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A Review on Anomaly Detection Methods for Optimizing Oil Well Surveillance


Mohd Hilmi Hasan, Azlinda Abdul Malik and Mazuin Jasamai


Vol. 17  No. 11  pp. 151-155


Current economic realities has pushed oil and gas company to produce “more with less”. The ever increasing amount of data available to surveillance engineers has caused engineers to spend more time gathering, analyzing them manually which is definitely a daunting exercise and inefficient. Leveraging on data driven surveillance by adopting the principle of management by exception (MBE), the project tries to minimize the manual interaction between data and engineers. The study will focus on monitoring the well production performance through pre-determined parameters with each set of rules. A model (with a certain algorithm) will be built to identify any deviations from the pre-set rules and the model will alert user for deviations that occur. Prediction will be done on when the well be offline if the problem keep on persisting without immediate action from user. The primary benefit of the study is it will allow for proactive measure, faster response time for well intervention, minimize well downtime, safeguard the production as well as contribute to cost saving. Other benefits include better use of practitioner’s time (focus on analysis rather than identification), elimination of repetitive data gathering and reformatting tasks, consistency and repeatability of evaluation and better knowledge management. To embark on this study, this paper intends to review previous works related to anomaly detection. The aims are to identify and discuss the characteristics of the available approaches and techniques with respect to adopting MBE in oil well surveillance, and to discover the performance of the techniques so that they can be used to deliver the anomaly detection in surveillance.


Well surveillance, anomaly detection, outlier detection, oil production prediction.