Abstract
Abstract The management of producing wells activated with ESP pumps has to take into account several problems: limited pump run life, production losses due to multi ESP failure and shut in wells. Monitoring of production and pump parameters is common use in the oil industry, but real time monitoring systems undoubtedly provide operators with the possibility of improvements of production conditions because the easy and rapid access to data allows engineers to plan effective and timely remedial jobs. This paper is a case history of a field test performed in an unmanned platform of a brown field with producing wells activated by electrical submersible pumps and shows the advantages related to the application of this innovative technology in terms of early diagnosis and problem solution. The methodology includes the following sequential stages: Design and installation of an ad hoc surveillance system with remote control and Multiphase Meter;Monitoring of several flow periods to identify different well behaviors: Stable flow, Anomalous Flow and No flow conditions. On the basis of stable flow conditions, the regular behavior is set using the well and pump performance;Identification of the base line reference values for the 2 main parameters (Tubing head pressure and Current) during different well behaviors;Smart Alarms Setting to detect abnormal behavior and identify related problems. The smart alarms status can then be directly sent to field production supervisors and decision makers. The pilot duration was 3 months long and the benefits directly related to the application of the Smart Alarms system were the following: oil production increase ≈ 10% − 20%production losses avoided ≈ 1500reduction of OPEX in terms of savings ≈ 1,000 K€pump run life increase not quantified due the test's short duration The advanced information provided to the decision makers brought additional benefits to oil company leading to early production recovery, improvement of well inflow and outflow performance knowledge. The Smart Alarms approach can be also applied to other well typologies and data sources.
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