WO2021167921A1 - Appareil de mesure coriolis et procédés pour la caractérisation de fluides polyphasiques - Google Patents

Appareil de mesure coriolis et procédés pour la caractérisation de fluides polyphasiques Download PDF

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Publication number
WO2021167921A1
WO2021167921A1 PCT/US2021/018283 US2021018283W WO2021167921A1 WO 2021167921 A1 WO2021167921 A1 WO 2021167921A1 US 2021018283 W US2021018283 W US 2021018283W WO 2021167921 A1 WO2021167921 A1 WO 2021167921A1
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WIPO (PCT)
Prior art keywords
measured
parameter
decoupling
coriolis
frequency
Prior art date
Application number
PCT/US2021/018283
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English (en)
Inventor
Daniel Gysling
Original Assignee
Corvera Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US16/946,497 external-priority patent/US11796366B2/en
Application filed by Corvera Llc filed Critical Corvera Llc
Priority to US17/800,039 priority Critical patent/US20230160734A1/en
Priority to EP21755679.4A priority patent/EP4107492A4/fr
Publication of WO2021167921A1 publication Critical patent/WO2021167921A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N9/00—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity
    • G01N9/002—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity using variation of the resonant frequency of an element vibrating in contact with the material submitted to analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/74—Devices for measuring flow of a fluid or flow of a fluent solid material in suspension in another fluid
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/76—Devices for measuring mass flow of a fluid or a fluent solid material
    • G01F1/78—Direct mass flowmeters
    • G01F1/80—Direct mass flowmeters operating by measuring pressure, force, momentum, or frequency of a fluid flow to which a rotational movement has been imparted
    • G01F1/84—Coriolis or gyroscopic mass flowmeters
    • G01F1/8409—Coriolis or gyroscopic mass flowmeters constructional details
    • G01F1/8431—Coriolis or gyroscopic mass flowmeters constructional details electronic circuits
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/76—Devices for measuring mass flow of a fluid or a fluent solid material
    • G01F1/78—Direct mass flowmeters
    • G01F1/80—Direct mass flowmeters operating by measuring pressure, force, momentum, or frequency of a fluid flow to which a rotational movement has been imparted
    • G01F1/84—Coriolis or gyroscopic mass flowmeters
    • G01F1/8409—Coriolis or gyroscopic mass flowmeters constructional details
    • G01F1/8436—Coriolis or gyroscopic mass flowmeters constructional details signal processing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/76—Devices for measuring mass flow of a fluid or a fluent solid material
    • G01F1/78—Direct mass flowmeters
    • G01F1/80—Direct mass flowmeters operating by measuring pressure, force, momentum, or frequency of a fluid flow to which a rotational movement has been imparted
    • G01F1/84—Coriolis or gyroscopic mass flowmeters
    • G01F1/845—Coriolis or gyroscopic mass flowmeters arrangements of measuring means, e.g., of measuring conduits
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F15/00—Details of, or accessories for, apparatus of groups G01F1/00 - G01F13/00 insofar as such details or appliances are not adapted to particular types of such apparatus
    • G01F15/02—Compensating or correcting for variations in pressure, density or temperature
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/05—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects
    • G01F1/34—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by measuring pressure or differential pressure
    • G01F1/36—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by measuring pressure or differential pressure the pressure or differential pressure being created by the use of flow constriction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/05—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects
    • G01F1/34—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by measuring pressure or differential pressure
    • G01F1/36—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by measuring pressure or differential pressure the pressure or differential pressure being created by the use of flow constriction
    • G01F1/40—Details of construction of the flow constriction devices
    • G01F1/44—Venturi tubes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N9/00—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity
    • G01N9/002—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity using variation of the resonant frequency of an element vibrating in contact with the material submitted to analysis
    • G01N2009/006—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity using variation of the resonant frequency of an element vibrating in contact with the material submitted to analysis vibrating tube, tuning fork

Definitions

  • [2] represents the second reference cited in the reference list, namely, “Gysling, D, “An aeroelastic model of Coriolis mass and density meters operating on aerated mixtures” Journal of Flow Measurement and Instrumentation, Volume 18, Issue 2, April 2007, Pages 69-77”.
  • Embodiments of the disclosure generally relate to apparatus and methods for determining flow characteristics using Coriolis flow meters in inhomogeneous and compressible process fluid flow regimes.
  • Coriolis meters are designed to provide a measurement of the mass flow and/or density of a fluid flow passing through a pipe. It is known that Coriolis meters provide erroneous mass flow and density measurements in the presence of entrained gases and or particles within the fluid flow (e.g., entrained gases within liquid are known as bubbly gas).
  • a system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions.
  • One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.
  • One general aspect includes a method of measuring a multiphase fluid.
  • the method also includes providing a flow measuring device that may include at least one fluid- conveying flow tube conveying the multiphase fluid, measuring a first measured parameter of the multiphase fluid, measuring at least one additional measured parameter of the multiphase fluid, providing a processing unit for analyzing the first measured parameter and the at least one additional measured parameter, determining at least one decoupling parameter using at least one of the first measured parameter and the at least one additional measured parameter, and quantifying, using the at least one decoupling parameter in a decoupling model, an effect of variable levels of decoupling on an interpretation of at least one of the first measured parameter and the at least one additional measured parameter in terms of at least one characteristic of the multiphase fluid.
  • Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
  • Implementations may include one or more of the following features.
  • the method may include where the determining at least one decoupling parameter may include: identifying, experimentally, the at least one decoupling parameter, producing a range of decoupling parameters, and specifying at least one specific decoupling parameter based on at the at least one of the first measured parameter and the at least one additional measured parameter in terms of the at least one characteristic of the multiphase fluid.
  • the method may include: determining a mass flow rate of the multiphase fluid based on the at least one decoupling parameter and the at least one characteristic of the multiphase fluid. The determining the at least one decoupling parameter is determined concurrently with the at least one characteristic of the multiphase fluid.
  • Measuring the first measured parameter and the at least one additional measured parameter are measured at a plurality of instances over which at least one characteristic of the multiphase fluid is varying and at least one characteristic of the multiphase fluid is constant or known and where the determining the at least one decoupling parameter may include using the plurality of instances of measuring the first measured parameter and the at least one additional measured parameter.
  • the first measured parameter is a measured Coriolis frequency and the at least one additional measured parameter is a measured multiphase fluid speed of sound.
  • the first measured parameter is indicative of a first measured Coriolis density at a first measured frequency and the at least one additional measured parameter is a second measured Coriolis density at a second frequency.
  • Determining the at least one decoupling parameter may include determining a first decoupling parameter at the first measured frequency and determining a second decoupling parameter using the first decoupling parameter, the first measured frequency and the second frequency.
  • the measuring of the first measured parameter and the at least one additional measured parameter are measured simultaneously over at least one period of time for which a characteristic of the multiphase fluid is essentially constant.
  • the first measured parameter is a measured Coriolis density and the at least one additional measured parameter is a measured speed of sound.
  • the first measured parameter is indicative of a first measured Coriolis density at a first measured frequency and the at least one additional measured parameter is a second measured Coriolis density at a second frequency.
  • Determining the at least one decoupling parameter may include determining a first decoupling parameter at the first measured frequency and determining a second decoupling parameter using the first decoupling parameter, the first measured frequency and the second frequency.
  • the first measured parameter is a measured Coriolis density and the at least one additional measured parameter may include a measured differential pressure, a measured Coriolis mass flow and a measured speed of sound.
  • the first measured parameter is a measured Coriolis density and the at least one additional measured parameter may include a measured differential pressure and a measured Coriolis mass flow.
  • the first measured parameter is a measured Coriolis density and the at least one additional measured parameter may include a measured differential pressure, a measured Coriolis mass flow and a speed of sound of the multiphase fluid.
  • the first measured parameter is indicative of a first measured Coriolis density at a first measured frequency and the at least one additional measured parameter may include a measured differential pressure, a measured Coriolis mass flow and a second measured Coriolis density at a second frequency.
  • Determining the at least one decoupling parameter may include determining a first decoupling parameter at the first measured frequency and determining a second decoupling parameter using the first decoupling parameter, the first measured frequency and the second frequency.
  • the at least one decoupling parameter may include a density decoupling parameter and where the density decoupling parameter is utilized to determine a mass flow decoupling parameter and the at least one characteristic of the multiphase fluid includes a mass flow of the multiphase fluid.
  • a flow measuring device may include: at least one flow tube configured to convey a multiphase fluid and capable of measuring a first measured a first measured parameter of the multiphase fluid, and the flow measuring device further configured to measure at least one additional measured parameter of the multiphase fluid.
  • the system also includes a processing unit configured to: receive the first measured parameter and the at least one additional measured parameter; determining at least one decoupling parameter using at least one of the first measured parameter and the at least one additional measured parameter; and quantify, by using the at least one decoupling parameter in a decoupling model, an effect of variable levels of decoupling on an interpretation of at least one of the first measured parameter and the at least one additional measured parameter in terms of at least one characteristic of the multiphase fluid.
  • Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
  • Implementations may include one or more of the following features.
  • the flowmeter system may include where the processing unit is configured to specify, based on at the at least one of the first measured parameter and the at least one additional measured parameter in terms of the at least one characteristic of the multiphase fluid, at least one specific decoupling parameter from a range of decoupling parameters, where the range of decoupling parameters is experimentally identified.
  • the flowmeter system is further configured to determine a mass flow rate of the multiphase fluid based on the at least one decoupling parameter and the at least one characteristic of the multiphase fluid.
  • the flowmeter system may include where the processing unit is configured to determine the at least one decoupling parameter concurrently with the at least one characteristic of the multiphase fluid.
  • the flowmeter system is further configured to measure the first measured parameter and the at least one additional measured parameter at a plurality of instances over which at least one characteristic of the multiphase fluid is varying and at least one of characteristic of the multiphase fluid are constant or known and to determine the at least one decoupling parameter using the plurality of instances of measuring the first measured parameter and the at least one additional measured parameter.
  • the flow measuring device may include a Coriolis meter and a sound speed meter and where the first measured parameter is a measured Coriolis frequency and the at least one additional measured parameter is a measured multiphase fluid speed of sound.
  • the flow measuring device may include a dual frequency Coriolis meter and where the first measured parameter is indicative of a first measured Coriolis density at a first frequency and the at least one additional measured parameter is a second measured Coriolis density at a second frequency.
  • the flowmeter system is further configured to determine the at least one decoupling parameter may include by determining a first decoupling parameter at the first frequency and by determining a second decoupling parameter using the first decoupling parameter, the first frequency and the second frequency.
  • the flowmeter system is further configured to measure the first measured parameter and the at least one additional measured parameter are measured simultaneously over at least one period of time for which a characteristic of the multiphase fluid is essentially constant.
  • the flow measuring device may include a Coriolis meter and a sound speed meter and where the first measured parameter is a measured Coriolis density and the at least one additional measured parameter is a measured speed of sound.
  • the flow measuring device may include a dual frequency Coriolis meter and where the first measured parameter is indicative of a first measured Coriolis density at a first frequency and the at least one additional measured parameter is a second measured Coriolis density at a second frequency.
  • the flowmeter system is further configured to determine the at least one decoupling parameter by determining a first decoupling parameter at the first frequency and by determining a second decoupling parameter using the first decoupling parameter, the first frequency and the second frequency.
  • the flow measuring device may include a Coriolis meter, a differential pressure meter and a sound speed meter and where the first measured parameter is a measured Coriolis density and the at least one additional measured parameter may include a measured differential pressure, a measured Coriolis mass flow and a measured speed of sound.
  • the flow measuring device may include a Coriolis meter and a differential pressure meter and where the first measured parameter is a measured Coriolis density and the at least one additional measured parameter may include a measured differential pressure and a measured Coriolis mass flow.
  • the flow measuring device may include a Coriolis meter, a differential pressure meter and a sound speed meter and where the first measured parameter is a measured Coriolis density at a first frequency and the at least one additional measured parameter may include a measured differential pressure, a measured Coriolis mass flow and a speed of sound of the multiphase fluid.
  • the flow measuring device may include a dual frequency Coriolis meter and a sound speed meter and where the first measured parameter is indicative of a first measured Coriolis density at a first frequency and the at least one additional measured parameter may include a measured differential pressure, a measured Coriolis mass flow and a second measured Coriolis density at a second frequency.
  • the flowmeter system is further configured to determine the at least one decoupling parameter may include by determining a first decoupling parameter at the first frequency and by determining a second decoupling parameter using the first decoupling parameter, the first frequency and the second frequency.
  • the at least one decoupling parameter may include a density decoupling parameter and where the density decoupling parameter is utilized to determine a mass flow decoupling parameter and the at least one characteristic of the multiphase fluid includes a mass flow of the multiphase fluid.
  • One general aspect includes a method of retrofitting a Coriolis meter.
  • the method also includes configuring the Coriolis meter to measure at least one additional measured parameter of the multiphase process fluid; and configuring the processing unit to receive the first measured parameter and the at least one additional measured parameter, to determine at least one decoupling parameter using at least one of the first measured parameter and the at least one additional measured parameter and to quantify, using the at least one decoupling parameter in a decoupling model, an effect of variable levels of decoupling on an interpretation of at least one of the first measured parameter and the at least one additional measured parameter in terms of at least one characteristic of the multiphase process fluid.
  • Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
  • Implementations may include one or more of the following features.
  • the method may include configuring the processing unit to specify, based on at the at least one of the first measured parameter and the at least one additional measured parameter in terms of the at least one characteristic of the multiphase process fluid, at least one specific decoupling parameter from a range of decoupling parameters, where the range of decoupling parameters is experimentally identified.
  • the method may include configuring the processing unit to determine a mass flow rate of the multiphase fluid based on the at least one decoupling parameter and the at least one characteristic of the multiphase fluid.
  • the method may include configuring the processing unit to determine the at least one decoupling parameter concurrently with the at least one characteristic of the multiphase process fluid.
  • the method may include configuring the processing unit to configured to measure the first measured parameter and the at least one additional measured parameter at a plurality of instances over which at least one characteristic of the multiphase fluid is varying and at least one of characteristic of the multiphase fluid are constant or known and to determine the at least one decoupling parameter using the plurality of instances of measuring the first measured parameter and the at least one additional measured parameter.
  • the first measured parameter is a measured Coriolis frequency and the at least one additional measured parameter is a measured multiphase process fluid speed of sound.
  • the Coriolis meter may include a dual frequency Coriolis meter and where the first measured parameter is indicative of a first measured Coriolis density at a first measured frequency and the at least one additional measured parameter is a second measured Coriolis density at a second frequency.
  • the method may include configuring the processing unit to determine the at least one decoupling parameter may include by determining a first decoupling parameter at the first measured frequency and by determining a second decoupling parameter using the first decoupling parameter, the first measured frequency and the second frequency.
  • the method may include configuring the processing unit to measure the first measured parameter and the at least one additional measured parameter simultaneously over at least one period of time for which a characteristic of the multiphase process fluid is essentially constant.
  • the first measured parameter is a measured Coriolis density and the at least one additional measured parameter is a measured speed of sound.
  • the Coriolis meter may include a dual frequency Coriolis meter where the first measured parameter is indicative of a first measured Coriolis density at a first measured frequency and where the at least one additional measured parameter is a second measured Coriolis density at a second measured frequency.
  • the method may include configuring the processing unit to determine the at least one decoupling parameter by determining a first decoupling parameter at the first measured frequency and by determining a second decoupling parameter using the first decoupling parameter, the first measured frequency and the second measured frequency.
  • the first measured parameter is a measured Coriolis density and the at least one additional measured parameter may include a measured differential pressure, a measured Coriolis mass flow and a measured speed of sound.
  • the first measured parameter is a measured Coriolis density and the at least one additional measured parameter may include a measured differential pressure and a measured Coriolis mass flow.
  • the first measured parameter is a measured Coriolis density at a first frequency and the at least one additional measured parameter may include a measured differential pressure, a measured Coriolis mass flow and a speed of sound of the multiphase process fluid.
  • the Coriolis meter may include a dual frequency Coriolis meter and where the first measured parameter is indicative of a first measured Coriolis density at a first measured frequency and the at least one additional measured parameter may include a measured differential pressure, a measured Coriolis mass flow and a second measured Coriolis density at a second frequency.
  • the method may include configuring the processing unit to determine the at least one decoupling parameter may include by determining a first decoupling parameter at the first measured frequency and by determining a second decoupling parameter using the first decoupling parameter, the first measured frequency and the second frequency.
  • the at least one decoupling parameter may include a density decoupling parameter and where the density decoupling parameter is utilized to determine a mass flow decoupling parameter and the at least one characteristic of the multiphase process fluid includes a mass flow of the multiphase process fluid.
  • Figure 1 is a graphical representation of flow characteristics for a fluid mixture from the prior art
  • Figure 2 is a schematic representation of an optimization process in accordance with the present disclosure
  • Figure 4 shows a schematic representation of an interpretation process to determine component flow rates of a three phase mixture in accordance with the present disclosure
  • Figure 5 is a graphical representation of an error function in accordance with the present disclosure.
  • Figure 6 is a schematic representation of an optimization process in accordance with the present disclosure.
  • FIG. 7 is an illustration of a gas liquid cylindrical cyclonic (GLCC) separator with a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 8 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 9 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 10 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 11 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 12 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 13 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 14 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 15 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 16 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 17 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 18 is a graphical representation of the decoupling parameter of a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 19 is a graphical representation of watercut data measured from a speed of sound augmented Coriolis meter versus reference watercut in accordance with the present disclosure
  • Figure 20 is a graphical representation of the decoupling amplitude ratio as function of Inverse Stokes Parameter for a Bubble in Liquid from the prior art
  • Figure 21 is a graphical representation of the decoupling parameter expressed as a simplified function of inverse Stokes number in accordance with the present disclosure
  • Figure 22 is a graphical representation of bubble size parameter versus decoupling parameter from a speed of sound augmented Coriolis meter versus reference watercut in accordance with the present disclosure
  • Figure 23 is a graphical representation of bubble size parameter versus reference watercut in accordance with the present disclosure.
  • Figure 24 is a schematic representation of an optimization process in accordance with the present disclosure.
  • Figure 25 is a graphical representation of optimized values for the mass flow, watercut, gas void fraction, and decoupling parameters from a speed of sound augmented Coriolis meter versus reference watercut in accordance with the present disclosure
  • Figure 26 is a graphical representation of an optimization function over a range of decoupling parameter versus watercut in accordance with the present disclosure
  • Figure 27 is a graphical representation of optimized values for the mass flow, watercut, gas void fraction, and decoupling parameters from a speed of sound augmented Coriolis meter versus reference watercut in accordance with the present disclosure
  • Figure 2823 is a graphical representation of an optimization function over a range of decoupling parameter versus watercut in accordance with the present disclosure
  • Figure 29 is a side view of a speed of sound augmented Coriolis meter enhanced with a venturi flow meter in accordance with the present disclosure
  • Figure 30 is a schematic representation of an optimization process for a speed of sound augmented Coriolis meter enhanced with a venturi flow meter in accordance with the present disclosure
  • Figure 31 is a graphical representation of an error function for a three-phase flow simulation of a speed of sound augmented Coriolis meter enhanced with a Venturi meter in accordance with the present disclosure
  • Figure 32 is a graphical representation of an error function for a three-phase flow simulation of a speed of sound augmented Coriolis meter enhanced with a Venturi meter in accordance with the present disclosure
  • Figure 33 is a graphical representation of an error function for a three-phase flow simulation of a speed of sound augmented Coriolis meter enhanced with a Venturi meter in accordance with the present disclosure
  • Figure 34 is a graphical representation of an error function for a three-phase flow simulation of a speed of sound augmented Coriolis meter enhanced with a Venturi meter in accordance with the present disclosure
  • Figure 35 is a graphical representation of an error function for a three-phase flow simulation of a speed of sound augmented Coriolis meter enhanced with a Venturi meter in accordance with the present disclosure
  • Figure 36 is a graphical representation of an error function for a three-phase flow simulation of a speed of sound augmented Coriolis meter enhanced with a Venturi meter in accordance with the present disclosure
  • Figure 37 is a graphical representation of the decoupling parameter of a speed of sound augmented Coriolis meter enhanced with a Venturi meter in accordance with the present disclosure
  • Figure 38 is a schematic representation of an optimization process in accordance with the present disclosure
  • Figure 39 is a graphical representation of an error function for an optimization to determine mass flow and watercut for a speed of sound augmented Coriolis meter enhanced with a momentum-based differential pressure measurement in accordance with the present disclosure
  • Figure 40 is a graphical representation of an error function for an optimization to determine mass flow and gas void fraction for a speed of sound augmented Coriolis meter enhanced with a momentum-based differential pressure measurement in accordance with the present disclosure
  • Figure 41 is a graphical representation of an error function for an optimization to determine watercut and decoupling parameters for a speed of sound augmented Coriolis meter enhanced with a momentum-based differential pressure measurement in accordance with the present disclosure
  • Figure 42 is a graphical representation of optimized values for the mass flow, watercut, gas void fraction, and decoupling parameters in accordance with the present disclosure
  • Figure 24 is a graphical representation of an optimization function over a range of decoupling parameter versus watercut in accordance with the present disclosure
  • Figure 44 is a graphical representation of a bubble size parameter for a dual frequency Coriolis meter in accordance with the present disclosure
  • Figure 45 is a schematic representation of an optimization process in accordance with the present disclosure.
  • Figure 46 is a graphical representation of optimized values for the mass flow, watercut, gas void fraction, and decoupling parameters in accordance with the present disclosure
  • Figure 47 is a graphical representation of an error plot for a simulation in accordance with the present disclosure
  • Figure 48 is a schematic representation of an in-situ decoupling method to simultaneously determine multiphase flow characteristic and decoupling characteristics of a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • Figure 49 is a schematic representation of an optimization process in accordance with the present disclosure.
  • Figure 50 is a graphical representation of an error plot for a simulation in accordance with the present disclosure.
  • Figure 51 is a graphical representation of an error function for a three-phase flow simulation of a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 52 is a graphical representation of an error function for a three-phase flow simulation of a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 53 is a graphical representation of an error function for a three-phase flow simulation of a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 54 is a schematic representation of an optimization process to determine a common watercut and common decoupling parameter and gas void fractions in accordance with the present disclosure
  • Figure 55 is a graphical representation of an error function for a three-phase flow simulation of a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 56 is a graphical representation of an error function for a three-phase flow simulation of a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 57 is a graphical representation of density versus gas void fraction for a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 58 is a graphical representation of density versus gas void fraction for a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 59 is a graphical representation of density versus gas void fraction for a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 60 is a graphical representation of density versus gas void fraction for a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 61 is a schematic representation of an optimization process to determine watercut and gas void fraction using a dual frequency Coriolis meter with a known decoupling parameter in accordance with the present disclosure
  • Figure 62 is a graphical representation of density versus gas void fraction for a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 63 is a graphical representation of density versus gas void fraction for a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 64 is a graphical representation of density versus gas void fraction for a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 65 is a graphical representation of density versus gas void fraction for a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 66 is a graphical representation of density versus gas void fraction for a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 67 is a schematic representation of an algorithm to optimize a decoupling parameter for use with a dual frequency Coriolis meter enhanced with a decoupling model in accordance with the present disclosure
  • Figure 68 is a schematic representation of an optimization process to determine optimized mass flow, watercut, gas void fraction, and a decoupling parameter in accordance with the present disclosure
  • Figure 69 is a graphical representation of a simulation of a method to determine the mass flow, watercut, gas void fraction, and decoupling parameter based on an model based optimization utilizing a measured Coriolis mass flow, a measured Coriolis density at a first frequency, and measured Coriolis density at a second frequency, and a differential pressure measurement;
  • Figure 70 is a graphical representation of an error plot for a simulation in accordance with the present disclosure.
  • Figure 71 is a side view of a Coriolis meter enhanced with a decoupling model in accordance with the present disclosure.
  • f tube is the vibrational frequency of the tube
  • D is the inner diameter of the tube
  • a mix is the sound speed of the process fluid.
  • the reduced frequency is a non-dimensional number that characterizes the impact of fluid compressibility Coriolis flow meters, and, as shown, is related to the inverse of the sound speed of the process fluid.
  • m is the dynamic viscosity of the liquid
  • p is the density of the liquid
  • w is the frequency
  • R bubbie is the radius of the bubbles.
  • the bubbles will tend to track with the liquid as the tube oscillates transversely and the effects of decoupling are minimized.
  • rh mix is the mass flow rate of the mixture. Since the mass flow rate of the gas of a bubbly mixture is typically negligible compared to the liquid, the mixture mass flow rate and the liquid mass flow rate are essentially the same.
  • the at least two decoupling parameters K d and K m are defined as real numbers in the range of ' ⁇ >K d >3 and ' ⁇ >K m >3 and depend at least on the inverse Stokes number. It should be appreciated by those skilled in the art that the decoupling ratio of the prior art is, in general, a complex number indicating a magnitude difference and phase lag between the motion of the fluid and the motion of the bubble.
  • the decoupling parameters K d and K m can, in some circumstances, be treated as being constant over a limited range of conditions, it is inventively disclosed that they can be determined on a real time or quasi-real time basis in some embodiments of this disclosure.
  • a decoupling parameter can be determined and a model is employed to capture the effects of decoupling which can be referred to herein as a decoupling model.
  • K d is the decoupling parameter, which can be specified or determined as part of an optimization.
  • K d is a parameter utilized in the decoupling model, it does not specify the decoupling effects and is but at least one decoupling parameter as will be disclosed herein after.
  • the product of gas void fraction, a, and K d specifies the decoupling effects.
  • Equation 3 and 4 models for the effects of decoupling and compressibility on Coriolis meter that quantify the effects of decoupling, and compressibility on the relationship between the measured density and the actual liquid density (Equation 3) and the relationship between the measured mass flow and the actual mass flow rate (Equation 4).
  • Wood’s equation provides a relationship between the mixture characteristics and the speed of sound. Other equations can be utilized to provide this relationship (Temkin [13]).
  • Wood’s Equation relates the measured mixture sound speed and density to the phase fractions, density and sound speeds of the components.
  • the elasticity E of the conduit also enters into Wood’s equation, for a thin-walled (i.e. the ratio of wall thickness to radius is small), circular cross section conduit of outer diameter D and wall thickness of t is given as follows: ⁇ Equation s).
  • the measured speed of sound for the mixture can be expressed as a function of the gas void fraction and the fluid properties and properties of the conduit as follows: (Equation 9).
  • the density measurement from the Coriolis meter (operating on a bubbly mixture and based on interpreting the natural frequency of the flow tube vibration in terms of a calibration developed for an essentially single phase, essentially incompressible fluid), is related to 1) the density of the liquid phase; 2) the gas void fraction; and 3) the reduced frequency, as a function of mixture sound speed, the decoupling parameter K d , and the compressibility parameter, G d is expressed as follows:
  • Equations 8, 9 and 10 can be solved for the three unknowns of p liq , p mix , and a. It should be appreciated by those skilled in the art that errors in estimating the sound speed of the liquid component in the mixture, provided it is a reasonable estimate, have little impact on the accuracy of the determination of liquid density and gas void fraction for gas void fractions on the order of approximately 1 % and above. As such, any inability to precisely determine the speed of sound of the liquid phase of a bubbly mixture is not viewed as limiting the applicability of embodiments of the present disclosure to a wide range of applications.
  • the density and speed of sound of the gas component is typically well-modelled knowing the gas composition and using an equation of state model for the gas properties.
  • a method to solve for the liquid density and the gas void fraction involves defining: 1) an error function based on comparing a measured density from a Coriolis meter to a simulated trial measured density; and 2) a measured sound speed to a simulated trial measured sound speed.
  • Trial values for characteristics of the multiphase flows are used as input to the models developed herein to calculate trial values for the measured parameters associated with the trial values for the flow characteristics. The difference between the actual measured parameters and the trial simulated measured parameters forms the basis of an error function.
  • the optimized liquid density and gas void fraction can then be determined by adjusting the trial liquid densities and trial gas void fractions over a range of allowable values such that the error function is minimized.
  • the error function is evaluated over a grid of trial values for the multiphase flow characteristics and the characteristics associated with the smallest error function are selected as the optimized answer.
  • Trial values for the liquid density and gas void fraction can span the range of allowable values for the individual characteristics. For example, for a mixture of oil, water, and gas, the allowable range of liquid densities could span range of densities between the oil component and the water component. If additional knowledge is available to reduce the range of allowable parameters, the allowable range of trial values could be constrained. For example, if, it is known that the gas void fraction is less than 10% by volume, this constraint could be placed on the allowable trial gas void fraction assessed in any optimization.
  • pmeas is a density measured by a Coriolis meter calibrated on a single phase fluid
  • a me as is the speed of sound of the process fluid and ai and oc2 are weighting constants for the optimization and (Equation 12).
  • Equation 11 Pmeas actuai is the density reported from the Coriolis meter that was calibrated on a single phase flow at negligible reduced frequency, and a meaSactiiai is the measured speed of sound of the process fluid within the flow tubes of the Coriolis meter.
  • the trial speed of sound of the mixture used in Equation 11 is characterized as follows: (Equation 13).
  • the process liquid can comprise a mixture of liquids such as a mixture of oil and water commonly found in the oil and gas industry.
  • the trial values for liquid density and the liquid sound speed were linked together as a function of watercut based on Wood’s Equation for an oil and water mixture over a range of watercuts using fixed values for the density and sound speeds of the oil and water components.
  • FIG. 2 there is shown a schematic diagram of an optimization process 20 to determine watercut and gas void fraction of a three phase fluid comprised of oil, water and entrained gas.
  • Optimization process 20 involves using the measured Coriolis frequency 21 of Coriolis flow tubes (and the associated measured density 22 interpreted therefrom based on calibration using an essentially incompressible, homogeneous fluid) and a measured process fluid speed of sound 23 with known Coriolis decoupling and compressibility parameters 24.
  • Optimization process 20 further uses a measured pressure and measured temperature of the process fluid as inputs into model 25 (which model can comprise Wood’s Equation and Coriolis density overreading models among other models relating to characterizing bubbly fluids) along with known fluid properties and parameters related specifically to the Coriolis meter.
  • model 25 outputs a trial measured density and a trial sound speed as input to the error function 26.
  • the method of optimization of the error function 26 to determine the gas void fraction and liquid density of a bubbly mixture being measured by a speed of sound augmented Coriolis meter can be simulated by using simulated “actual” measured parameters calculated utilizing said models developed herein with a specified decoupling parameter utilized to generate simulated measured sound speed and measured Coriolis density for use in the optimization process 20.
  • the error generated by error function 26 using said simulated “actual” measured parameters and said trial measurements can be seen with reference to Figure 3 where the error function 30 is shown as a function of trial gas void fractions and trial liquid densities.
  • the simulated “measured” parameters were determined using a consistent set of equations for given set point values of gas void fraction and liquid density.
  • the liquid density for the set point is 940 kg/m A 3 with a gas void fraction of 5%, for a measured speed of sound of 205 m/sec.
  • the error function 30 has a unique minimum at the set point values where the actual solution matches the optimized solution at point 31.
  • FIG. 4 there is shown a schematic diagram of an interpretation process 40 to determine component flow rates 41 of a three-phase (two liquid components and a gas component) mixture using a speed of sound augmented Coriolis meter.
  • interpretation process 40 minimizes the error function given in Equation 11.
  • the speed of sound augmented Coriolis meter utilizes the measured speed of sound 42, measured density 43, a known density decoupling parameter 44 and a known compressibility parameter to determine the liquid density and gas void fraction.
  • a measure of the liquid density provides a measure of the watercut of the liquid.
  • the measured sound speed 42, the determined gas void fraction, and the measured Coriolis mass flow 46 are used in a model in step 48 which accounts for the effects of decoupling and compressibility as expressed in Equation 10 where mass flow decoupling parameter 45 and compressibility parameter are known to determine mass flow of the mixture. Assuming that the mixture is well-mixed with negligible slip among the oil, water, and gas components, the flow rates of the three components (gas, oil and water) are then determined utilizing the mixture mass flow and the compositional information from interpretation process 40.
  • the optimization process finds a unique minimum; however, the optimized value 51 does not match the set point value 52.
  • the optimized value for the liquid density is 5% lower than the set point values (893 kg/m A 3 compared to 940 kg/m A 3) and the optimized gas void fraction was 5.3% compared the set point value of 5.0%.
  • optimization procedure 60 can be used to the determine mass flow, watercut, and gas void fraction utilizing the mass flow and density interpreted using a Coriolis meter calibrated on essentially incompressible and homogeneous fluid, a measured process fluid sound speed to determine the mass flow, watercut, and gas void fraction of a bubbly liquid utilizing a model that characterizes the effects of decoupling and compressibility with known decoupling and compressibility characteristics.
  • the methods and apparatus of the present disclosure include embodiments that determine estimates for decoupling parameters by measuring the reported mass flow and/or density of a process fluid with entrained gases at multiple instances over a period of time for which either the mass flow and/or density of the process fluid, is either sufficiently known, or is known to be sufficiently constant.
  • This method is particularly advantageous for situations in which the mass flow and/or density is either known, or constant, and the gas void fraction levels are unknown and variable.
  • the density decoupling parameter K d can be estimated using a least squares method to find the best fit for density decoupling parameter for a series of measurements for which, ideally, the gas void fraction is varying.
  • the discoveries disclosed herein make it possible to quantify a decoupling parameter as a real number ranging between 1 and 3 for a range of bubbly flow regimes.
  • the models for decoupling and compressibility can be used to define an equation for the density decoupling parameter, K d in terms of known or measured quantities in accordance with the following relationship: (Equation 15).
  • Equation 16 there needs only to be a first measured parameter and at least one additional measured parameter to solve for K d . It should be appreciated by those skilled in the art that it has been demonstrated, that in certain circumstances, the sound speed of the process fluid mixture can be accurately determined and, with knowledge of, or estimates of, the parameters of the components of the mixture, the gas void fraction can be determined therefrom. Thus, measuring 1) the mixture sound speed thereby enabling a determination of an estimate of the mixture gas void fraction, and 2) determining the ratio of the measured density to the actual liquid density over one or more operating conditions provides a basis from which to estimate the density decoupling parameter/ ⁇ . It should be noted that in certain circumstances determining the gas void fraction from a process fluid sound speed measurement requires knowledge of the process liquid density, which in this particular example is known. It should be further noted that the known liquid density could be varying with time.
  • the decoupling parameter can vary with varying process conditions such as fluid viscosity and bubble size parameters.
  • the decoupling parameter K d is constant for operating conditions sufficiently similar to the operating conditions for which the decoupling parameter was determined empirically.
  • the decoupling parameter remains sufficiently constant, thus providing a means to account for decoupling using an empirically determined decoupling parameter and the measured sound speed and interpreted gas void fraction.
  • the method described above for determining the density decoupling parameter K d could be used to periodically update the estimate of the decoupling parameter , and then utilize the determined decoupling parameter to interpret a measured density and measured speed of sound in terms of the watercut and gas void fraction of a three phase mixture.
  • density decoupling parameter, K d is a good approximation for the mass flow decoupling parameter, K m , and vice versa, for instance as disclosed in the following relationship:
  • a similar type of in-situ calibration can be performed for cases where a speed of sound augmented Coriolis meter is operating on a bubbly liquid of varying gas void fraction for which the liquid density is unknown, but sufficiently constant for periods of time over which the gas void fraction is varying.
  • the decoupling parameter is unknown, but also essentially constant.
  • an error function can be defined in accordance with the model to account for the effects of decoupling and compressibility to minimize the error over K d and p Uquid to concurrently determine an optimized estimate of the liquid density and the decoupling parameter.
  • the error function is characterized in accordance with the following:
  • Equation 19 can be expressed as a linear least squares optimization.
  • the measured Coriolis density is a sufficiently accurate estimate of the density of the liquid phase for the purposes of estimating the gas void fraction from the speed of sound measurement
  • the gas void fraction can be estimated at each measurement instance prior to determining an optimized liquid density and decoupling parameter.
  • the measured Coriolis density and the speed of sound measurement are the two measured parameters used to solve for K d .
  • the correction terms for the errors due to decoupling and compressibility expressed in terms of measured density, normalized by the liquid density, are small compared to unity
  • the liquid density at each instance in time can be expressed as: (Equation 20).
  • the set of N equations constitute a set of over-constrained linear equations.
  • the equations have a least-squares optimized solution for the “best” liquid density and the “best” decoupling parameter associated with the data from the “N” instances given by the standard form for linear least squares optimization: (Equation 27).
  • the value for the liquid density determined from a first optimization for the liquid density and decoupling parameter, can be used with the measured speed of sound for an improved estimate of gas void fraction at each instance of time and the least squares optimization can be repeated, until the value for the liquid density and decoupling parameter converge to a predefined specific tolerance. It should be noted by those skilled in the art that there are many methods to determine optimized values for the liquid density and the density decoupling parameter utilizing the methodology describe above, and the linear least squares method described is an example of one such method.
  • Equation 28 an optimization of Equation 28 can be expressed as a linear least-squares optimization.
  • the gas void fraction can be estimated utilizing the speed of sound measurement as an additional measured parameter at each measurement instance.
  • the correction terms for the error in measured mass flow over the liquid mass flow due to decoupling and compressibility are assumed to be small compared to unity, the liquid mass flow at each instance in time can be expressed as before utilizing a first order Taylor series: (Equation 29).
  • Equation 36 can be expressed as a set of linear equations for the mass flow rate and the mass flow decoupling parameter in matrix form as follows:
  • a least-squares optimized solution for liquid mass flow and the decoupling parameter for the N instances is given by:
  • Figure 6 is a schematic representation of an optimization process in accordance with the present disclosure.
  • FIG. 7 shows a schematic of a common apparatus 70 configured to measure the production rates of oil and gas wells.
  • Apparatus 70 is comprised of an inlet 71 configured to receive a process fluid from an oil and gas well head (not shown) consisting of a multiphase flow.
  • Multiphase inlet 71 leads to a compact, two phase (gas/liquid) separator 72 that is configured to separate the liquid and gas phases of the multiphase mixture wherein the gas exits the separator through the gas outlet 73 and the liquid exits the separator through the liquid outlet 74.
  • a speed of sound augmented Coriolis meter 75 configured to report mass flow rate and density is mounted in fluid communication with liquid outlet 74 of separator 72.
  • a speed of sound augmented Coriolis meter 75 further includes processor 76 which includes hardware and software capable of performing the functions disclosed herein to improve the accuracy of Coriolis-based flow measure applications on bubbly flow.
  • processor 76 includes hardware and software capable of performing the functions disclosed herein to improve the accuracy of Coriolis-based flow measure applications on bubbly flow.
  • the flows through liquid outlet 74 and gas outlet 73 recombine and exit through multiphase outlet 77. Note that although the design intent of the separator is to eliminate all the gas from liquid outlet
  • speed of sound augmented Coriolis meter 75 includes a methodology to improve the accuracy of the net oil measurement in this type of application.
  • the gas carry- under through the liquid outlet 74 of separator 72 can vary with production rates and other factors such as separator liquid level.
  • the time scales of the variations in gas carry-under are often much shorter than variations in produced water cut or other changes that could change the density of the produced liquids.
  • variations in gas carry-under can be unintentional due to naturally occurring process variations, or intentionally induced by varying certain separator control parameters, such as, for example, modifying the liquid level with the separator.
  • this particular example is well-suited for defining a two parameter optimization procedure as described above with reference to Equations 19- 27 in which the multiple data points at which the density reported by the Coriolis meter as a first measured parameter, and the measured speed of sound as an additional measured parameter (used to determine estimates of the gas void fraction within the Coriolis meter, and reduced frequency of the Coriolis measurement) are used to determine a best fit estimate of the liquid density and the density decoupling parameter.
  • This estimate of the density decoupling parameter can be used to approximate the mass flow decoupling parameter, which then can be used in conjunction with the gas void fraction and measured process fluid sound speed to improve the measured mass flow, in a process such as described in Figure 4 or Figure 6. Then, the net oil and net water rates can be determined using one or both of the improved liquid density and / or improved mass flow measurements. It should be appreciated by those skilled in the art that the estimate of the density decoupling parameter at one watercut is likely a good approximation for the density decoupling parameter over a range of watercuts, the larger the range of watercut ratios for which the density decoupling parameter is considered sufficiently accurate influences how often the system requires recalibration.
  • [00146] includes the raw density 81 and “raw” mass flow rate 82 from speed of sound augmented Coriolis meter 75, along with process fluid pressure, temperature, and sound speed, and Coriolis drive gain for ⁇ 1000 sec duration of this data set.
  • “Raw” describes the density and mass flow rate reported by speed of sound augmented Coriolis meter 75, as referred to as measured density and measured mass flow within this disclosure, that was based on the interpretation of a measured Coriolis frequency and measured phase lag, respectively, as interpreted by a calibration intended for a homogeneous, single phase flow.
  • the constant reference liquid density 83 and mass flow rate 84 are also indicated.
  • the mass flow rate and composition of the high gas volume fraction multiphase flow into the separator 72 was held constant, and the liquid level in the separator was varied to induce changes in the amount of gas carry-under that was exiting liquid outlet 74.
  • the raw density 81 and raw mass flow rate 82 reported by the Coriolis meter varied throughout the test point, with drive gain 85 increasing, and saturating at 100%, for a period during the test point associated with the lowest measured process-fluid speed-of-sound 86.
  • FIG. 9 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure
  • FIG. 10 there is shown a measured process-fluid speed-of-sound 91 and interpreted gas void fraction 92.
  • Interpreted gas void fraction 92 was derived utilizing the raw density 81 as an approximation of the actual liquid density used in Wood’s Equation.
  • Also shown in the figure is the reduced frequency 93 of speed of sound augmented Coriolis meter 75.
  • variations in the gas void fraction 92 and reduced frequency 93 track inversely with variations in process-fluid speed-of-sound 91.
  • the gas void fraction 92 (GVF) was in the range of 0.2% ⁇ GVF ⁇ 0.8%
  • the reduced frequency 93 was in the range of 0.015>fred ⁇ 0.025.
  • FIG. 10 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure; 1, there is shown raw density 101 points shown as “Reported Density” plotted as a function of gas void fraction in the figure, and the interpreted density of the liquid phase of a bubble mixture 102, also plotted as a function of gas void fraction.
  • the Flemp model determines a constant liquid density of 979 kg/m 3 , well-matched the reference liquid density of 980 kg/m 3 .
  • Figure 11 is a graphical representation of data measured from a speed of sound augmented Coriolis meter in accordance with the present disclosure; 2 shows the gas carry-under, in terms of gas void fraction 111 within the liquid outlet 74, the reference watercut 112, the raw watercut 113, and the corrected watercut 114 for this particular example as a function of time.
  • the corrected watercut 114 was determined using the liquid density, interpreted using the model to correct for Coriolis decoupling and compressibility with the optimized decoupling parameter, and the relationship between liquid density and watercut of an oil/water mixture (Equation 42).
  • the raw watercut 113 defined as the watercut determined using the raw density 81 measurement and Equation 36, is significantly lower than the reference watercut 112, with the error in raw watercut increasing with increasing gas void fraction 111.
  • the corrected watercut 114 interpreted utilizing the model to correct for Coriolis decoupling and compressibility is shown to be significantly more accurate compared to the reference and insensitive to variations in gas carry-under.
  • the gas carry-under is relatively low for this particular example, in the range of 0.2 % ⁇ GVF ⁇ 0.8%.
  • the gas carry-under is responsible for the majority of the error in raw watercut 113, most of which is eliminated utilizing speed of sound augmented Coriolis meter 75 of the present disclosure and the methods described herein.
  • the raw data recorded for another example test point involves a process fluid with a nominal liquid flow rate of 3005 BPD at a nominal 50% watercut and is shown with reference to Figure 12.
  • the raw density 121 and raw mass flow rate 122 from speed of sound augmented Coriolis meter 75 of the present disclosure were measured, along with process fluid pressure 123, temperature 124, and sound speed 125, along with Coriolis drive gain 126 for approximately 1500 secs.
  • the “raw” density 121 and mass flow rate 121 reported by speed of sound augmented Coriolis meter 75 vary significantly over the test points due to varying levels of gas carry-under despite the liquid mass flow rate and watercut being held constant. Note that the Coriolis drive gain 126 is saturated at 100% for the entire data point.
  • gas void fraction 131 the reduced frequency 132 and the measured sound speed 133 of the process fluid for this particular example, comprised of 3005 BPD of liquid, 50% watercut.
  • gas carry-under gas void fraction 131
  • the gas carry-under was in the range of 0.5% ⁇ GVF ⁇ 2.5%, comprising approximate three times more gas carry-under than that measured for the previously present example herein above at a lower liquid rate and higher watercut.
  • the model to correct for Coriolis decoupling and compressibility model predicts a liquid density 142 of 888 kg/m 3 , matching the reference liquid density of 888 kg/m 3 .
  • the raw watercut 151 and corrected watercut 152 measured for this particular example, wherein the process fluid comprises 3005 BPD of liquid at 50% watercut, as a function of time using the a model for the effects of decoupling and compressibility on a Coriolis meter with the optimized decoupling parameter.
  • the measured process fluid density corrected utilizing a model for the effect of decoupling and compressibility on a Coriolis meter with a best-fit decoupling parameter, reports a density-based corrected watercut 152 value that matches the reference watercut 153, independent of variations in gas carry-under (gas void fraction 154) of 0.5% to 2.5%.
  • each test point for which the liquid phase watercut was held constant while the gas carry-under varied provides a data set with which to determine a decoupling parameter, K d.
  • Error! Reference source not found.18 shows a number of test points were run and with reference to Figure 18 the value for the decoupling parameters 161 for 37 test points in which the variations in gas carry-under were sufficient to determine a density decoupling parameter are plotted versus watercut.
  • the 37 decoupling parameters 161 exhibit a clear trend with watercut.
  • the polynomial fit 162 shown in Error! Reference source not found.8, as part of the current disclosure, can serve as a calibration for the decoupling parameter as a function of watercut for speed of sound augmented Coriolis meter 75 in such operating regimes. Note that the shape of this curve will, in general, be a function of many parameters including fluid and Coriolis meter properties and is likely to be specific to the examples disclosed.
  • the polynomial fit 162 was applied to the each of the 37 data points to determine the density decoupling parameter K d as a function of watercut.
  • the resulting measured watercut 171 (labeled “WC raw” in the figure) and corrected watercut 172 (labeled “WC KD1 Fit” in the figure), averaged over time for each of the 37 test points, is plotted versus the reference watercut 173 (labeled “UNITY” in the figure) Figure 199.
  • the speed-of-sound augmented Coriolis meter 75 is effective in correcting the watercut errors due to variable levels of gas carry-under over a wide range of watercuts and liquid flow rates.
  • FIG. 20 shows the decoupling amplitude ratio as function of Inverse Stokes Parameter for a bubble in liquid from Weinstein [5]
  • the decoupling amplitude ratio is defined as the amplitude of a particle or bubble entrained in a fluid in a vibrating tube divided by the amplitude of the vibrating tube, and as disclosure herein, this parameter can be assumed to be functionally equivalent to the decoupling parameters, K d and K m , defined herein.
  • Figure 21 shows a parameterized version of this these results, where the decoupling parameter 181, K d defined herein is assumed to be functionally equivalent to the decoupling ratio of Weinstein [5] as a function of inverse Stokes number.
  • the term functional equivalent is used here to indicate, that while the physics of an oscillation of an individual bubble in a large oscillating fluid is similar to those of a distribution of bubble within a Coriolis meter, the simplified model does not represent the generally much more complex physics of an oscillatory flow within a Coriolis meter. For example, while bubble size is well-defined for the isolated bubble, bubble size is replaced by a bubble size parameter that represents a representative bubble size for a mixture with a distribution of bubbles. Also, as developed in Weinstein [5], the relationship between the fluid motion and a particle exhibits both an amplitude ratio and a phase difference, indicating that the even physics of the simplified model is not fully captured using the decoupling parameter defined within.
  • Equation 2 The inverse Stokes number of Equation 2 disclosed herein above can be expressed in terms cyclic frequency (instead of angular frequency) as
  • determining the density decoupling parameter provides a means for determining a parameter R bub that is representative of the size of the entrained bubbles. Note that the size of the bubbles within a bubbly fluid with typically spans a range of actual sizes, and the bubble size parameter is a parameter indicative of a representative size of the bubbles.
  • Bubble size can be an important parameter for a process fluid, for example, as a quality metric for products with entrained gases, such as ice cream, or other foamed products, as indicted by Zhu [12] Additionally, it can be useful in diagnosing the performance of process equipment such as gas / liquid separators.
  • Figure 22 shows a plot 191 of bubble size parameter R bub versus decoupling parameter K d for a bubbly liquid within liquid properties representative of water for speed of sound augmented Coriolis meter 75 with a vibration frequency of 78 Hz.
  • the estimated bubble size parameter as a function of watercut.
  • the bubble size parameter R bub was determined utilizing the identified decoupling parameter K d , the Coriolis vibrational frequency / , the liquid density and liquid viscosity v f at each point as set forth in the following equation:
  • decoupling parameter of a Coriolis measurement involve including the decoupling parameter as an optimization parameter along with the physical parameters of the process fluid, such as mass flow, liquid phase density, and gas void fraction in a non-linear parameter optimization process.
  • the previous examples concurrently solved for a characteristic of the multiphase flow (i.e. the liquid density) and a decoupling parameter (i.e. Kd), utilizing a linear least squares optimization process.
  • One method to evaluate the effectiveness of the optimization process 201 illustrated in Figure 22 is a graphical representation of bubble size parameter versus decoupling parameter from a speed of sound augmented Coriolis meter versus reference watercut in accordance with the present disclosure
  • Figure 23 is a graphical representation of bubble size parameter versus reference watercut in accordance with the present disclosure.
  • Figure 4 is to perform simulations in which the mass flow, watercut, gas volume fraction and decoupling parameter are optimized using optimization process 201 based on an error function utilizing: 1) a measured density 202 as a first measured parameter; and 2) a measured speed of sound 203 as an additional measured parameter as inputs along with the additional measured parameters characterizing the Coriolis meter and components of the process fluid.
  • the “measured” parameters are calculated utilizing the models developed herein, and then the error function is minimized through iterations over a range of allowable of trial multiphase flow characteristics to determine optimal multiphase flow characteristics 204.
  • Figure 25 is a graphical representation of optimized multiphase flow characteristics (i.e. the mass flow, watercut, gas void fraction, and decoupling parameters) obtained optimizing an error function based on differences in 1) measured vs trial Coriolis density and 2) measured vs trial sound speed for 100 simulations in which the simulated measured parameters, calculated to be consistent with input values for the mass flow, watercut, density, and decoupling parameters (shown with dashed lines in Figure 25) with +/- 1% maximum amplitude random noise added to each of the said simulated measured parameters. The simulations were performed for a set point with 30% watercut with 5% gas void fraction at 3.0 kg/sec mass flow.
  • optimized multiphase flow characteristics i.e. the mass flow, watercut, gas void fraction, and decoupling parameters
  • the Coriolis flow tube 352 ( Figure 71) has a diameter of 2 inches and a nominal vibrational frequency of 80 Flz.
  • the density decoupling parameter K d and mass flow decoupling parameter K m were each 1.8. These parameters are summarized in Table 1 below:
  • FIG. 25 there is shown the results from 100 simulations labeled as “Measurement Point” along the X-axis for multiphase flow characteristics that are determined based on an optimization process similar to optimization process 201 described herein with noise added to the simulated data.
  • the optimization utilized equal weighting of: 1) error contributions from the simulated measured Coriolis density with noise versus a trial Coriolis density; and 2) error contributions from the simulated measured sound speed with noise versus a trial sound speed. It should be appreciated by those skilled in the art that, although only errors associated with the measured density and measured speed of sound are used in the error function, the optimization process determines an optimized watercut 211, an optimized gas void fraction 212, and an optimized decoupling parameter 213.
  • An optimized mass flow 214 is determined utilizing the measured mass flow, and optimized gas void fraction 212, the measured reduced frequency, and optimized decoupling parameter 213. As shown, the optimization yields reasonable estimates for mass flow, water cut, gas void fraction and decoupling parameter in the presence of random noise on each of the measured variables of +/- 1%. There is a relatively large standard deviation of the optimized watercut and density decoupling parameter associated with the random noise input. The mean values for of the optimized parameters in the presence of random noise closely match the input parameters.
  • FIG. 26 there is shown a contour plot 221 of a cross section of the error function associated with the optimization process of Figure 25 generated utilizing the simulated measure parameters without any noise added to the measured parameters over a range of trial density decoupling parameter and trial watercuts.
  • the contours indicate region of low error exists is around the optimized value.
  • the optimized watercut and density decoupling parameter for the 100 points, each with random noise added to the measured values bounded to +/- 1% of the measured value, are overlayed on the plot as well.
  • the simulation disclosed immediately herein above demonstrates the heretofore unknown enablement of speed of sound augmented Coriolis meter 75 and the methods of the present disclosure to determine multiphase flow parameters and a decoupling parameter based on an optimization involving only a measured Coriolis density and a measured sound speed.
  • the points with the random noise on the simulated measured values fall within the low error region indicated by the contours of the contour plot 221.
  • this low error region 222 spans a reasonably large range of watercut and decoupling parameters, resulting in significant noise in the optimized flow parameters associated with the random noise input to the simulated measured parameters.
  • Figure 27 shows the results of an optimization that is similar to that described with reference to Figure 25, however in the results shown in Figure 27 the optimization function included an error contribution from the difference in a measured versus trial mass flow.
  • Figure 27 shows values for the optimized mass flow, 231 watercut 232, gas void fraction 233, and decoupling parameter 234 based on optimizing an error function based on differences in: 1) measured Coriolis mass flow versus trial Coriolis mass flow; 2) measured Coriolis density versus trial Coriolis density; and 3) measured sound speed versus trial sound speed for 100 simulations in which the simulated measured values are based on constant input values for the mass flow, watercut, density, and decoupling parameter (shown with dashed lines on graph) with+/-1% bounded random noise added to each of the said simulated measured values.
  • FIG. 28 there is shown a cross section plot 241 of an error function constructed without no noise applied to the measurement parameters over a range of decoupling parameters, K d versus watercut with the results of the optimization for the 100 optimized points 242 based on 1) the simulated measured mass flow; 2) the simulated measured Coriolis density; and 3) the simulated measured speed of sound.
  • the optimized points 242 are shown plotted in relation to the actual value of K d of 1.8. It should be appreciated, and as shown in Error! Reference source not found.6 and Error!
  • FIG. 29 there is shown an embodiment of a speed of sound augmented Coriolis meter enhanced with a momentum -based differential pressure flow meter 258.
  • Flow meter 250 is similar to speed of sound augmented Coriolis meter 75 ( Figure 7) which comprises a bent tube Coriolis meter with an array of strain-based pressure sensors 251 attached to the flow tube 252 positioned within housing 253.
  • the array of strain-based pressure sensors in this embodiment is configured to measure the speed of sound of the process fluid, but other methods could be utilized to determine the speed of sound of the process fluid.
  • Flow meter 250 further includes inlet flange 254 configured to be in fluid communication with a flow 255 of process fluid and an outlet flange 256 as well as a transmitter 257.
  • Flow meter 250 further includes a Venturi flow meter 258 positioned with its throat 259 near the outlet flange 256. It should be appreciated by those skilled in the art that Venturi flow meter 258 functions just as a Venturi flow meter of the prior art in that flow 255 exits flow tube 252 and enters the Venturi flow meter.
  • a first pressure sensor is positioned at the inlet end of Venturi flow meter 258 proximate flow tube 252 and a second pressure sensor is positioned in the necked down throat area of the Venturi flow meter.
  • Transmitter 257 includes a processing unit which includes a processor (similar to processor 76 disclosed herein above with reference to Figure 7) and it performs the standard Coriolis measurement requirements of the prior art such as to drive the flow tube 252 and measure and interpret the vibrational characteristic of the flow tube, as well as measure and interpret the output of the array of strain-based pressure sensors 251 in terms of process-fluid speed of sound, and to measure and interpret the differential pressure across the Venturi.
  • Transmitter 257 includes hardware and software capable of performing the algorithms described herein to provide accurate and robust measurement of measurement of single and multiphase flows as will be described in more detail herein below.
  • Embodiments also include prior art Coriolis meters that are retrofitted to include the methods disclosed herein to include a sound speed meter to provide a measured multiphase process fluid speed of sound measurement to correct the prior art meters for the decoupling effects of an inhomogeneous multiphase process fluid including those have bubbly flow and particles.
  • the method and apparatus of flow meter 250 approach utilizes four primary measured parameters: 1) the mass flow reported by a Coriolis meter calibrated for homogeneous fluids with low compressibility; 2) the density reported by a Coriolis meter calibrated for homogeneous fluids with low compressibility; 3) process fluid sound speed representative of the sub-bubble-resonant speed of sound from the array of strain-based pressure sensors 251 in for which the wavelength of the sound is long compared the characteristic length of fluid inhomogeneities; and 4) a differential pressure measurement across a flow area change such as Venturi flow meter 258.
  • Flow meter 250 uses an approach shown schematically in Figure 30, including optimization process 260, which utilizes process fluid component information in the form of measured parameters 261, such as process fluid sound speed and densities of the components, and other information combined with a reduced order simulation model 262 to simulate the described process fluid measurements based on trial values of characteristics of a three phase flow.
  • the trial measurements parameters are process fluid mass flow, watercut of the liquid, and gas void fraction. The three components of the flow, oil, water and gas, are assumed to be well-mixed, with each phase, or components, flowing at nominally the same flow velocity.
  • the simulation model 262 simulates process fluid measurements associated with each trial set of mass flow, watercut, and gas void fraction. These simulated measurements for each trial flow condition are compared to the actual process measurement measured parameters 261 within error function 263 which comprises a positive-definite error function.
  • error function 263 contains a set of weighting functions 268 that weight errors contributions associated with each of the four measured parameters 261 and simulated parameters 264.
  • the value of the error function is evaluated at step 265 to determine if it is minimized within a tolerance, and if the error is not determined to be minimized, the trial values are updated at step 266 and the process is repeated until the error is minimized.
  • the values of mass flow, watercut, and gas void fraction that result in the minimum error function are reported as the mass flow, watercut, and gas void fraction at step 267.
  • the values of the weighting function 268 can be updated based on available information. For example, for periods in which the drive gain 269 of the Coriolis is low, indicating limited multiphase conditions, the weighting of the Coriolis mass flow can be increased, and the weighting on the Venturi differential pressure can be decreased. Conversely, when the drive gain 269 is elevated, more relative weighting can be placed on the Venturi. [00180] In single phase flows, and multiphase flows with limited gas void fraction and limited inhomogeneities within the fluid, a Coriolis density and mass flow measurements are typically highly accurate and stable.
  • the Coriolis meter can be utilized to periodically calibrate the Venturi based flow measurement, accounting for well-known issues such a drift in the differential pressure measurement to improve the overall accuracy and reliability of the Venturi augmented, speed of sound augmented, Coriolis meter.
  • the process involves using trial values to specify a three phase flow, in this case, assuming the flow is a well-mixed flow of oil, water, and gas, along with information on the fluid properties and the geometry of the Coriolis and Venturi meter, and the decoupling and compressibility parameters in the Coriolis model, to simulate the four measured parameters: Coriolis reported mass flow, Coriolis reported density, differential pressure across a Venturi, and the process fluid sound speed.
  • the simulation model 262 utilizes a model for the effects of decoupling and compressibility on Coriolis mass flow and density, Wood’s Equation to related process fluid sound speed to process parameters, and a model of the flow through the Venturi, to simulate the four measured parameters based on trial input values for mass flow, watercut, and gas void fraction. Given the trial watercut and gas void fraction, along with the fluid properties, Wood’s Equation can be used to simulate the measured process fluid sound speed.
  • the measured parameters are compared with the parameters simulated for a set of trial flow characteristics.
  • error function 263 utilized here in is a weighted sum of the square of a normalized difference between measured and simulated parameters and is given as follows:
  • FIG. 31 there is shown contour plots of the error function for a simulated three phase measurement with mass flow versus watercut in plot 271 and water cut versus gas volume fraction in plot 272 in Figure 32.
  • the error function 263 was optimized by utilizing the models described above to calculate “measured” process parameters including Coriolis reported mass flow, Coriolis reported density, differential pressure across Venturi flow meter 258, and process fluid speed of sound from a SONAR array of strain based pressure sensors 251 for an input flow condition of 3.0 kg/sec mass flow, 70% watercut, with 3% gas void fraction.
  • the density of the oil, water, and gas phases is 930 kg/m A 3, 1000 kg/m A 3, and 11.1 kg/m A 3, respectively.
  • the area ratio from the inlet to the throat of the Venturi was 3.0, and the inlet of the Venturi had a diameter of 3.0 inches.
  • the weights in weighting function 268 for this simulation were as follows: 1 for the Coriolis mass flow, 100 for the Coriolis density, 1 for the differential pressure across the Venturi, and 1 for the speed of sound measurement.
  • the contours show a smooth surface, with the optimized result closely matching the input flow conditions, with the difference between the input and optimized values are related to the noise imposed on the simulated “actual” measured values at points 273 and 274.
  • the flow measurement system of a speed of sound augmented Coriolis meter enhanced with a Venturi flow meter has some degree of redundancy.
  • Figure 33 shows a similar simulation to that shown in Figure 31, however for the simulation of Figure 33, the Venturi weighting function was set to zero.
  • the error function is smooth and exhibits a single minimum value 281, 282.
  • the speed sound augmented Coriolis meter is capable of accurately determining, albeit less accurately than when enhanced with a Venturi flow meter, the three-phase flow without input from the Venturi meter.
  • the Venturi enhanced meter 250 uses the optimization process 260, provides redundant information when the speed of sound augmented Coriolis meter is operating accurately, such as a point that would be indicated by low drive gain values.
  • the measurement from the speed- of-sound augmented Coriolis meter, optimized with the weighting function 268 on the venturi error set to zero, could be used to calibrate the Venturi meter during normal operation.
  • the weighting of the Coriolis mass flow measurement can be reduced within weighting function 268, or set to zero, and the system would continue to accurately measure three phase flow 255. Additionally, the weight on the Coriolis mass flow measurement could be reduced within weighting function 268 if there were excessive uncertainty in the mass flow decoupling and/or compressibility coefficients.
  • Venturi model in this disclosure is but an example and represents the broad class of differential pressure flow measurements devices and includes orifice plates, and any other controlled area change devices for which the differential pressure can be measures and related the momentum of the process fluid without departing from the scope of the present disclosure.
  • Venturi meters are often utilized for flow measurement due to their high differential pressure from the inlet to the throat, but low overall total pressure loss with most of the pressure drop through the throat being recovered in the diffusing section downstream of the throat resulting in a relatively low overall pressure loss.
  • the acceleration of a flow through the throat of the Venturi, or other differential pressure-based flow measurement device can serve as a source for acoustic noise with the piping system and thereby serves to improve the ability of any passive-listening-based speed of sound measurement by the array of strain-based pressure sensors 251 , particularly in low acoustic noise environments.
  • the differential pressure device is located upstream of the flow tube 252, the differential pressure meter can serve to increase the homogeneity of a multiphase flow, reducing bubble size and thereby reducing the effects of decoupling.
  • Another advantage of augmenting a speed of sound Coriolis meter with a momentum-based differential pressure flow measurement is that the differential pressure measurement enables an improved determination of the decoupling parameters K d and/or K m of the Coriolis flow meter.
  • the optimization process 260 developed above assumed that the decoupling parameter was known. While this indeed may be the case for some applications, and, notwithstanding other embodiments of this disclosure described elsewhere in this disclosure, providing a sufficiently accurate, yet practical means for determining the mass flow decoupling characteristics and the density decoupling characteristics can still present a practical challenge in applying Coriolis meters to multiphase flows.
  • the measured differential pressure from a Venturi meter can be expressed as a function mass flow and density measured by a Coriolis meter, the gas volume fraction, the reduced frequency, and the decoupling parameter for mass flow, K m , and density, K d , and the compressibility parameter for mass flow and density, G m and G d
  • Equation 44 a calculated differential pressure based on measured mass flow and density from the Coriolis meter, corrected for decoupling and compressibility effects, and the geometry of the differential pressure device, can be expressed as a function of the decoupling parameters.
  • Figure 37 shows the trial simulated differential pressure minus the actual simulated pressure across a Venturi flow meter normalized by the actual simulated pressure as a function of a trial decoupling parameter where the density decoupling parameter K d and the mass flow decoupling parameter K m are assumed to be equal and the compressibility parameters G d and G m are assumed to be known.
  • a comparison of the predicted and actual differential pressure, divided by the actual pressure difference, across a Venturi flow meter can provide an effective means to determine the decoupling parameters, K d and K m , of a speed of sound augmented Coriolis meter enhanced with differential flow meter device.
  • Figure 38 shows a schematic representation of an optimization procedure 310 to determine the mass flow, the watercut, and the gas void fraction, and decoupling parameter 311 of a bubbly, three phase mixture utilizing: 1) the mass flow reported by a Coriolis meter calibrated for a single-phase flow; 2) the density reported by a Coriolis meter calibrated for a single-phase flow; 3) a process fluid speed-of-sound measurement reported by a SONAR based flowmeter; and 4) a momentum-based differential flow measurement reported by a Venturi flowmeter as inputs 312. Optimization procedure 310 also uses the pressure and temperature and frequency of the Coriolis meter as inputs 313.
  • the optimization procedure 310 is similar to the optimization procedure for which the decoupling parameters are assumed to be known, however in this approach, the decoupling parameters are unknown, but the mass flow and density decoupling parameters are assumed to be equal, or have a known relationship, and are solved-for in an optimization procedure that optimizes mass flow, water cut, gas void fraction, and the decoupling parameter.
  • Figures 39-41 show three views of an error function for an optimization to determine mass flow, watercut, gas void fraction, and decoupling parameters for a speed of sound augmented Coriolis meter enhanced with a momentum-based differential pressure measurement with noise added to the data showing unique minimum values 321, 322, 323 close to the actual values 324, 325, 326 respectively.
  • a speed of sound augmented Coriolis meter enhanced with differential flow meter device can uniquely determine the decoupling parameter for the Coriolis meter within optimization process, eliminating any need to pre-specify a specific decoupling parameter. It should be appreciated by those skilled in the art that identifying the decoupling parameter based on measurement from a single instance of time not only reduces the amount of calibration information required to accurately characterize bubbly fluids.
  • Bubble size measurement can be an important process control parameter that can indicate, among other things, product quality and/or operational characteristics of process equipment such as separators.
  • Figure 43 is a graphical representation showing a cross section of a “zero noise” error function of optimization procedure 310 ( Figure 38) over a range of decoupling parameters K d versus watercut with the results of the optimization for the 100 points shown in Figure 42 optimized based on: 1) the simulated measured mass flow; 2) the simulated measured Coriolis density; 3) the simulated measured speed of sound; and 4) the simulated versus measured Venturi meter as inputs.
  • adding a second vibratory frequency can be achieved without an increase of the pressure drop across the Coriolis meter, such as that incurred with a differential pressure (DP) device, and without adding any additional hardware to a Coriolis flow tube, such as strain-based pressure sensors to measure a speed of sound.
  • DP differential pressure
  • the two modes of vibration are associated with the same flow tubes, or pair of flow tubes, each vibrational mode involves the same process fluid.
  • the gas void fraction, process fluid sound speed, and liquid density are the same for each measured vibrational mode.
  • the decoupling parameter is assumed to be a function of the inverse Stokes parameter.
  • the decoupling characteristics of bubbly fluid within a vibrating tube are a function of several parameters, include the vibrational frequency.
  • the vibrational frequency include the vibrational frequency.
  • the fluid properties and bubble size parameters are the same for each vibrational frequency, and the ratio of the inverse Stokes numbers at the two vibrational frequencies can be related as follows.
  • Figure 44 shows an example of liquid density interpreted at two frequencies for a simulation representative of a dual frequency Coriolis meter operating in an environment having a process fluid comprising a bubbly liquid with relatively high decoupling with parameters.
  • first mode vibrational frequency 200Flz
  • second mode vibration frequency 1200Flz
  • liquid density 940 kg/m A 3
  • gas void fraction 0.05
  • mixture density 894 kg/m A 3
  • speed of sound of mixture 205 m/sec
  • liquid viscosity 0.001 Pa-sec
  • a bubble size parameter of R bubbie 1mm
  • K di 2.696
  • K dz 2.87, with a measured density of 813 kg/m A 3 for mode 1 , and a measured density of 810 kg/m A 3 for mode 2.
  • the bubble size parameter is varied over a range of bubble size parameters and the interpreted liquid density, based on the constant simulated “measured” densities at each of the two frequencies of the dual frequency Coriolis meter, is shown as a function of trial bubble size parameter.
  • the interpreted liquid density associated with each of the two frequencies matches at a single value 351 of the bubble size parameter, and single value of the liquid density.
  • the set point liquid density and bubble size parameter used to generate the simulated measurement is indicated on the plot at a single value 351 , coinciding with the intersection of the two trial estimates of the liquid density based on the interpretation of the two measured densities from the dual frequency Coriolis meter.
  • FIG. 45 shows a schematic representation of an optimization procedure 360 to determine the mass flow, the watercut, and the gas void fraction 611 of a bubbly, three phase mixture utilizing: 1) the mass flow reported by a dual frequency Coriolis meter calibrated for a single-phase flow; 2) the density reported by a first frequency of a dual frequency Coriolis meter calibrated for a single-phase flow; 3) the density reported by a second frequency of a dual frequency Coriolis meter calibrated for a single-phase flow; and 4) a process fluid speed-of-sound measurement; as inputs 362.
  • Optimization procedure 360 also uses the pressure and temperature of the process fluid and both frequencies of the dual frequency Coriolis meter as inputs 363.
  • the density decoupling parameters associated with the two frequencies are assumed to be related as described in Equations 43-45.
  • the optimization procedure 360 is similar to the optimization procedure for which the mass flow and density decoupling parameters are assumed to be known, however in this approach, the mass flow decoupling parameter and the density decoupling parameters at a first frequency are assumed to unknown, but equal, or have a known relationship, and are solved-for in an optimization procedure that optimizes mass flow, water cut, gas void fraction, and the decoupling parameter.
  • Kd in Figure 45 refers to the density decoupling parameter associated with the first vibratory mode of the dual frequency Coriolis meter.
  • FIG. 46 it shows the results of a simulation to assess a relative effectiveness of this approach.
  • simulated measured values were used for: 1) the measured density at a first frequency; 2) the measured density at a second frequency; 3) the measured speed of sound of the process fluid; and 4) the measured mass flow.
  • the simulated measured values were calculated utilizing a model for the effects of decoupling and compressibility on Coriolis meters and Wood’s Equation. Values for these simulated measured parameters were then compared to trial values calculated using said models to define an error function. The error function was then minimized over a range of process fluid mass flow, water cut, gas void fraction, and decoupling parameter using the methods that have been disclosed in more detail herein before.
  • the dual frequency speed of sound augmented Coriolis meter is capable of determining the mass flow, watercut, gas void fraction, and decoupling parameter of a multiphase flow based on the optimization algorithm described.
  • Figure 47 shows the same simulated results at Figure 46, with the optimized values for a first frequency decoupling parameter and watercut plotted on error contours generated using values for the measured parameters without noise for an optimization based on 1) a Coriolis density measurements at a first frequencies, 2) a Coriolis density measurement at a second frequency, 3) a process fluid sound speed, and 4) a Coriolis mass flow measurement at a first frequency for 100 cases.
  • a 1% max amplitude random noise was applied to each of the four measured parameters. It should be appreciated by those skilled in the art that the weighting terms in the error functions can be adjusted improve the results of the optimization to reflect, for example, comparatively higher weighting on comparisons of measured and modelled parameters with higher confidence.
  • the mass flow measured from a dual frequency, speed of sound augmented Coriolis meter at each of the two frequencies could also be used within an error function optimization to provide a means for determining flow parameters.
  • the optimized solution with noise fall within the low error regions of the first frequency decoupling parameter versus watercut error contours, showing that the approach is sensitive to noise, and indicating that, in some situations, a system that is less sensitive to noise may be desirable.
  • Figure 48 shows a schematic diagram of a robust and novel real time method 390 to simultaneously determine multiphase flow characteristic and decoupling characteristics of a speed of sound augmented Coriolis meter utilizing analytical models for the effect of decoupling and compressibility on augmented Coriolis meter being informed by a range of measurements 391.
  • the measured parameters include one of more of the following: 1) a Coriolis mass flow at a first frequency; 2) a Coriolis mass flow at a second frequency; 3) a Coriolis density at the first frequency; 4) a Coriolis density at the second frequency; 5) a process fluid sound speed; 6) a differential pressure from a momentum based differential pressure measurement; and 7) a process fluid velocity from a cross correlation meter.
  • Real time method 390 starts with inputting trial values for mass flow, watercut, gas void fraction and density decoupling parameter (at a first frequency) 391 into appropriate analytical models 392 along with the temperature and pressure of the process fluid (if available) and known fluid properties to calculate a set of trial measured variables 393. Depending on which of the range of measured parameters 395 that is available, some of the measurements would be calibrated at step 394 to output the calibrated actual measured parameters at step 395. Drive gain(s) from the Coriolis meter can be used to determine weighting function 396. Other weighting factors can be applied by a user as described herein above.
  • the actual measured parameters and the trial measured parameters are utilized as input to the weighted error function 397 and an algorithm is executed to minimize the to determine the multiphase flow characteristic and decoupling characteristics of a Coriolis meter. If the error function is below a predetermine value the values for mass flow, watercut, gas void fraction and density decoupling parameter are output to the user at step 398. If the error is not sufficiently minimized, the trial values are updated at step 399 and the method is repeated.
  • the models disclosed herein above can be utilized to interpret the parameters measured by a prior art dual frequency Coriolis meter (i.e. density reported at the two frequencies as interpreted by a calibration developed for essentially homogeneous and incompressible single phase flows) in terms of the physical characteristics (i.e. the mass flow, the density, the gas void fraction, and, bubble size through its relationship with a decoupling parameter) of a bubbly process fluid being conveyed through the flow tubes of the dual frequency Coriolis meter without the addition of other measurements such as a sound speed meter.
  • the first measure parameter is a measured Coriolis density reported at a first frequency and the additional parameter is a measured Coriolis density reported at a second frequency.
  • An embodiment of the disclosure utilizes the models disclosed herein to calculate trial measured parameters (i.e. a trial measured density at a first frequency, and a trial measured density at a second frequency) for use in an optimization process that minimizes the difference between trial and measured values.
  • Trial measured densities at a first frequency and at a second frequency can be calculated based of a trial liquid density, a trial decoupling parameter, a trial measured frequency, and a trial measured sound speed as follows:
  • An error function at instance in time , “i”, can be defined as the sum of the squares of the difference in trial measured density and the actual measured density, normalized by the actual measured density, at each frequency as follows: / r - , uation 5 r . (Eq 4 > )
  • the process involves calculating trial measured parameters (i.e. density at the two frequencies p me asi triaV Pmeas2 trial ) as a function of trial physical characteristics of the bubbly fluid (i.e. watercut, gas void fraction and, if unknown, a decoupling parameter).
  • the decoupling parameters can be viewed as a proxy for the inverse Stokes number, a non-dimensional parameter calculated based on physical characteristics of the bubbly mixture as will be described in more detail herein after.
  • the process further involves comparing these trial measured parameters to the two actual measured parameters using, for example, the error function of Equation 54 herein above.
  • the trial physical characteristics are adjusted to minimize the error function over a range of allowable physical characteristics to determine optimized values for each of the physical characteristics.
  • the density decoupling parameters K dltrial , K d2trial can be defined as a function an inverse Stokes number, in which the frequency for a given mode of vibration is known, and the kinematic viscosity is either known or estimated. In most applications, the bubble size parameter is unknown.
  • the relationship between the decoupling parameter, the fluid kinematic viscosity, the frequency of oscillation, and the bubble size parameter for the two frequencies can be expressed as follows:
  • the decoupling parameter can be viewed as a proxy for the inverse Stokes number, a non-dimensional parameter calculated based on physical characteristics of a bubbly mixture in a vibrating tube, thus the decoupling parameter can be viewed as a proxy for a physical characteristic of a bubbly mixture within a vibrating tube.
  • FIG. 49 there is shown a schematic of an optimization procedure 401 to determine watercut, gas void fraction, and the decoupling parameters as output 402 of a bubbly process fluid mixture utilizing, among other things, the relationships disclosed herein above with relation to Equations 52-57.
  • the optimization characteristics of watercut, gas void fraction, and a decoupling parameter associated with one of two vibrational frequencies, are iteratively updated until the error function 403 is minimized.
  • the optimization procedure 401 utilizes the density and sound speeds of the oil, water, and gas components, as input to various models at step 404, disclosed herein above such as Wood’s Equation, a model for the effects of decoupling and compressibility on the density measurement of a Coriolis meter, gas equation of state and a model linking the decoupling parameter at one frequency to the decoupling parameter at another frequency for the same fluid associated with the vibration of two modes of the same fluid-conveying flow tube.
  • Optimization procedure 401 utilizes the measured parameters of the vibrational frequencies at two modes, and the temperature and pressure of the process fluid 405.
  • FIG. 50 and 50 there are shown examples of error contours for a dual frequency Coriolis meter that is enhanced with the optimization procedure 401 of Figure 49.
  • the parameters of the simulation that were used with optimization procedure 401 are given in Table 3 with the gas void fraction set to 1 %. No noise was applied to the optimization and the results of the optimization of the error function over watercut, gas void fraction, and first frequency decoupling parameter matched the input values for the watercut, gas void fraction, and decoupling parameter.
  • Figure 50 shows the contours of the error function as a function of watercut and gas void fraction constructed utilizing the correct, optimized value for the first frequency decoupling parameter. As shown, utilizing that correct value for the decoupling, a dual frequency Coriolis meter can uniquely determine the watercut and gas void fraction of a mixture.
  • Figure 51 shows the error function for the correct gas void fraction of 1 % as a function a watercut and decoupling parameter. As shown, the optimization determines the correct values, but the error contours reveal a trough of almost equiprobable solutions. This trough of almost equiprobable solutions indicates that this method would be susceptible to noise and/or modeling errors,
  • Figure 52 shows error contours for a similar simulation for which the input gas void fraction was 5%. As shown, there is similar trough like structure, indicating that this approach would be susceptible to noise at this gas void fraction as well.
  • Figure 53 shows contours of the error function for a simulation of a dual frequency Coriolis meter for the same conditions as those used for Figure 52 solving for flow parameters and K d , with solutions for 100 simulations with 1% maximum amplitude random noise added to the simulated measured densities overlayed on the contour plot As shown, the optimized solutions exhibit significant error, with most of the optimized solutions for K d and watercut within the trough region of the error function. [00227] It would be desirable to reduce the range of the almost-equiprobable solutions to determine a more accurate representation of the decoupling parameter and other characteristics of a multiphase flow.
  • One embodiment of the present disclosure improves the ability to characterize the decoupling parameter.
  • Such an embodiment utilizes a dual frequency Coriolis meter operating on a fluid with essentially constant liquid density, but varying gas void fraction, to determine a characteristic of the fluid and to characterize the decoupling parameter.
  • the liquid density, decoupling parameter, and gas void fractions for each instance in time can be determined by minimizing the following expression:
  • the values for the density and sound speed of the oil, water, and gas components that comprise the bubbly mixture are assumed to be known.
  • Wood’s equation is utilized with the various models 432 within optimization procedure 431, the compressibility decoupling parameters are assumed to be known, and the density decoupling parameters at the two frequencies of a dual frequency Coriolis meter are related as disclosed herein above.
  • the optimization parameter of watercut, gas void fraction, and a decoupling parameter at the two frequencies for each instance N over time for both frequencies are iteratively updated until the error function 433 is minimized.
  • the optimization procedure 431 utilizes the density and sound speeds of the oil, water, and gas components, as input to various models 432, disclosed herein above such as Wood’s Equation, a model for the effects of decoupling and compressibility of Coriolis meters, gas equation of state and a model linking the decoupling parameter at one frequency to the decoupling parameter at another frequency for the same fluid associated with the vibration of two modes of the same fluid-conveying flow tube to produce trial measured densities 438 for each of the N instances.
  • Optimization procedure 431 utilizes the measured parameters 435 of the dual frequencies, and the temperature and pressure of the process fluid.
  • Known calibrations 436 are applied to the measured frequencies at each instant assuming a single-phase fluid to determine the actual measured densities 437 at the two frequencies and these are used as input into error function 433.
  • Such functions as those disclosed with respect to Figure 54 and Equations 52-57 can be performed using software and hardware residing in a processor or processors of a dual frequency Coriolis meter.
  • Embodiments also include prior art dual frequency Coriolis meters that are retrofitted to include the methods disclosed herein to correct the prior art meters for the decoupling effects of inhomogeneous process fluids including those have bubbly flow and particles.
  • Figures 55, 56 are visualizations of an error function generated for 7 variables (watercut, decoupling parameter, and gas void fractions of 5 operating points associated with 5 sets of measured parameters) displayed as a contour plot over two dimensions.
  • Figures 55 and 56 are the result of the optimization of Equation 55 that was formed by identifying the lowest values of the 7 dimensional global error function to identify an optimized gas void fraction for each of the 5 instances for which the two densities were measured, and then utilizing the identified best gas void fraction for each set point to construct an error function as a function of watercut and decoupling parameter that represents the sum of the contributions of each measured point at an optimized.
  • the composition error function for the case with varying gas void fractions has contours 440 which are indicative of a unique optimization in the decoupling and watercut space
  • the error function for which the gas void fraction was constant has an error function more indicative of trough 441 of almost-equiprobable solutions in the watercut and decoupling optimization space.
  • Optimizations with trough-like regions of closely equiprobable solutions are likely sensitive to errors and noise in measurements and in model uncertainty, indicating that the topology of the error function for the 5 points with varying gas void fraction is preferable to the topology of the 5 points with a constant gas void fraction.
  • FIG. 57 there is shown a graphical representation of density results versus interpreted gas void fraction for a 5-point simulation with constant liquid density and varying gas void fraction without measurement noise.
  • the density was measured at a first frequency 451 and a second frequency 452 of a dual frequency Coriolis meter, and the interpreted liquid density 453 as a function of the interpreted gas void fraction along the x-axis.
  • the results shown in the figures were generated without noise added to the measured densities.
  • Figure 58 shows the results of the simulation from Figure 57, interpreted in terms of interpreted watercut 461 with the raw watercut 462 determined using the raw density measured from the first frequency 451, and the interpreted watercut utilizing the optimization procedure 431 ( Figure 54). As shown, the effects of primarily decoupling can have a significant impact on the interpreted watercut of a bubbly liquid.
  • Figure 59 shows the results of a 5 point optimization procedure with 0.5% random noise added to the density measurements
  • Figure 60 shows the results of a 5 point optimization procedure with 1.0% random noise added to the density measurements.
  • the simulation provides an accurate interpretation based on the two density measurements at each point, providing a measure of the watercut, the gas void fraction, and the decoupling parameter.
  • the optimization procedure 431 ( Figure 54) for determining the decoupling parameter K dl does not need to be updated at the same update rate of the Coriolis meter.
  • the optimization procedure 431 to determine the decoupling parameter can be executed periodically or based on observed characteristics of the process, such as process variability. If the decoupling parameter has been identified and assumed applicable over a range of process conditions, a similar, faster optimization can be performed to determine the watercut and gas void fraction utilizing the assumed known decoupling parameter. As indicated in Figure 50, the error function over trial ranges of watercut and gas void fraction with a known decoupling parameter exhibits a well- defined, unique minimum utilizing measured densities from a single instance in time, enabling an efficient two-parameter optimization.
  • optimization procedure 480 to determine watercut and gas void fraction using a dual frequency Coriolis meter with known decoupling parameter.
  • the decoupling parameter K d can be determined using the apparatus and methods disclosed herein.
  • the previously determined decoupling parameter 481 can used as input to the optimization procedure 480.
  • various models 482 within optimization procedure 480 can include Wood’s Equation, a model for the effects of decoupling and compressibility of Coriolis meters, gas equation of state and a model linking the decoupling parameter at one frequency to the decoupling parameter at another frequency for the same fluid associated with the vibration of two modes of the same fluid-conveying flow tube to produce trial measured densities 484 for each of the N instances.
  • the optimization parameter of watercut and gas void fraction for each instance N over time for both frequencies are iteratively updated until the error function 483 is minimized.
  • the optimization procedure 480 utilizes the density and sound speeds of a process fluid that can be comprised of oil, water, and gas components, as input to various models 482, disclosed herein above such as Wood’s Equation, a model for the effects of decoupling and compressibility of Coriolis meters, gas equation of state and a model linking the decoupling parameter at one frequency to the decoupling parameter at another frequency for the same fluid associated with the vibration of two modes of the same fluid-conveying flow tube to produce trial measured densities 438 for each of the N instances.
  • Optimization procedure 480 utilizes the measured parameters 485 of the dual frequencies, and the temperature and pressure of the process fluid.
  • Known calibrations 486 are applied to the measured frequencies at each instant assuming a single phase fluid to output the measured densities 487 at the two frequencies and are used as input into error function 483.
  • Such functions as those disclosed with respect to Figure 61 and the various equations disclosed above can be performed using software and hardware residing in a processor or processors of a dual frequency Coriolis meter.
  • Embodiments also include prior art dual frequency Coriolis meters that are retrofitted to include the methods disclosed herein to correct the prior art meters for the decoupling effects of inhomogeneous process fluids including those have bubbly flow and particles.
  • Figure 63 shows examples of the optimization procedure 480 results with K dl fixed to determine the gas void fraction and water cut based on the two density measurements for 100 cases with 0.5% max amplitude random noise added to the measured densities 487.
  • Figure 64 shows examples of the optimization procedure 480 results with K dl fixed to determine the gas void fraction and water cut based on the two density measurements for 100 cases with 1% max amplitude random noise added to the measured densities 487.
  • the optimization procedure 480 with known K dl can provide a reasonable determination of gas void fraction and watercut in the presence of significant noise.
  • the dual frequency optimization procedure 480 is capable of providing an accurate measurement of the liquid density in presence of a significant error in an assumed decoupling parameter and in the presence of significant noise applied to the measured parameters.
  • the decoupling parameter determined through the optimization procedure 480 of the density measurements from a dual frequency Coriolis meter can provide a good estimate of the mass flow decoupling parameter.
  • the mass flow of the bubbly process fluid mixture can utilize the density decoupling parameter and the identified process fluid speed of sound and gas void fraction from the optimization procedure 480 to provide process fluid mass flow rate using equation 48 disclosed herein above. Assuming the bubbly flow is well-mixed, the process mass flow, density, and gas void fraction enable a determination of a process fluid flow, for example, the oil, water, and gas mass and volume flow rates.
  • Figure 67 shows a schematic of an algorithm 520 to measure 3-phase flows with a dual frequency Coriolis meter augmented with such an algorithm.
  • the three phase flow may, for example, be comprised of oil, water and gas and the output includes the oil, water, and gas mass flow rates and volumetric flow rates 523.
  • Algorithm 520 utilizes a watercut and gas void fraction optimization procedure with a specified K dl , such as described in Figure 61.
  • the decoupling parameter, K dl could be determined utilizing an optimization procedure, such as disclosed with reference to optimization procedure of Figure 54, based on data from a bubbly fluid operating at multiple gas void fractions.
  • the constant K dl 521 in optimization procedure 522 is used to determine watercut and gas void fraction, and a process 524, which process can include a model for the effects of decoupling and compressibility on a Coriolis meter to interpret the mixture mass flow based on the measured mass flow 528 and, at least in part, on the relationships of Equation 42 which utilizes the identified density decoupling parameter 521 as the mass flow decoupling parameter, along the optimized speed of sound and gas void fraction 525 to determine mixture mass flow and mass flow rates of the oil, water, and gas components to output the volumetric flow rates 523.
  • the liquid density measurement at a first frequency 526 and a second frequency 527 using standard processing are used as the two measured parameters in the optimization procedure 522.
  • the phase shift between the tubes of the dual frequency Coriolis meter using standard processing together with the second frequency 527 are used as input to process 524.
  • Dual Frequency Coriolis meter with Momentum-Based Differential Pressure Meter [00245] Referring next Figure 68, there is shown an optimization process 680 for an embodiment of the current disclosure that utilizes measurements from a dual frequency Coriolis meter augmented with a momentum-based, differential pressure flow meter to inform a model-based optimization process the determine the decoupling parameters and characteristics of a multiphase flow.
  • the momentum- based flow meter could be installed in close proximity to the Coriolis flow (such as Venturi flow meter 258 in Figure 29) such that the process fluid pressure and temperature conditions at the momentum-based flow meter are essentially the same as those within the Coriolis flow tubes.
  • the momentum-based flow meter can be installed at any point with a process fluid piping network in communication with the Coriolis flow tubes and differences in the pressure and temperature of the process fluid can be corrected utilizing analytical models the effect of pressure and temperature on the process fluid.
  • This particular embodiment advantageously includes several aspects for characterizing multiphase flows including a method that is relatively insensitive to errors in measured parameters, an ability to leverage the flow tubes of an existing single frequency Coriolis meter, and the with a retrofitted transmitter having a processing unit, the flexibility to install a commonly available venturi, or similar, momentum based flow meter in line with the existing Coriolis flow tubes to provide an additional measured parameter.
  • Optimization process 680 which utilizes process fluid properties, Coriolis and Venturi flow meter geometry and calibration information and a compressibility factor 681, as part of the input to model 682 to simulate the described process fluid measurements based on trial values of characteristics of a three phase flow.
  • the trial characteristics of the multiphase flow are process fluid mass flow, watercut of the liquid, decoupling parameter and gas void fraction are input as trial set 683.
  • the three components of the flow, oil, water and gas, are assumed to be well-mixed, with each phase, or components, flowing at nominally the same flow velocity.
  • the model 682 which includes Wood’s Equation, a decoupling compressibility Coriolis model, a decoupling model and a Venturi model (and additional models in some cases) simulates process fluid measurements 684 associated with each trial set 683. These simulated measurements 684 for each trial flow condition are compared to the actual process measurement parameters 685 within a positive-definite error function 686.
  • the four actual process measurement parameters 685 include a mass flow measurement, a density measurement at a first frequency and a density measurement at a second frequency using a dual frequency Coriolis meter and a differential pressure measurement using a Venturi meter.
  • Optimization process 680 includes a set of weighting functions 687 that weight errors associated with each of the simulated measurements 684.
  • the value of the error function is evaluated at step 688 to determine if it is minimized within a tolerance, and if the error is not determined to be minimized, the trial values are updated at step 689 and the process is repeated until the error is minimized.
  • the values of mass flow, watercut, and gas void fraction that result in the minimum error function are reported as the mass flow, watercut, density decoupling parameter K d and gas void fraction at step 690.
  • the values of the weighting function 687 can be updated based on available information. For example, for periods in which the drive gain of the Coriolis is low, indicating limited multiphase conditions, the weighting of the Coriolis mass flow can be increased, and the weighting on the Venturi differential pressure can be decreased. Conversely, when the drive gain is elevated, more relative weighting can be placed on the Venturi.
  • the measurement system of this particular embodiment provides an accurate measure of the multiphase flow characteristics and decoupling parameter in the presence of 1% max amplitude random noise applied to the measured parameters of mass flow measurement, density measurement at a first frequency, density measurement at a second frequency and differential pressure at each of the 100 instances for which the optimization procedure was performed.
  • Figure 70 shows contours of an error plotted as a function of the decoupling parameter K d versus watercut generated using measured values without noise with results from a 100 point simulation with 1% maximum amplitude noise added the measured values. As shown, the cluster of points from the 100 point simulation with random noise are well aligned with the contours around the input, actual conditions.
  • flow meter system 530 comprises a bent tube Coriolis meter with an array of strain-based pressure sensors 531 attached to the flow tube 532 positioned within housing 533
  • various embodiments of the present disclosure can omit such sensors without deviated from the scope of the present disclosure.
  • the array of strain-based pressure sensors in this embodiment is to achieve a measured multiphase fluid speed of sound of the process fluid, but other methods could be utilized to determine the speed of sound of the process fluid.
  • Flow meter system 530 further includes inlet flange 534 configured to be in fluid communication with a flow 535 of process fluid and an outlet flange 536 as well as a transmitter 537.
  • Flow meter system 530 further includes outlet flange 536 where process fluid exits the flow meter.
  • Transmitter 537 includes a processing unit having a processor (similar to processor 76 disclosed herein above with reference to Figure 7) and it performs the standard Coriolis measurement requirements of the prior art such as to drive the flow tube 532 and measure and interpret the vibrational characteristic of the flow tube, as well as measure and interpret the output of the array of strain-based pressure sensors 531 in terms of process-fluid speed of sound.
  • flow meter system 530 can comprise a dual frequency Coriolis flowmeter capable of driving flow tube 532 at two different frequencies or it can include a second flow tube (not shown) adapted to be driven at a second frequency different from flow tube 532.
  • Transmitter 537 includes memory, communications capability, hardware and software capable of performing the algorithms, optimization procedures, error functions and the like described herein to provide accurate and robust measurement of measurement of single and multiphase flows as disclosed herein above.
  • the memory of transmitter 537 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) and/or cache memory.
  • the memory can include a storage system that can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”).
  • a magnetic disk drive for reading from and writing to a removable, non volatile magnetic disk (e.g., a “floppy disk”).
  • an optical disk drive for reading from or writing to a removable, nonvolatile optical disk such as a CD-ROM, DVD-ROM or other optical media.
  • each can be connected to a bus (not shown) by one or more data media interfaces.
  • Transmitter 537 can include programs and utilities, having a set (at least one) of program modules, which may be stored in memory by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of n optimization procedure disclosed herein.
  • Program modules generally carry out the functions and/or methodologies of embodiments to store and analyze data.
  • an artificial intelligence (Al) platform can be included in transmitter 537 and can be in communication with the processing unit.
  • the Al platform may be local to memory.
  • the Al platform provides support for identification, detection and analysis of one or more parameters and/or characteristics of a process fluid flowing through flow meter system 530 as will be disclosed in more detail herein after.
  • the Al platform includes tools which can be, but are not limited to, an optimization procedure, an error function optimizer and an optimization algorithm. Each of these tools functions separately or combined in the Al platform to dynamically evaluate one or more process fluid parameters and/or characteristics.
  • the Al platform of flow meter system 530 of Figure 71 can use a trained neural network to produce an optimized decoupling parameter for a process fluid.
  • the inputs e.g., phase difference between sensor signals, frequency of flowtube oscillation, temperature, pressure, drive gain, speed of sound, compressibility constant decoupling parameter etc.
  • the inputs are selected to provide a smooth, continuous input in relation to the outputs. Note that more than one neural network can be used depending on the measurements to be corrected.
  • the network is trained using the raw measurements listed above from a plurality of states of liquids and matching reference data as inputs.
  • the various equations disclosed herein above are used calculate the inputs and to calculate targets based on the reference input.
  • the neural network is trained by calculating the corrections factors between the raw input and the reference input. The results are then used to generate corrected fluid characteristics for the plurality of states.
  • the trained neural network model is either hosted on, or otherwise accessed by, the Al platform of transmitter 537.
  • the trained neural network uses the raw measurements of flow meter system 530 to calculate the processed inputs and apply the corrections form the trained neural network to produce the corrected or enhanced output.

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Abstract

L'invention concerne un dispositif de mesure de débit permettant de mesurer au moins des paramètres d'un écoulement polyphasique et de quantifier un effet de découplage sur une interprétation des paramètres sur la base d'au moins une caractéristique du fluide polyphasique. Le système de mesure de débit comprend diverses augmentations et améliorations apportées à un débitmètre de Coriolis. Le système de mesure de débit permet de déterminer des paramètres de découplage qui peuvent être utilisés pour améliorer la sortie d'un débitmètre de Coriolis. L'invention concerne en outre un procédé d'amélioration d'un débitmètre de Coriolis.
PCT/US2021/018283 2020-02-17 2021-02-17 Appareil de mesure coriolis et procédés pour la caractérisation de fluides polyphasiques WO2021167921A1 (fr)

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EP21755679.4A EP4107492A4 (fr) 2020-02-17 2021-02-17 Appareil de mesure coriolis et procédés pour la caractérisation de fluides polyphasiques

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US16/946,497 2020-06-24
US16/946,497 US11796366B2 (en) 2019-06-24 2020-06-24 Coriolis meter
US202062706986P 2020-09-22 2020-09-22
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