Print Email Facebook Twitter Improving the accuracy of virtual flow metering and back-allocation through machine learning Title Improving the accuracy of virtual flow metering and back-allocation through machine learning Author Shoeibi Omrani, P. Dobrovolschi, I. Belfroid, S.P.C. Kronberger, P. Munoz, E. Publication year 2019 Abstract In this study we have investigated a fully data-driven approach (artificial neural networks) for real-time back-allocation and virtual flow metering in oil and gas production wells. The main goal of this study is to develop computationally efficient data-driven models to determine the multiphase production rates of individual phases (gas and liquid) in wells using existing measured data in fields. The developed approach was tested on simulated and field data from several gas wells. Two different type of artificial neural networks (ANNs) were tested on simulated and field data to assess the accuracy of estimations for steady-state, transients and dynamics in productions due to cyclic operation (shut-ins and restart). The results showed that ANN was capable of accurately estimate the multiphase flow rates in both simulated and field data. The accuracy of the production rates estimation depends on the type of neural networks employed, production behavior (steady-state or transients) and uncertainties in data. Subject MechanicsIndustrial InnovationE-learningFlow measurementFlowmetersGasolineLearning systemsNeural networksOil well productionComputationally efficientCyclic operationData-driven approachData-driven modelOil and gas productionProduction behaviorsProduction rates estimationNatural gas well production To reference this document use: http://resolver.tudelft.nl/uuid:cc59317e-7294-405c-92ef-9ec132c30ac6 TNO identifier 861661 Publisher Society of Petroleum Engineers SPE ISBN 9781613996324 Source Abu Dhabi International Petroleum Exhibition and Conference 2018, ADIPEC 2018, 12-15 November 2018 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.