A Multisensor Intelligent Device for Real-Time Multiphase Flow Metering in Oil Fields

Meribout, Mahmoud; Al-Rawahi, Nabeel; Al-Naamany, Ahmed; Al-Bimani, A.; Al-Busaidi, K.; Meribout, Adel

doi:10.1109/tim.2009.2028210

Cited by 48 publications

(24 citation statements)

References 13 publications

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“…Osman [26] presented two feedforward neural networks, one to estimate the liquid holdup and the other to identify the flow regime based on the information of superficial gas and liquid velocities, pressure, temperature, and fluid properties. Meribout et al [27] integrated neural networks into a complex multisensory multiphase flow metering system to derive phase fractions and phase flow rates based on capacitance, conductance, and ultrasonic and differential pressure signals. Arubi and Yeung [28] applied neural networks to determine the flow rates of individual phases in two phase air-water and three phase air-oil-water flows based on the statistical features of photon counts signals from a single-energy gamma densitometer.…”

Section: Neural Networkmentioning

confidence: 99%

Two-Phase Air–Water Slug Flow Measurement in Horizontal Pipe Using Conductance Probes and Neural Network

Fan

Yan

2014

IEEE Trans. Instrum. Meas.

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This paper presents a method to obtain gas and liquid flow rates of two-phase air-water slug flow in a horizontal pipe through conductance probes and neural network. Contrary to statistical features commonly used in other works, five characteristic parameters of the mechanistic slug flow model are extracted from conductance signals, i.e., translational velocity, slug holdup, film holdup, slug length, and film length, which are used as the neural network inputs. The translational velocity is obtained through cross correlation of signals from the two ring-type conductance probes that are placed apart at a fixed distance. A feedforward neural network is adopted to correlate the characteristic parameters of slug flow and the gas and liquid flow rates and further used as a prediction tool. The experimental results show that the neural network method is able to learn the implicit correlations between the characteristic parameters of slug flow and the corresponding gas and liquid flow rates. It provides a performance for measurement of gas and liquid flow rates in slug flow regime within ±10% of full scale.Index Terms-Conductance probe, cross correlation, gas flow rate, liquid flow rate, liquid holdup, neural network, slug flow.

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Section: Neural Networkmentioning

confidence: 99%

Two-Phase Air–Water Slug Flow Measurement in Horizontal Pipe Using Conductance Probes and Neural Network

Fan

Yan

2014

IEEE Trans. Instrum. Meas.

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“…Examples are the linearisation of sensor outputs [4], the compensation of non-linearities induced by environmental quantities [4–7], the fusion of multiple sensors data into one or more improved sensor outputs [8,9], or the classification of pre-processed sensor data [10,11]. Besides functionality extension, the efficiency of intelligent sensors has also been addressed.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

Ferreira

Gomes

Martins

et al. 2012

Sensors

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Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.

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“…The theoretical correlations of each method (Eqs. (5) and (10)) should be used to calculate the water holdup. For the conductance sensor, the water holdup calculation results will higher than 100% when the flow condition is oil continuous flow (water holdup is less than 30% and flow patterns is not ST flow and ST&MI flow at low flow rate) and it is a wrong result.…”

Section: Cccs System Real-time Measurement Methodsmentioning

confidence: 99%

“…Accurate water holdup measurement is therefore affected by the complex flow patterns. Many methods have been developed to measure the liquid holdup, such as quick closing valves [1], radiation sensors [3], microwave sensors [4], wire-mesh sensors, ultrasonic sensors [5,6] and electrical sensors [7].…”

Section: Introductionmentioning

confidence: 99%

Design of a Conductance and Capacitance Combination Sensor for water holdup measurement in oil–water two-phase flow

Tan

Dong

2015

Flow Measurement and Instrumentation

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A Multisensor Intelligent Device for Real-Time Multiphase Flow Metering in Oil Fields

Cited by 48 publications

References 13 publications

Two-Phase Air–Water Slug Flow Measurement in Horizontal Pipe Using Conductance Probes and Neural Network

Two-Phase Air–Water Slug Flow Measurement in Horizontal Pipe Using Conductance Probes and Neural Network

A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

Design of a Conductance and Capacitance Combination Sensor for water holdup measurement in oil–water two-phase flow

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