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• W2064179972 abstract "Abstract The accuracy and precision of well rates are paramount in reservoir management, well performance surveillance, flow assurance, and any third party processing arrangements. Rate allocation is traditionally based on well rate tests and downtime. This method is usually time-consuming and thus performed relatively infrequently. This could be inadequate for proactive asset management especially with wells that may produce in transient state. This paper discusses methodology to improve well rate allocation quality and save engineering time. In practice, many fields face some or all of the following challenges that are related to well rate allocation: 1) reservoir communication, 2) well interference, 3) changing skin factors or other near wellbore boundaries, 4) uncertainties around reservoir fluid properties, and 5) difficulty in obtaining well rate tests for a variety of reasons. For many intelligent wells, it is a common practice to install permanent downhole gauges which are playing critical roles in field management. This paper describes a framework on how to capitalize on the real time data from well pressure and temperature sensors and use the data in Integrated Asset Modeling (IAM) to allocate the well rate with enhanced accuracy, increased frequency, and reduced processing time. The paper uses Atlantis in Gulf of Mexico as an example to demonstrate this process. The real-time data supported model based allocation process becomes virtual flow meters for the intelligent wells. For Atlantis, field-wide allocation accuracy has been improved from previously +/− 10% error using the traditional allocation method based on well rate test and downtime method to current +/−3% error using the new method. This paper also shows model maintenance is a journey that needs strenuous attention especially after water injections commences and more production wells come online." @default.
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• W2064179972 date "2012-05-14" @default.
• W2064179972 modified "2023-09-25" @default.
• W2064179972 title "Enhancing Production Allocation in Intelligent Wells via Application of Models and Real-Time Surveillance Data" @default.
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• W2064179972 doi "https://doi.org/10.2118/155031-ms" @default.
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