PROBLEM STATEMENT Due to the availability of large quantities of real-time data, many operators are faced with the challenge of extracting meaningful information from it. In many cases, analysis of the data, the updating of models for Wells, Reservoirs and Facilities requires human intervention. Because WRFM teams do not have analysed information, there are delays in the decision making to optimise. OBJECTIVES To demonstrate how auto analysis can reduce time consuming and repetitive tasks of comparing real time data with operating envelopes. Exception based applications and the updating of multiple operating envelopes and models without human intervention, enables the analyst to concentrate on anomalies and opportunities to optimise. The constant visualisation of analysed information should assist the decision making process. METHOD • Highlight the problems of managing large quantities of data • Show how alerts can be sorted, analysed and prioritised automatically • Explain how operating envelopes and models can be auto-updated • Visualise analysed information using digital signage • Give examples of different approaches RESULTS • The initial deployment of the system produced more than expected anomalies • Automatic sorting of the alerts prevented excessive emails being generated • Human activity is concentrated on higher value-add actions • Production increased • Optimisations increased CONCLUSIONS The analysing of data by automated methods means that all well and facilities are under surveillance and updated models are available for production system optimisation. Analysts have more information and time to focus on critical issues rather than mundane tasks. APPLICATIONS This approach can be used for wells, reservoirs and facilities surveillance and model updates. INNOVATIONS 1. Monitor by exception 2. Auto updating models 3. Better visualisation 4. Increased productivity
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