Oil well drilling operation is a complex process, in which there are always new lessons to be learned during drilling operation. A case-based<br>reasoning (CBR) system is used to provide an intelligent advisory system based on previous experiences. Whenever the process is running smoothly,<br>or is failing, the experiences gained during such episodes are valuable and should be stored for later re-use and prediction. Contributing features are<br>selected for characterizing an episode (case) that is compared to other cases in a case database. In this way, cases are classifed into case classes<br>according to the similarity between a new case and other cases. As most problems during drilling operation are depth dependant, the system keeps all<br>the cases and experiences in each defined depth interval to compose sequences of cases. Our aim is to implement the sequence building for the<br>purpose of predicting problems before they occur. To meet this goal, each sequence is composed of previous, present and next case.


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