Drilling, being one of the key methods of investigation of the earth’s interior and extraction of hydrocarbons, represents an interdisciplinary domain, which brings together geological and technical, qualitative and quantitative, well-understood and purely intuitive knowledge. Specialists from different fields (engineers, geologists, geophysicists, economists) perceive the issues of drilling differently, and understanding may be readily hampered by discordance of personal perceptions of collaborators or a teacher and student. To efficiently plan and manage drilling campaigns, support teamwork, handle problems and adequately interpret and process the drilling data, a unified framework is desired, which could reconcile different perceptions.
In recent years, quite a few studies appeared on application of knowledge engineering and representation in hydrocarbon industry, which largely deal with drilling (e.g., Xu, 2012). However, following the mainstream of knowledge engineering – the ontology design, these studies, as pointed by Norheim and Fjellheim (2006), come to a challenge that the knowledge processes are not well connected to existing core working processes. In our opinion, this challenge cannot be met sufficiently well by the said approach because of the static nature of ontology (Gruber, 2009) and clearly dynamic character of the modeled environment of drilling that implies changes from one event to another in a single scenario. Representation of a drilling scenario as a labeled, bi-directional graph that consists of nodes representing concepts, which are connected by links representing relations (Skalle and Aamodt, 2005), even provided the relation types also have their semantic definition, i.e. are concepts themselves, does not capture this change. The solution is deemed to be found in the newly-emerged subfield of dynamic knowledge engineering and representation, the key method of which is the event bush.
The research is carried out by the Geognosis team and Pavel Komar from the Donetsk National Technical University, Donetsk People’s Republic.
Gruber, T., 2009. Ontology. In: Liu, L., and Özsu, M.T. (Eds.), Encyclopedia of Database Systems, Springer-Verlag, in 5 volumes.
Norheim, D., and Fjellheim, R., 2006. AKSIO — Active Knowledge management in the petroleum industry. In: Leger, A., Kulas, A., Nixon, L., and Meersman, R. (Eds.), How Business application challenges Semantic Web Research (ESWC’06 Industry Forum), Budva, Montenegro, p. 22-26. Available from: http://ceur-ws.org/Vol-194/complete.pdf.
Skalle, P., and Aamodt, A., 2005. Knowledge-based decision support in oil well drilling: combining general and case-specific knowledge for problem solving. In: Shi, Zhongzhi, and He, Qing (Eds.), Intelligent information processing II. Springer-Verlag, London, UK: p. 443-455.
Xu, Yingzhuo, 2012. Research on Method of Ontology-Based Knowledge Representation and Integration for Drilling Risk Decision. International Journal of Digital Content Technology and its Applications, v. 6, no.16, doi:10.4156/jdcta.vol6.issue16.33.