Abstract: Oil well drilling is a complex process which frequently is leading to operational problems. The process generates huge amounts of data. In order to deal with the complexity of the problems addressed, and the large number of parameters involved, our approach extends a pure case-based reasoning method with reasoning within a model of general domain knowledge. The general knowledge makes the system less vulnerable for syntactical variations that do not reflect semantically differences, by serving as explanatory support for case retrieval and reuse. A tool, called TrollCreek, has been developed. It is shown how the combined reasoning method enables focused decision support for fault diagnosis and prediction of potential unwanted events in this domain.

As single-paradigm reasoning methods become more mature and understood, research activities targeted at combining them become more frequent. Methods that combine case-based reasoning with a model-based component are attracting a growing number of researchers within the case-based reasoning community. There is clear evidence of synergy effects, in that the resulting systems become both more competent and more efficient than if only a single reasoning method is used. The catch is that the burden on the knowledge engineer and domain expert is increased, due to the need for developing an explicit model of general domain knowledge. Methods developed within the knowledgeacquisition and modelling communities, however, as well as work on reusable ontologies, can provide some help. In particular, the notion of knowledge-level modelling has proved to be a useful one in this context. The type of application targeted in our research is decision making in complex environments, such as oil well drilling. Building application systems in such domains further calls for a clarification of basic terms such as “data”, “information”, and “knowledge”, related to their roles in cognitive and computational information processing. For case-based reasoning this becomes particularly relevant, since a case can hold structures of various basic kinds. In the Creek system, developed in our group, there is a strong coupling between cases and general domain knowledge, in that cases are embedded