Multiphase Flowmeter Using Microwave and Venturi Sensors
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Solution Overview
Problem
Existing multiphase flowmeters face challenges in accurately measuring flow parameters of multiphase fluids, such as oil, water, and gas, especially in hydrocarbon production pipelines, due to complexity and the need for gamma ray sensors, which increase costs and regulatory compliance issues.
Innovation Solution
A machine-learning multiphase flowmeter system that uses microwave transmitters and receivers, combined with a Venturi meter, to determine flow parameters by selecting adaptive signal frequencies and employing neural networks for accurate measurement of water liquid ratio, gas volume fraction, and other parameters without gamma ray sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gamma ray sensors are used in multiphase flowmeters, then measurement capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and removes the gamma ray sensor component from the multiphase flowmeter system. Instead of using gamma ray sensors, the invention employs microwave transmitters and receivers combined with a Venturi meter to measure flow parameters. This extraction eliminates the complexity and regulatory compliance issues associated with gamma ray sensors while maintaining measurement capability through alternative microwave-based detection methods.
2Measurement precision
If gamma ray sensors are used in multiphase flowmeters, then measurement capability is improved, but manufacturing cost increases
Solution Approach 1:
The patent replaces expensive gamma ray sensors with more cost-effective microwave transmitters and receivers. These microwave components are generally less expensive to manufacture and do not require the specialized shielding and safety infrastructure needed for gamma ray sources. The Venturi meter provides a mechanical flow measurement component that is also more cost-effective than gamma ray-based systems, overall reducing manufacturing costs while maintaining measurement accuracy.
3Measurement precision
If gamma ray sensors are used in multiphase flowmeters, then measurement capability is improved, but regulatory compliance requirements increase
Solution Approach 1:
The patent converts the potential harm of using radioactive gamma ray sources into a beneficial alternative by employing non-ionizing microwave radiation. Microwaves do not pose the same radiation safety concerns as gamma rays, eliminating the need for complex regulatory compliance regarding radioactive material handling, storage, and disposal. This substitution maintains measurement capability while removing the harmful regulatory burden associated with gamma ray sensors.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate and cost-effective measurement of multiphase fluid parameters, reducing operational complexity and compliance costs while maintaining high accuracy across varying flow conditions.
Implementation Method 1
a microwave receiver to, in response to receiving the signal, determine at least one of attenuation data or phase shift data based on the signal
Implementation Method 2
determine at least one of attenuation data or phase shift data based on the signal
Implementation Method 3
combined with a Venturi meter, to determine flow parameters
Data Source
AI summary
Multiphase flowmeters and related methods are disclosed herein. An example multiphase flowmeter includes a microwave transmitter to transmit a signal through a fluid, a microwave receiver to determine at least one of attenuation or phase shift, a sensor to obtain at least one of pressure, temperature, or differential pressure, an intermediate-output generator to determine intermediate flow parameters, and a flow rate generator to determine a flow rate for respective phases in the fluid, and a model selector to select at least one of a physics model or a machine learning model for determining the intermediate flow parameters or the flow rates, the physics model to determine a first value of the intermediate flow parameters or the flow rates, and, in response to an error not being less than a threshold, the machine learning model to determine a second value of the intermediate flow parameters or the flow rates.