.: XEOD: an expert system for determining clastic depositional environments. In: Geobyte [S.l]. pp. 22- 32 (1988)Google Scholar
  11. 11.
    Gappa, U., Puppe, F.: A study of knowledge acquisition - experiences from the SISYPHUS III experiment for rock classification. In: Workshop on Knowledge Acquisition, Modeling and Management, Voyager Inn, Alberta, Canada (1998)Google Scholar
  12. 12.
    Wagner, W.P., Chung, Q.B., Najdawi, M.K.: The impact of problem domains and knowledge acquisition techniques: A content analysis of P/OM expert system case studies. Expert Systems with Applications 24(1), 79–86 (2003)CrossRefGoogle Scholar
  13. 13.
    Ericsson, K.A., Smith, J.: Toward a general theory of expertise: prospects and limits. Cambridge University Press, New York (1991)Google Scholar
  14. 14.
    VanLehn, K.: Problem-solving and cognitive skill acquisition. In: Posner, M.I. (ed.) Foundations of Cognitive Science, pp. 526–579. The MIT Press, Cambridge (1989)Google Scholar
  15. 15.
    Abel, M., Silva, L.A.L., Mastella, L.S., Campbell, J.A., Ros, L.F.D.: Visual knowledge modelling and related interpretation problem-solving method. In: Conferencia Latinoamericana de Informática - CLEI 2002 (2002)Google Scholar
  16. 16.
    Silva, L.A.L., Abel, M., Ros, L.F.D., Campbell, J.A., Santos, C.S.d.: An Image-Based Reasoning Model for Rock Interpretation. In: Workshop on Intelligent Computing in the Petroleum Industry - 18th International Joint Conference in Artificial Intelligence, pp. 27–32. Proceedings of the Second Workshop Intelligence Computing in the Petroleum Industry, Acapulco - Mexico (2003)Google Scholar
  17. 17.
    Abel, M., Silva, L.A.L.,, L.F.: d. Ros, L.S. Mastella, J.A. Campbell, and T. Novello, Petro-Grapher: managing petrographic data and Knowledge using an intelligent database application. Expert Systems with Applications (2004)Google Scholar
  18. 18.
    Mastella, L.S.: Análise de Formas de Representação para Modelar Conhecimento Inferencial - Research Report (000405143), Universidade Federal do Rio Grande do Sul: Porto Alegre. p. 72 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mara Abel
    • 1
  • Laura S. Mastella
    • 1
  • Luís A. Lima Silva
    • 1
  • John A. Campbell
    • 2
  • Luis Fernando De Ros
    • 3
  1. 1.Instituto de InformáticaFederal University of Rio Grande do Sul UFRGSPorto AlegreBrazil
  2. 2.Dept of Computer ScienceUniversity College LondonLondonUK
  3. 3.Instituto de GeociênciasFederal University of Rio Grande do Sul UFRGSPorto AlegreBrazil

Personalised recommendations