Integration of real-time data and past experiences for reducing operational problems (2009)

Abstract Oil well drilling operation is a complex process, in which there are always new lessons to be learned during drilling operation. A case-based reasoning (CBR) system is used to provide an intelligent advisory system based on previous experiences. Whenever the process is running smoothly, or is failing, the experiences gained during such episodes are valuable and should be stored for later re-use and prediction. Contributing features are 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 according to the similarity between a new case and other cases. As most problems during drilling operation are depth dependant, the system keeps all the cases and experiences in each defined depth interval to compose sequences of cases. Our aim is to implement the sequence building for the purpose of predicting problems before they occur. To meet this goal, each sequence is composed of previous, present and next case. The paper presents a methodology of a semi-automatic case building and case discrimination process to make a robust sequence-based reasoning system. Our approach addresses the task of developing an intelligent system for prediction through sequeces. A CBR platform, developped in our group, was used and employed in the oil well drilling domain. The methodology was applied in one well section in the North Sea and the methodology clearly showed its ability through the good results which were obtained. Introduction Drilling a well is associated with different activities such as drilling, circulation, tripping in and out, and reaming. It usually takes a month, but unwanted problems leads to additional time. Encountering problems during drilling is inevitable. Hole cleaning and wellbore restriction is a serious problem in the oil industry. It can lead to delay in the drilling process, increase in drilling cost, and in some cases even abandonment of the well. The prediction of upcoming situations will help to avoid downtime and serious problems which may lead in worst scenarios to abandoning the whole or part of the well section. Case based reasoning is an approach to solving problems and gaining lessons learned for future use [1]. This technique has been employed in different domains of petroleum engineering. In 2002, Bhushan at al. [2] applied CBR to globally search for reservoir analogues as an important step in planning a new field development. CBR has also been tried in the oil well drilling domain by different groups around the world. One of the first applications of CBR was documented by CSIRO and later refined by Karvis and Irrgang [3]. The technique was applied to derive alternate drilling plans based on previously drilled wells. Each well was represented as one case. Skalle et al. [4, 5] described a system for prevention of unwanted events in the domain of offshore oil well drilling. Mendes et al. [6] presented application of CBR in offshore well design. The intention of their work was to formalize a methodology applied to petroleum well design in the context of case-based reasoning. They also used fuzzy set theory to deal with indexing attributes. Perry et al. [7] described the development of a case-based knowledge repository for drilling optimization. Later, Abdollahi et al. [8] explained the applicability of CBR for diagnosis of well integrity problems to reduce the risk of uncontrolled release of formation fluids through the lifecycle of a well. CBR was also implemented for planning and execution of well interventions. In this regard, Popa et al. [9] presented a specific application of CBR to determine the optimum cleaning technique for sanded/ seized well failures. The number of publications about application of the CBR in oil well drilling indicates that this is a potential method for reducing the cost of drilling by using previous experiences, hidden in reports and/or known by experts. Although, CBR applications are still at its beginning in the oil industry.