CN111364986A - Device and method for measuring water holding rate of oil-water two-phase flow under oil well - Google Patents
Device and method for measuring water holding rate of oil-water two-phase flow under oil well Download PDFInfo
- Publication number
- CN111364986A CN111364986A CN202010089215.0A CN202010089215A CN111364986A CN 111364986 A CN111364986 A CN 111364986A CN 202010089215 A CN202010089215 A CN 202010089215A CN 111364986 A CN111364986 A CN 111364986A
- Authority
- CN
- China
- Prior art keywords
- water
- oil
- phase flow
- optical fiber
- light
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
Images
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
- E21B49/08—Obtaining fluid samples or testing fluids, in boreholes or wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Abstract
The invention discloses a device and a method for measuring the water holding capacity of oil-well underground oil-water two-phase flow, wherein the device comprises a sensing light source module, an optical fiber sensitive structure, a Raman spectrum acquisition module and a data processing module; the sensing light source module is used for transmitting light wave signals; the optical fiber sensitive structure is used for irradiating the light wave signal emitted by the sensing light source module to the two-phase flow to be measured, collecting the Raman scattering light generated by the oil-water two-phase flow to be measured and the light wave, and sending the Raman scattering light to the Raman spectrum acquisition module; the Raman spectrum acquisition module comprises a light splitting system and an imaging system, wherein the light splitting system is used for obtaining Raman scattering light intensity information in a specific wavelength range and separating Raman scattering light with different frequencies into Raman spectrum characteristic curves, and the imaging system is used for converting optical signals into electric signals; the data processing module is used for analyzing the oil-water two-phase flow Raman spectrum characteristic curve by using the deep learning model to obtain the concentration of oil in the two-phase flow, so that the water holding rate of the oil-water two-phase flow is obtained.
Description
Technical Field
The invention belongs to the field of two-phase flow water holding capacity measurement, and relates to a device and a method for measuring the water holding capacity of an oil well underground oil-water two-phase flow.
Background
In order to improve the exploitation efficiency of crude oil, water injection oil extraction measures and horizontal well exploitation methods are increasingly adopted in each oil field, which puts higher requirements on online detection of the water holding rate of oil-water two phases in the well.
The instruments commonly used for measuring the water holding capacity of oil-water two-phase flow at present comprise: a ray method water holding capacity measuring device, a microwave method water holding capacity measuring device, an electrical method water holding capacity measuring device, a thermal method water holding capacity measuring device, an ultrasonic method water holding capacity measuring device and the like.
The drawback of the ray method water retention measurement is that not only the problem of attenuation of the radiation passing through the tube wall needs to be solved, but also a stable and reliable radiation source is needed, and the radiation protection and the safety management of the radiation are also considered, so that the application of the method is limited.
The microwave method water holdup measuring device has the defects that the microwave has certain harm to human bodies and has higher cost requirements on safety management and maintenance.
The capacitance conductance value measured by the electrical method water holdup measuring device is not only related to the components of the fluid, but also influenced by the flow pattern of the fluid and the conductivity change of the components of the fluid, so that the precision in actual use is greatly influenced.
The actual use of the thermal method water holding rate measuring device is susceptible to noise.
The ultrasonic method water holdup measuring device comprises the measuring device, and has the defect that the device can not work at high temperature and high pressure any more, which causes great limitation on the measurement of the oil-water two-phase water holdup.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the device and the method for measuring the water holding rate of the oil-well underground oil-water two-phase flow, which have stable and reliable measurement, no harm to human bodies and no influence of high temperature and high pressure.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a device for measuring the water holding rate of oil-water two-phase flow under an oil well comprises a sensing light source module, an optical fiber sensitive structure, a Raman spectrum acquisition module and a data processing module;
the sensing light source module is used for transmitting light wave signals;
the optical fiber sensitive structure comprises a Y-shaped optical fiber, a shell and an optical fiber probe, wherein the Y-shaped optical fiber extends into the shell and is connected with the optical fiber probe, and two optical fibers branched at the front end of the Y-shaped optical fiber are an incident optical fiber and a collecting optical fiber respectively; the optical fiber probe comprises a lens and an optical filter, the lens is positioned at the front end of the shell, the front end of the optical filter faces the rear end of the lens, the incident optical fiber is connected with the rear end of the lens, and the collecting optical fiber is connected with the rear end of the optical filter; the rear end of the Y-shaped optical fiber is connected with the output end of the sensing light source module; the optical fiber sensitive structure is used for irradiating the light wave signals emitted by the sensing light source module onto the two-phase flow to be measured through the incident optical fiber of the Y-shaped optical fiber and the lens, collecting Raman scattering light generated by the two-phase flow of oil and water and the light wave to be measured through the collecting optical fiber and sending the backward Raman scattering light to the Raman spectrum acquisition module through the Y-shaped optical fiber;
the input end of the Raman spectrum acquisition module is connected with the rear end of the Y-shaped optical fiber, the Raman spectrum acquisition module comprises a light splitting system and an imaging system, the light splitting system is used for obtaining Raman scattering light intensity information in a specific wavelength range and separating Raman scattering light with different frequencies into Raman spectrum characteristic curves, and the imaging system is used for converting optical signals into electric signals;
the input end of the data processing module is connected with the output end of the Raman spectrum acquisition module, and the data processing module is used for analyzing an oil-water two-phase flow Raman spectrum characteristic curve by using a deep learning model to obtain the concentration of oil in the two-phase flow, so that the water holding rate of the oil-water two-phase flow is obtained.
Preferably, the optical splitting system uses a wavelength division multiplexer.
Preferably, the front end of the shell is provided with an optical glass window, and the optical glass window is positioned in front of the optical fiber probe.
Furthermore, the optical glass window is made of sapphire.
Preferably, the shell is of a full stainless steel structure and is arranged in a sealing mode.
A method for measuring the water holding capacity of the oil well downhole oil-water two-phase flow based on any one device comprises the following steps;
placing an optical fiber sensitive structure into an oil well pipeline, transmitting light wave signals with different line widths and power by a sensing light source module, and irradiating the light wave signals onto a measured two-phase flow through an incident optical fiber of a Y-shaped optical fiber and a lens;
collecting Raman scattered light generated by the measured oil-water two-phase flow and the light wave by the collecting optical fiber, and sending the backward Raman scattered light to a Raman spectrum acquisition module through the Y-shaped optical fiber;
thirdly, the Raman spectrum acquisition module receives the Raman scattering light with different frequencies, separates the Raman scattering light with different frequencies to obtain the Raman scattering light intensity information in a specific wavelength range, further obtains a Raman spectrum characteristic curve, converts an optical signal into an electric signal and sends the electric signal to the data processing module;
and step four, establishing a deep learning model of the water holding capacity of the oil-water two-phase flow in the data processing module, inputting the Raman spectrum characteristic curve of the oil-water two-phase flow into the deep learning model in the embedded computer module, and analyzing the Raman spectrum characteristic curve of the oil-water two-phase flow to obtain the concentration of the oil in the two-phase flow, so as to obtain the water holding capacity of the oil-water two-phase flow.
Preferably, in the third step, the first step,intensity of Raman scattered light I in a specific wavelength range(v)Comprises the following steps:
wherein C is the speed of light, h is the Planckian constant, ILFor the intensity of the excitation light, N is the number of scattered molecules collected by the collection fiber 9, v is the molecular vibration frequency, v0Mu is a vibration atom equivalent mass, kappa is a Boltzmann constant, and T is an absolute temperature value, α'aIs the polarizability tensor invariant mean, γ'aIs an anisotropic polarizability tensor invariant.
Preferably, in the fourth step, the establishing of the deep learning model of the water holding rate of the oil-water two-phase flow comprises the following steps;
step 1, configuring a plurality of oil-water two-phase flow samples with water holding rates from 0% to 100% to form a correction set and a verification set;
step 3, constructing a preliminary deep learning model with the characteristic information obtained in the step two correlated with the water holding rate of the oil-water two-phase flow, and performing model training on the preliminary deep learning model by using the characteristic information obtained in the step 2 to obtain a stable deep learning model;
and 4, performing result prediction on the water holding rate of the oil-water two-phase flow of the verification set by using a stable deep learning model to check whether the accuracy rate meets the requirement of engineering application, completing the establishment of the deep learning model if the required accuracy rate is met, and returning to the step 3 for retraining if the required accuracy rate is not met.
Compared with the prior art, the invention has the following beneficial effects:
the invention adopts the optical fiber and the optical fiber probe to send the optical wave signal to the two-phase flow to be measured and collect Raman scattered light, utilizes the high temperature and high pressure resistance of the optical fiber and the optical fiber probe to ensure that the detection device is not influenced by high temperature and high pressure, and utilizes the advantages of high resolution, high sensitivity, large dynamic range, convenience and rapidness of Raman spectrum measurement to ensure stable and reliable measurement process and ensure the precision of the measurement result.
The method provided by the invention utilizes the advantages of high resolution, high sensitivity, large dynamic range and convenience and rapidness of Raman spectrum measurement, and analyzes the Raman spectrum characteristic curve of the oil-water two-phase flow to obtain the concentration of the oil in the two-phase flow, so that the water holding capacity of the oil-water two-phase flow is obtained, the measurement precision is high, and an optical instrument is not subjected to electromagnetic interference and can be used for measuring a horizontal well.
Drawings
FIG. 1 is a schematic structural view of an apparatus for measuring water holdup of an oil-water two-phase flow according to the present invention;
FIG. 2 is a schematic structural diagram of an optical fiber sensing structure according to the present invention;
FIG. 3 is a schematic representation of Raman scattering of downhole fluid molecules or particles according to the present invention;
FIG. 4 is a flowchart of deep learning model building according to the present invention.
Wherein: 1-a sensing light source module; 2-optical fiber sensitive structure; 3-a Raman spectrum acquisition module; 4-a data processing module; 5-Y type optical fiber; 6-a shell; 7-a fiber optic probe; 8-an incident optical fiber; 9-collecting optical fibers; 10-a lens; 11-an optical filter; 12-an optical glazing; 13-oil well pipe.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
as shown in figure 1, the oil well downhole oil-water two-phase flow water holdup measuring device comprises a sensing light source module 1, an optical fiber sensitive structure 2 and a signal processing module; the signal processing module comprises a Raman spectrum acquisition module 3 and a data processing module 4.
The sensing light source module 1 is used for emitting light waves with a certain line width and power; the optical fiber sensitive structure 2 is used for irradiating the light wave signal emitted by the sensing light source module 1 to the two-phase flow to be measured through the incident optical fiber 8 of the Y-shaped optical fiber 5 and the lens 10, collecting the Raman scattering light generated by the two-phase flow of oil and water and the light wave to be measured through the collecting optical fiber 9, and sending the backward Raman scattering light to the Raman spectrum acquisition module 3 through the Y-shaped optical fiber 5; the input end of the Raman spectrum acquisition module 3 is connected with the output end of the optical fiber sensitive structure 2 and is used for receiving Raman scattering light transmitted by the optical fiber sensitive structure 2 to obtain Raman scattering light intensity information in a specific wavelength range, separating the Raman scattering light with different frequencies into Raman spectrum characteristic curves, and converting the Raman scattering light into electric signals after light splitting and outputting the electric signals; the data processing module 4 adopts an embedded computer with the model of PC104, and the input end of the embedded computer is connected with the output end of the Raman spectrum acquisition module 3 and is used for calculating the water holding rate of the oil-water two-phase flow.
As shown in fig. 2, the optical fiber sensitive structure 2 is composed of the following parts: y-shaped optical fiber 5, metal shell, optical glass window 12 and optical fiber probe 7. The shell 6 is of a full stainless steel structure and is arranged in a sealing mode, so that the optical fiber sensitive structure 2 can be protected to be normally used under severe working environment conditions such as high temperature, high pressure, sediment accumulation or corrosive media in fluid, and the diameter of the front end of the shell 6 is smaller than that of the rear end; the Y-shaped optical fiber 5 extends into the shell 6 to be connected with the optical fiber probe 7, and two optical fibers branched at the front end of the Y-shaped optical fiber 5 are an incident optical fiber 8 and a collecting optical fiber 9 respectively; the optical fiber probe 7 comprises a lens 10 and an optical filter 11, the lens 10 is positioned at the front end of the shell 6, the front end of the optical filter 11 faces the rear end of the lens 10, the incident optical fiber 8 is connected with the rear end of the lens 10, and the collecting optical fiber 9 is connected with the rear end of the optical filter 11; the rear end of the Y-shaped optical fiber 5 is connected with the output end of the sensing light source module 1.
The front end of the shell 6 is provided with an optical glass window 12, and the optical glass window 12 is positioned in front of the optical fiber probe 7, so that light waves output by the optical fiber probe 7 can be incident on fluid, and backward scattered light returns to the optical fiber probe 7. In order to better protect the fiber-optic probe 7, the optical glass window 12 is made of sapphire.
The optical fiber probe 7 collects the backward rayleigh scattered light and the raman scattered light generated by converging and transmitting the light in the sensing light source module 1 into the oil-water two-phase flow through the incident optical fiber 8 by the lens 10, filters the rayleigh scattered light without wavelength change through the filter 11, and transmits the raman scattered light into the collection optical fiber 9.
As shown in fig. 3, the principle of collecting and processing the fluid molecular raman scattering cloud signal is that light in the light source module is converged and transmitted to the oil-water two-phase flow through the incident optical fiber 8 by the lens 10 to generate rayleigh scattering light and raman scattering light, the generated backward rayleigh scattering light and raman scattering light are collected by the lens 10, the rayleigh scattering light with unchanged wavelength is filtered by the optical filter 11, the raman scattering light is transmitted to the collecting optical fiber 9, and then transmitted to the raman spectrum collecting module 3 in the signal processing module through the optical fiber sensitive structure 2.
The raman spectrum acquisition module 3 comprises a light splitting system and an imaging system, wherein the light splitting system adopts a wavelength division multiplexer to separate the raman scattering light with different frequencies to obtain the intensity information of the raman scattering light in a specific wavelength range, so as to obtain a raman spectrum characteristic curve, and the imaging system is used for converting optical signals into electric signals.
The sensing light source module 1 emits light waves with a certain line width and power, the light waves irradiate the oil-water two-phase flow to be detected through the optical fiber sensing structure 2, the oil-water two-phase flow to be detected reacts with the light waves, and the generated backward Raman scattering light is collected back to the collecting optical fiber 9 of the optical fiber probe 7 through the lens 10 and is transmitted to the Raman spectrum collecting module 3 in the signal processing module through the optical fiber sensing structure 2. The optical fiber sensitive structure 2 adopts a Y-shaped optical fiber 5, one end of the Y-shaped optical fiber is connected with the sensing light source module 1, the other end of the Y-shaped optical fiber is connected with the Raman spectrum acquisition module 3 in the signal processing module, the last end of the Y-shaped optical fiber is connected with a transmission optical fiber, the other end of the transmission optical fiber is connected with an optical fiber probe 7, and the optical fiber probe 7 needs to stretch into the oil-water two-phase.
The Raman scattering light with different frequencies incident on the Raman spectrum acquisition module 3 is divided into Raman spectrum characteristic curves by the light splitting system, then an imaging system converts optical signals into electric signals, the electric signals are transmitted into the embedded computer module, the Raman scattering light with different frequencies is divided into Raman spectrum characteristic curves by the light splitting system, and the Raman spectrum characteristic curves are analyzed through an artificial intelligence analysis algorithm based on deep learning, so that the water holding rate of oil-water two-phase flow is obtained.
The method for measuring the water holding rate of the oil-water two-phase flow comprises the following steps;
firstly, the optical fiber sensitive structure 2 is placed in an oil well pipeline 13, a sensing light source emits light wave signals with different line widths and power, the light wave signals pass through an incident optical fiber 8 of a Y-shaped optical fiber 5, and the light wave signals are irradiated on the two-phase flow to be measured through a lens 10.
And step two, Raman scattering is generated between the measured oil-water two-phase flow and the light wave, wherein a part of backward Raman scattering light returns to the sensing optical fiber through the optical fiber probe 7 again and then is transmitted to a Raman analysis module in the signal processing module.
And step three, receiving the Raman scattering light of each frequency of the optical signal in the Raman spectrum acquisition module 3, separating the Raman scattering light of different frequencies to obtain the Raman scattering light intensity information in a specific wavelength range, further obtaining a Raman spectrum characteristic curve, and converting the Raman scattering light into an electric signal by a photoelectric detector in the Raman spectrum acquisition module 3.
Intensity of Raman scattered light I in a specific wavelength range(v)Comprises the following steps:
wherein C is the speed of light, h is the Planckian constant, ILFor the intensity of the excitation light, N is the number of scattered molecules collected by the collection fiber 9, v is the molecular vibration frequency, v0Mu is a vibration atom equivalent mass, kappa is a Boltzmann constant, and T is an absolute temperature value, α'aIs the polarizability tensor invariant mean, γ'aIs an anisotropic polarizability tensor invariant.
And step four, establishing a deep learning model, wherein the deep learning model is a correction model which is constructed by selecting and extracting the Raman peak wavelength and the intensity characteristic of the oil-water two-phase flow with known concentration and training the characteristic data, and the Raman spectrum and the water holding capacity of the oil-water two-phase flow are quantitatively related, as shown in figure 4, the specific process is as follows.
1. Selecting a sample; a large number of oil-water two-phase flow samples with water holding rates from 0% to 100% are configured to form a correction set and a verification set.
2. And (3) performing the experiments from the step one to the step three on the calibration set samples with different water holdup rates respectively to obtain intensity characteristic information of different wavelengths and the wavelengths, and selecting and extracting the wavelengths and the intensity characteristic information with ideal signals and good discrimination in the Raman spectrum.
3. And (3) constructing a preliminary deep learning model with the extracted characteristic information and the water holding rate of the oil-water two-phase flow related by using Python, and performing model training on the preliminary deep learning model by using the massive selectively extracted characteristic information obtained in the step (2) to obtain a stable deep learning model.
4. And (3) performing result prediction on the water holding rate of the oil-water two-phase flow of the verification set by using a stable deep learning model to check whether the accuracy rate meets the requirement of engineering application, completing the establishment of the deep learning model if the required accuracy rate is met, and returning to the step (3) for retraining if the required accuracy rate is not met.
And inputting the oil-water two-phase flow Raman spectrum characteristic curve into a deep learning model in an embedded computer module, and analyzing the oil-water two-phase flow Raman spectrum characteristic curve to obtain the concentration of oil in two-phase flow, thereby obtaining the water holding rate of the oil-water two-phase flow.
Compared with the traditional principal component regression method and partial least square regression method, the deep learning method has the advantages of being high in nonlinear problem solving capability, capable of obtaining a better prediction effect and high in problem solving efficiency by utilizing the technical advantages of high resolution, high sensitivity, large dynamic range, convenience, rapidness and the like of Raman spectrum measurement and the advantages of being capable of achieving better performance and stronger in severe environment adaptability compared with the traditional oil-water two-phase flow water holding rate measuring device.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (8)
1. The device for measuring the water holding capacity of the oil-well underground oil-water two-phase flow is characterized by comprising a sensing light source module (1), an optical fiber sensitive structure (2), a Raman spectrum acquisition module (3) and a data processing module (4);
the sensing light source module (1) is used for emitting light wave signals;
the optical fiber sensitive structure (2) comprises a Y-shaped optical fiber (5), a shell (6) and an optical fiber probe (7), wherein the Y-shaped optical fiber (5) extends into the shell (6) to be connected with the optical fiber probe (7), and two optical fibers branched at the front end of the Y-shaped optical fiber (5) are an incident optical fiber (8) and a collecting optical fiber (9) respectively; the optical fiber probe (7) comprises a lens (10) and an optical filter (11), the lens (10) is positioned at the front end of the shell (6), the front end of the optical filter (11) faces the rear end of the lens (10), the incident optical fiber (8) is connected with the rear end of the lens (10), and the collecting optical fiber (9) is connected with the rear end of the optical filter (11); the rear end of the Y-shaped optical fiber (5) is connected with the output end of the sensing light source module (1); the optical fiber sensitive structure (2) is used for irradiating a light wave signal emitted by the sensing light source module (1) to a measured two-phase flow through an incident optical fiber (8) of the Y-shaped optical fiber (5) through a lens (10), collecting Raman scattering light generated by the measured oil-water two-phase flow and the light wave through a collecting optical fiber (9), and sending the backward Raman scattering light to the Raman spectrum acquisition module (3) through the Y-shaped optical fiber (5);
the input end of the Raman spectrum acquisition module (3) is connected with the rear end of the Y-shaped optical fiber (5), the Raman spectrum acquisition module (3) comprises a light splitting system and an imaging system, the light splitting system is used for obtaining Raman scattering light intensity information in a specific wavelength range and separating Raman scattering light with different frequencies into Raman spectrum characteristic curves, and the imaging system is used for converting optical signals into electrical signals;
the input end of the data processing module (4) is connected with the output end of the Raman spectrum acquisition module (3), and the data processing module (4) is used for analyzing an oil-water two-phase flow Raman spectrum characteristic curve by using a deep learning model to obtain the concentration of oil in the two-phase flow, so that the water holding rate of the oil-water two-phase flow is obtained.
2. The device for measuring the water holdup of the oil-well downhole oil-water two-phase flow according to claim 1, wherein the light splitting system adopts a wavelength division multiplexer.
3. The oil well downhole oil-water two-phase flow water holdup measuring device according to claim 1, characterized in that the front end of the shell (6) is provided with an optical glass window (12), and the optical glass window (12) is positioned in front of the optical fiber probe (7).
4. The oil well downhole oil-water two-phase flow water holdup measuring device according to claim 3, wherein the optical glass window (12) is made of sapphire.
5. The oil well downhole oil-water two-phase flow water holdup measuring device according to claim 1, characterized in that the housing (6) is made of a full stainless steel structure and is arranged in a sealing manner.
6. An oil well downhole oil-water two-phase flow water holding rate measuring method based on the device of any one of claims 1 to 5, characterized by comprising the steps of;
placing an optical fiber sensitive structure (2) into an oil well pipeline (13), emitting light wave signals with different line widths and power by a sensing light source module (1), irradiating the light wave signals onto a two-phase flow to be measured through an incident optical fiber (8) of a Y-shaped optical fiber (5) and a lens (10);
step two, collecting the Raman scattered light generated by the oil-water two-phase flow and the light wave to be detected by the collecting optical fiber (9), and sending the backward Raman scattered light to the Raman spectrum acquisition module (3) through the Y-shaped optical fiber (5);
thirdly, the Raman spectrum acquisition module (3) receives the Raman scattering light with different frequencies, separates the Raman scattering light with different frequencies to obtain the Raman scattering light intensity information in a specific wavelength range, further obtains a Raman spectrum characteristic curve, converts an optical signal into an electric signal and sends the electric signal to the data processing module (4);
and fourthly, establishing a deep learning model of the water holding capacity of the oil-water two-phase flow in the data processing module (4), inputting the Raman spectrum characteristic curve of the oil-water two-phase flow into the deep learning model in the embedded computer module, and analyzing the Raman spectrum characteristic curve of the oil-water two-phase flow to obtain the concentration of the oil in the two-phase flow, so that the water holding capacity of the oil-water two-phase flow is obtained.
7. The method for measuring the water holding capacity of the oil-well downhole oil-water two-phase flow according to claim 6, wherein in the third step, the intensity I of the Raman scattered light in a specific wavelength range(v)Comprises the following steps:
wherein C is the speed of light, h is the Planckian constant, ILFor the intensity of the excitation light, N is the number of scattered molecules collected by the collection fiber 9, v is the molecular vibration frequency, v0Mu is a vibration atom equivalent mass, kappa is a Boltzmann constant, and T is an absolute temperature value, α'aIs the polarizability tensor invariant mean, γ'aIs an anisotropic polarizability tensor invariant.
8. The method for measuring the water holding capacity of the oil-water two-phase flow under the oil well according to claim 6, wherein in the fourth step, the establishing of the deep learning model of the water holding capacity of the oil-water two-phase flow comprises the following steps;
step 1, configuring a plurality of oil-water two-phase flow samples with water holding rates from 0% to 100% to form a correction set and a verification set;
step 2, respectively carrying out the first step to the third step on the calibration set samples with different water holdup rates to obtain intensity characteristic information of different wavelengths and the wavelengths;
step 3, constructing a preliminary deep learning model with the characteristic information obtained in the step two correlated with the water holding rate of the oil-water two-phase flow, and performing model training on the preliminary deep learning model by using the characteristic information obtained in the step 2 to obtain a stable deep learning model;
and 4, performing result prediction on the water holding rate of the oil-water two-phase flow of the verification set by using a stable deep learning model to check whether the accuracy rate meets the requirement of engineering application, completing the establishment of the deep learning model if the required accuracy rate is met, and returning to the step 3 for retraining if the required accuracy rate is not met.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010089215.0A CN111364986A (en) | 2020-02-12 | 2020-02-12 | Device and method for measuring water holding rate of oil-water two-phase flow under oil well |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010089215.0A CN111364986A (en) | 2020-02-12 | 2020-02-12 | Device and method for measuring water holding rate of oil-water two-phase flow under oil well |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111364986A true CN111364986A (en) | 2020-07-03 |
Family
ID=71206212
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010089215.0A Pending CN111364986A (en) | 2020-02-12 | 2020-02-12 | Device and method for measuring water holding rate of oil-water two-phase flow under oil well |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111364986A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111982862A (en) * | 2020-08-01 | 2020-11-24 | 中国石油天然气股份有限公司 | Calculation method of gas-liquid two-phase flow gas holdup of optical fiber sensor |
| CN112304922A (en) * | 2020-10-29 | 2021-02-02 | 辽宁石油化工大学 | Method for quantitatively analyzing crude oil by Raman spectrum based on partial least square method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202187760U (en) * | 2011-08-11 | 2012-04-11 | 中国石油化工股份有限公司 | Real-time monitoring logging system for oil gas of formation fluid |
| WO2015135415A1 (en) * | 2014-03-10 | 2015-09-17 | 北京理工大学 | Method and apparatus for measuring light-splitting pupil laser differential motion confocal brillouin-raman spectrums |
| CN205384224U (en) * | 2016-03-10 | 2016-07-13 | 北京杏林睿光科技有限公司 | Raman spectroscopy measures experimental apparatus |
| CN108982405A (en) * | 2018-08-30 | 2018-12-11 | 电子科技大学 | A kind of water content in oil measurement method and measuring instrument based on deep learning |
| CN109187426A (en) * | 2018-08-30 | 2019-01-11 | 四川莱威盛世科技有限公司 | A kind of water content in oil measuring instrument and measurement method based on infra-red sepectrometry |
| CN110376180A (en) * | 2019-07-31 | 2019-10-25 | 东北大学 | A kind of Raman Measurement system and method for the high spectral resolution based on LED light source |
-
2020
- 2020-02-12 CN CN202010089215.0A patent/CN111364986A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202187760U (en) * | 2011-08-11 | 2012-04-11 | 中国石油化工股份有限公司 | Real-time monitoring logging system for oil gas of formation fluid |
| WO2015135415A1 (en) * | 2014-03-10 | 2015-09-17 | 北京理工大学 | Method and apparatus for measuring light-splitting pupil laser differential motion confocal brillouin-raman spectrums |
| CN205384224U (en) * | 2016-03-10 | 2016-07-13 | 北京杏林睿光科技有限公司 | Raman spectroscopy measures experimental apparatus |
| CN108982405A (en) * | 2018-08-30 | 2018-12-11 | 电子科技大学 | A kind of water content in oil measurement method and measuring instrument based on deep learning |
| CN109187426A (en) * | 2018-08-30 | 2019-01-11 | 四川莱威盛世科技有限公司 | A kind of water content in oil measuring instrument and measurement method based on infra-red sepectrometry |
| CN110376180A (en) * | 2019-07-31 | 2019-10-25 | 东北大学 | A kind of Raman Measurement system and method for the high spectral resolution based on LED light source |
Non-Patent Citations (1)
| Title |
|---|
| 朱和国等, 东南大学出版社 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111982862A (en) * | 2020-08-01 | 2020-11-24 | 中国石油天然气股份有限公司 | Calculation method of gas-liquid two-phase flow gas holdup of optical fiber sensor |
| CN111982862B (en) * | 2020-08-01 | 2023-10-27 | 中国石油天然气股份有限公司 | Calculation method for gas-liquid two-phase flow gas holding rate of optical fiber sensor |
| CN112304922A (en) * | 2020-10-29 | 2021-02-02 | 辽宁石油化工大学 | Method for quantitatively analyzing crude oil by Raman spectrum based on partial least square method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20200291772A1 (en) | Detecting events at a flow line using acoustic frequency domain features | |
| CN102197287B (en) | Equipment and method for tracking objects in conduits | |
| JP2020537147A (en) | Event detection using acoustic frequency domain features | |
| JP2018511062A (en) | Online measurement of black powder in gas and oil pipelines | |
| EP1903329A1 (en) | An apparatus and method for optically determining the presence of carbone dioxide | |
| CN111364986A (en) | Device and method for measuring water holding rate of oil-water two-phase flow under oil well | |
| US20230043381A1 (en) | Detecting flow obstruction events within a flow line using acoustic frequency domain features | |
| CN101699265A (en) | Device and method for measuring scattering particles by using dynamic polarized light | |
| CN103558179A (en) | Apparatus and method for measuring sectional phase volume fraction of gas-liquid two-phase flow in pipeline | |
| CN108415067A (en) | A kind of earthquake wave measuring system based on microstructured optical fibers distribution sound wave sensing | |
| CN104034666A (en) | Graded and fast detection device and method for quality of foods and medicines | |
| CN103822868A (en) | Device and method for detecting grain size of phytoplankton in seawater | |
| CN101122555A (en) | High concentration super fine granule measuring device and method based on backward photon related spectrum | |
| CN105067101A (en) | Fundamental tone frequency characteristic extraction method based on vibration signal for vibration source identification | |
| EP3479098A1 (en) | Apparatus and method for analyzing composition in oil and gas production well | |
| CN110273681B (en) | System and method for measuring oil-gas-water multiphase fluid split-phase content rate in petroleum production logging | |
| CN108344432B (en) | The method for obtaining the heat transfer agent of Brillouin fiber optic distributed sensing system | |
| CN105092691A (en) | Quantitative detection method and detector for detecting accumulated oxide scales in pipelines | |
| US9464986B2 (en) | Multiplex fiber optic biosensor and detection method by using the same | |
| Vahabi et al. | Fluid flow velocity measurement in active wells using fiber optic distributed acoustic sensors | |
| CN116026449A (en) | Vibration positioning monitoring system based on single-core optical fiber sensing | |
| CN109798448A (en) | Concrete duct leakage experiment device and method based on anti-Stokes light filtering | |
| Zhang et al. | Tunnel disturbance events monitoring and recognition with distributed acoustic sensing (DAS) | |
| CN103257114B (en) | Underground gas detecting method based on optical fiber Bragg grating sensor network | |
| CN112611444B (en) | Distributed optical fiber vibration monitoring system and method capable of achieving accurate positioning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |