Improving the accuracy of virtual flow metering and back-allocation through machine learning

Improving the accuracy of virtual flow metering and back-allocation through machine learning

Shoeibi Omrani, P.
Dobrovolschi, I.
Belfroid, S.P.C.
Kronberger, P.
Munoz, E.

2019

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.

Mechanics
Industrial Innovation
E-learning
Flow measurement
Flowmeters
Gasoline
Learning systems
Neural networks
Oil well production
Computationally efficient
Cyclic operation
Data-driven approach
Data-driven model
Oil and gas production
Production behaviors
Production rates estimation
Natural gas well production

http://resolver.tudelft.nl/uuid:cc59317e-7294-405c-92ef-9ec132c30ac6

861661

Society of Petroleum Engineers SPE

9781613996324

Abu Dhabi International Petroleum Exhibition and Conference 2018, ADIPEC 2018, 12-15 November 2018

conference paper