Multiphase flow metering using capacitance transducer and multivariate calibration

Øyvind Midttveit; Viktor Berge; Eivind Dykesteen

doi:10.4173/mic.1992.2.1

Multiphase flow metering using capacitance transducer and multivariate calibration

Modeling Identification and Control A Norwegian Research Bulletin ◽

10.4173/mic.1992.2.1 ◽

1992 ◽

Vol 13 (2) ◽

pp. 65-76 ◽

Cited By ~ 4

Author(s):

Øyvind Midttveit ◽

Viktor Berge ◽

Eivind Dykesteen

Keyword(s):

Multiphase Flow ◽

Multivariate Calibration ◽

Capacitance Transducer ◽

Flow Metering

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A new FPGA-based terahertz (THz) imaging device for multiphase flow metering

Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XIV ◽

10.1117/12.2593071 ◽

2021 ◽

Author(s):

Faisal Shehaz

Keyword(s):

Multiphase Flow ◽

Thz Imaging ◽

Imaging Device ◽

Flow Metering

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Coupled Heat and Mass Transfer CFD Model for Methane Hydrate

Volume 10: Petroleum Technology ◽

10.1115/omae2015-42258 ◽

2015 ◽

Author(s):

Eugenio Turco Neto ◽

M. A. Rahman ◽

Syed Imtiaz ◽

Thiago dos Santos Pereira ◽

Fernanda Soares de Sousa

Keyword(s):

Natural Gas ◽

Multiphase Flow ◽

Gas Hydrate ◽

Gas Flow ◽

Three Dimensional ◽

Hydrate Formation ◽

Cfd Model ◽

Flow Metering ◽

Ansys Cfx ◽

Gas Hydrate Formation

The gas hydrates problem has been growing in offshore deep water condition where due to low temperature and high pressure hydrate formation becomes more favorable. Several studies have been done to predict the influence of gas hydrate formation in natural gas flow pipeline. However, the effects of multiphase hydrodynamic properties on hydrate formation are missing in these studies. The use of CFD to simulate gas hydrate formation can overcome this gap. In this study a computational fluid dynamics (CFD) model has been developed for mass, heat and momentum transfer for better understanding natural gas hydrate formation and its migration into the pipelines using ANSYS CFX-14. The problem considered in this study is a three-dimensional multiphase-flow model based on Simon Lo (2003) study, which considered the oil-dominant flow in a pipeline with hydrate formation around water droplets dispersed into the oil phase. The results obtained in this study will be useful in designing a multiphase flow metering and a pump to overcome the pressure drop caused by hydrate formation in multiphase petroleum production.

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Artificial Intelligence and Data Analytics for Virtual Flow Metering

10.2118/204662-ms ◽

2021 ◽

Author(s):

Anton Gryzlov ◽

Liliya Mironova ◽

Sergey Safonov ◽

Muhammad Arsalan

Keyword(s):

Machine Learning ◽

Multiphase Flow ◽

Real Time ◽

Production Control ◽

Measurement Data ◽

Sufficient Accuracy ◽

Operating Conditions ◽

Machine Learning Techniques ◽

Flow Metering ◽

Oil Gas

Abstract Modern challenges in reservoir management have recently faced new opportunities in production control and optimization strategies. These strategies in turn rely on the availability of monitoring equipment, which is used to obtain production rates in real-time with sufficient accuracy. In particular, a multiphase flow meter is a device for measuring the individual rates of oil, gas and water from a well in real-time without separating fluid phases. Currently, there are several technologies available on the market but multiphase flow meters generally incapable to handle all ranges of operating conditions with satisfactory accuracy in addition to being expensive to maintain. Virtual Flow Metering (VFM) is a mathematical technique for the indirect estimation of oil, gas and water flowrates produced from a well. This method uses more readily available data from conventional sensors, such as downhole pressure and temperature gauges, and calculates the multiphase rates by combining physical multiphase models, various measurement data and an optimization algorithm. In this work, a brief overview of the virtual metering methods is presented, which is followed by the application of several advanced machine-learning techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore. The predictive capabilities of different types of machine learning instruments are explored using a model simulated production data. Also, the effect of measurement noise on the quality of estimates is considered. The presented results demonstrate that the data-driven methods are very capable to predict multiphase flow rates with sufficient accuracy and can be considered as a back-up solution for a conventional multiphase meter.

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Implementation of Instrumentalised Virtual Multiphase Flow Metering Technology in the Wells of the Vostochno-Makarovskoye Gas Condensate Field

10.2118/202001-ms ◽

2020 ◽

Author(s):

Andrey Zozulya ◽

Vladimir Baranov ◽

Mikhail Miletski ◽

Konstantin Rymarenko ◽

Marat Nukhaev ◽

...

Keyword(s):

Multiphase Flow ◽

Liquid Hydrocarbon ◽

Operating Conditions ◽

Gas Condensate ◽

Flow Measurements ◽

Hydrocarbon Production ◽

Flow Meters ◽

Well Production ◽

Track Record ◽

Flow Metering

Summary Liquid hydrocarbon quantity optimization is among key technological indicators in the gas condensate fields development. To achieve it one needs to select and maintain optimal well-operating conditions. In this case, multiphase flow measurements are prioritized as an important optimization tool. The article presents a proven record of implementing the technology of instrumentalised virtual multiphase flow metering in the wells of the Vostochno-Makarovskoye gas condensate field to increase the efficiency of liquid hydrocarbon production. Virtual flow metering technologies that use modeling methods and adapt models to actual well-operating parameters aiming at determining well production rates are becoming increasingly popular. At that, the quality of the data at the model input does not often guarantee a qualitative determination of multiphase flow parameters. This article presents a track record of building a virtual multiphase flow meter based on single-phase streamer flow meters mounted on gas wells. Venturi flow meters were used. A series of well tests were conducted in various modes. To configure the streamer model, additional tuning studies were conducted on the separator. While testing the wells, the results of constructing a streamer model were verified by nodal analysis.

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