Microfluidic and fluorescence microscope based oil-water multiphase flow monitoring and identification system

Abstract

The application discloses an oil-water multiphase flow monitoring and identifying system based on microfluidics and fluorescence microscopy, and relates to the field of oil-water multiphase flow monitoring and identifying systems; comprising a microfluidic chip, a fluid injection control, a microscopic imaging, an image processing module and a produced liquid collecting device. The microfluidic chip is etched based on real core CT data and accurately simulates pore structure; the fluid injection control module realizes high-precision liquid injection and constant temperature control; the microscopic imaging module captures four-phase fluid dynamics through multi-band fluorescence imaging; the image processing module carries self-developed algorithms, can identify six types of remaining oil and complete multi-dimensional quantitative analysis, and the results can feedback injection parameters. The system solves the problems of real-time observation loss, high cost and indirect mechanism inference of traditional core experiments, realizes in-situ visualization of the displacement process, data precision and intelligent regulation and control, is suitable for chemical flooding mechanism research of complex reservoirs, and provides an efficient research platform for improving recovery efficiency.

Description

Technical Field

[0001] This invention relates to the field of oil-water multiphase flow monitoring and identification systems, specifically an oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy. Background Technology

[0002] In chemical flooding for enhanced oil recovery, polymers and other chemicals are prone to flocculation reactions with formation particles, leading to pore throat blockage and uneven fluid distribution, significantly affecting the displacement effect. Traditional core displacement experiments are costly, time-consuming, and cannot observe microscopic flow processes in real time. Microfluidic technology combined with fluorescence microscopy provides an effective means to study multiphase flow mechanisms at the pore scale, but currently there is still a lack of dedicated devices and methods that can systematically integrate real-time observation, dynamic identification, and intelligent analysis.

[0003] Traditional core displacement experiments have the following significant shortcomings:

[0004] (1) Lack of real-time visualization capability: Traditional displacement experiments cannot achieve in-situ, dynamic and visual observation of the displacement process. They can only rely on core slices or CT scans after the displacement is completed for static analysis, making it difficult to capture the real-time changes of key processes such as polymer flocculation, particle migration and dynamic distribution of oil and water.

[0005] (2) High experimental costs and long cycle: The cost of each real core displacement experiment is usually as high as thousands of yuan, and the core is a consumable and cannot be reused; the experimental cycle is long, often requiring several days or even weeks from preparation to completion. In contrast, microfluidic chips are low in cost, reusable, and the time of a single experiment is greatly shortened, significantly improving research efficiency.

[0006] (3) The inference of micro-mechanisms is indirect and the accuracy is limited: Traditional methods rely on indirect data after displacement (such as pressure curves, recovery rate, and slice images) to infer micro-mechanisms. They cannot directly observe key processes such as multiphase fluid interaction, floc formation and migration in pores, and the conclusions contain many assumptions and uncertainties.

[0007] (4) Difficulty in simulating real pore structure and complex fluid behavior: Although traditional core experiments can reflect macroscopic displacement effects, they cannot accurately control pore structure characteristics (such as throat distribution and connectivity), and it is also difficult to simulate fluid behavior at the micro-nano scale (such as interface effects and capillary action). Microfluidic models are based on etching of real core CT data, which is closer to the real pore throat structure, and supports fine simulation of multiphase fluids, chemical agents and particle interactions.

[0008] (5) Insufficient precision in experimental parameter control: Traditional displacement experiments have low precision in terms of injection rate, pressure response, and temperature control, and are easily affected by fluctuations in experimental equipment. Microfluidic systems, combined with high-precision syringe pumps, temperature control modules, and real-time image acquisition, can achieve high-precision control and dynamic adjustment of injection parameters, resulting in higher data reliability.

[0009] (6) Inability to achieve multi-dimensional data fusion analysis: Traditional methods have difficulty in simultaneously acquiring multi-dimensional data on fluid motion, chemical reactions and geological structures. This technology integrates fluorescent labeling, multispectral imaging and image processing algorithms, which can simultaneously identify and quantify oil, water, polymers and particles, supporting more comprehensive mechanism research.

[0010] (7) Limited application scenarios: Traditional core experiments have limited ability to simulate low-permeability, ultra-low-permeability, and fractured reservoirs, while microfluidic models can flexibly adjust pore structure, surface wettability, and fluid composition, making them more suitable for simulation studies of complex reservoir conditions, especially for the study of micro-mechanisms such as chemical flooding and flocculation reactions.

[0011] (8) Lack of real-time feedback and intelligent control capabilities: Traditional methods are usually open-loop experiments, which cannot dynamically adjust the injection strategy based on real-time observation results. This technology can be combined with image processing algorithms and automatic control systems to achieve real-time feedback and intelligent optimization of the displacement process, providing more accurate decision support for oilfield development. Summary of the Invention

[0012] The purpose of this invention is to provide an oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy to solve the problems mentioned in the background art.

[0013] To achieve the above objectives, the present invention provides the following technical solution: an oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy, comprising: a microfluidic chip module, a fluid injection control module, a microscopic imaging module, and an image processing module;

[0014] The microfluidic chip module is a glass chip formed by laser confocal scanning and vector processing based on real core CT data and then etching it. It is used to simulate the pore structure of real cores, and its injection end is connected to the fluid injection control module.

[0015] The fluid injection control module includes a high-precision injection pump and a temperature control system. The high-precision injection pump is connected to the injection end of the microfluidic chip module to inject a binary composite displacement fluid into the chip. The temperature control system acts directly on the microfluidic chip module to maintain the constant temperature environment required for the experiment.

[0016] The microscopic imaging module includes a fluorescence microscope and a CCD camera. The fluorescence microscope is aimed at the pore structure region of the microfluidic chip module to acquire fluorescence images of oil, water, chemical agents and particle transport within the chip in real time, and transmits the acquired image data to the image processing module.

[0017] The image processing module is a residual oil quantitative analysis software equipped with a self-developed algorithm. It establishes data interaction with the microscopic imaging module to receive image data, and realizes multiphase fluid identification and residual oil distribution quantitative analysis through the algorithm. The analysis results can be used to adjust the injection parameters of the fluid injection control module.

[0018] Furthermore, the glass chip fabrication method for the microfluidic chip module is as follows:

[0019] First, the glass substrate is used as the base material. It is ultrasonically cleaned with alcohol and acetone for 15 minutes, then thoroughly rinsed with deionized water, and finally dried at 50°C for later use.

[0020] Second, a laser confocal scanning system was used to obtain real core pore structure data with a scanning resolution of micrometer level. The bitmap obtained from the scan was then converted into vector graphics using vectorization software.

[0021] Third, SU-8 photoresist was used for coating. A spin coater was used to rotate the coating at 4500 rpm for 10 minutes to ensure a uniform coating layer. Ultraviolet exposure was then performed with an exposure energy controlled at 200 mJ / cm². After exposure, development was performed for 60 seconds.

[0022] Fourth, wet chemical etching is performed using hydrofluoric acid buffer solution, with the etching time controlled at 30 minutes and the etching depth maintained within the range of 50-100 μm;

[0023] Fifth, select a glass cover plate, evenly coat the surface with UV-curable adhesive, press it firmly with the etched substrate, and irradiate it with a UV lamp for 1 hour to complete the curing; use a 1mm diameter glass drill bit to drill holes at the injection end and the extraction end to complete the chip fabrication.

[0024] Furthermore, the binary composite displacement solution is prepared by dissolving two solutions in deionized water: a 1000 mg / L HPAM polymer solution with a molecular weight of 25 million and a 0.2% petroleum sulfonate solution. The solutions are then thoroughly dissolved and mixed using a stirrer, with the temperature controlled at 20-30°C and the pH value kept neutral.

[0025] Furthermore, the residual oil quantitative analysis software of the image processing module can identify six types of residual oil distribution: pore surface film, particle adsorption, corner, throat, cluster, and intergranular adsorption residual oil. It can also calculate the area percentage (Sj), number density (nj), and average scale (Sj) of each type of residual oil. ) and average thickness ( ), interface contact degree and type distribution ratio;

[0026] Furthermore, the formula for calculating the area percentage (Sj) is as follows:

[0027]

[0028]

[0029]

[0030] in: - Total area of ​​type j (μm) 2 ); - The actual area represented by a single pixel (μm) 2 ); - Residual oil mask of type j: 1 if a pixel belongs to oil of type j, 0 otherwise; -The effective pore area of ​​the entire observation area; - Solid framework / particle mask: Solid is 1, porosity is 0; - Area percentage of type j;

[0031] The formula for calculating the number density (nj) is as follows:

[0032]

[0033] in: - The number of oil droplets / spots of type j; - The set of all oil-connected domains (each C is an oil cluster); - Connected region C; - The number density per unit area reflects the density of this type of oil;

[0034] mean scale ( ) and average thickness ( The calculation formula is as follows:

[0035]

[0036]

[0037] in: -The average scale of type j; - The equivalent diameter of the connected region C: the diameter of a circle of equal area, representing the size of the oil globule; - Average thickness of type j; - Average thickness of oil spot C;

[0038] The formula for calculating interface contact degree is as follows:

[0039]

[0040]

[0041]

[0042] in: - The total contact length between the oil stain of type j and the solid wall surface; - The length of the interface between oil stain C and the solid wall / particle; - The total contact length between the oil spot of type j and the aqueous phase; -Length of the interface between oil spot C and the aqueous phase; - The proportion of oil spots of type j that are "encased" by solids; - The perimeter of oil spot C;

[0043] Formula for calculating the distribution ratio of types:

[0044]

[0045]

[0046] in: - The proportion of type j in all remaining oil; - Total area of ​​type j; - The total area of ​​residual oil of all types.

[0047] Furthermore, it also includes a produced fluid collection device, which is connected to the production end of the microfluidic chip module and equipped with a volume measurement sensor. This device can record the produced fluid volume in real time and transmit the data to the image processing module, which is used to correct the calculated value of oil displacement efficiency by combining the image analysis results.

[0048] Furthermore, the fluid injected by the fluid injection control module also includes kaolinite suspension and crude oil, wherein the kaolinite suspension has a concentration of 30% and a pH value of 4, the crude oil has a density of 0.8626 g / cm³ and a kinematic viscosity of 18.65 mm² / s, and the crude oil needs to be preheated to the experimental set temperature before injection.

[0049] Furthermore, the fluorescence microscope of the microscopic imaging module is equipped with a DAPI / FITC / TRITC filter group, and the CCD camera has a resolution of no less than 2048×2048 pixels, supporting multi-band fluorescence imaging to distinguish four-phase fluids of oil, water, polymers and particles.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] This invention uses a microfluidic chip to simulate the pore structure of real rock cores and combines it with fluorescence microscopy multispectral imaging technology to achieve real-time, in-situ, and dynamic visualization of the migration behavior of oil, water, polymers and particles during displacement, which significantly improves the accuracy of understanding the microscopic oil displacement mechanism.

[0052] This invention uses fluorescent labeling and high-definition imaging to clearly capture the dynamic processes of floc formation, migration, and deposition, and to identify the location and extent of their blockage of seepage channels. This provides direct experimental evidence for optimizing polymer molecular weight, concentration, and injection strategies, thereby reducing reservoir damage.

[0053] This invention employs a reusable microfluidic chip combined with a precision injection pump control system, resulting in extremely low cost and significantly shortened cycle time for a single experiment. It supports rapid, multi-condition reproducible experiments and comparative analysis, greatly improving research efficiency.

[0054] This invention uses multi-band fluorescence labeling and image processing algorithms to achieve accurate identification and quantitative analysis of the transport path, saturation, and distribution state of multiphase fluids, providing high-precision data support for numerical simulation and displacement policy optimization.

[0055] This invention replicates a microfluidic model based on real core CT data, which can simulate the displacement process under different permeability, heterogeneity and wettability conditions, and is closer to the actual reservoir environment. It is especially suitable for mechanism research of low-permeability and fractured reservoirs.

[0056] This invention allows for direct observation of phenomena such as polymer flocculation, particle retention, and emulsion blockage, clarifying the damage mechanism and providing intuitive and reliable experimental evidence for optimizing chemical flooding formulations and developing anti-blockage measures.

[0057] This invention, by integrating microfluidic visualization technology with fluorescence microscopy, overcomes the limitations of traditional core displacement experiments in terms of observation capabilities, cost efficiency, data dimensionality, and depth of mechanism research. It provides an efficient, intuitive, and reliable research platform for solving key problems such as polymer flocculation blockage, reservoir damage optimization, and improved oil recovery during chemical flooding, and has significant scientific value and engineering application prospects. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the real-time observation and identification system for oil-water migration using a fluorescence microscope as described in this invention;

[0059] Figure 2 yes Figure 1 Schematic diagram of a microfluidic chip;

[0060] Figure 3 These are observation diagrams during the polymer flooding experiment;

[0061] Figure 4 This is a schematic diagram of polymer and crude oil identification under transmitted light, crossed light, and fluorescence illumination using a fluorescence microscope.

[0062] Figure 5 This is a quantitative analysis diagram of residual oil at the microscopic level from a core fluorescence image. Detailed Implementation

[0063] Please see Figure 1 —5. The present invention provides a technical solution: an oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy, comprising: a microfluidic chip module, a fluid injection control module, a microscopic imaging module, and an image processing module;

[0064] The microfluidic chip module is a glass chip formed by laser confocal scanning and vector processing based on real core CT data and then etching. It is used to simulate the pore structure of real cores, with a pore size range of 1–100 μm, and its injection end is connected to the fluid injection control module.

[0065] The fluid injection control module includes a high-precision injection pump and a temperature control system. The high-precision injection pump is connected to the injection end of the microfluidic chip module to inject binary composite displacement fluid into the chip. The temperature control system acts directly on the microfluidic chip module to maintain the constant temperature environment required for the experiment.

[0066] The microscopic imaging module includes a fluorescence microscope and a CCD camera. The fluorescence microscope is aimed at the porous structure area of ​​the microfluidic chip module to acquire fluorescence images of oil, water, chemical agents and particle transport within the chip in real time, and transmits the acquired image data to the image processing module.

[0067] The image processing module is a residual oil quantitative analysis software equipped with a self-developed algorithm. It establishes data interaction with the microscopic imaging module to receive image data, and realizes multiphase fluid identification and residual oil distribution quantitative analysis through the algorithm. The analysis results can be used to adjust the injection parameters of the fluid injection control module.

[0068] The displacement experiment includes the following steps:

[0069] First, the glass substrate is cleaned (alcohol → acetone → deionized water); after cleaning, the core section is scanned with a laser and vectorized; finally, the chip is coated with adhesive, exposed to ultraviolet light, developed, etched with hydrofluoric acid, and encapsulated with holes.

[0070] Prepare experimental reagents, including a 1000 mg / L solution of HPAM polymer with a molecular weight of 25 million and a 0.2% solution of petroleum sulfonate, forming a binary composite displacement solution; kaolinite suspension, 30% concentration, pH=4; and crude oil, density 0.8626 g / cm³, viscosity 18.65 mm² / s.

[0071] First, saturate the model with a kaolinite suspension or crude oil mixture; then inject the displacement fluid (10–80 PV) into the model using an injection pump and maintain the temperature at 46°C; finally, use a fluorescence microscope to acquire images in real time and analyze the formation and migration of flocs.

[0072] The distribution of remaining oil is quantified using image algorithms; after analysis, the oil displacement efficiency is calculated and the degree of flocculation and blockage is assessed.

[0073] The glass chip fabrication method for the microfluidic chip module is as follows:

[0074] First, the glass substrate, as the base material, is ultrasonically cleaned with alcohol and acetone for 15 minutes, then thoroughly rinsed with deionized water, and finally dried at 50°C for later use. This step ensures that the substrate surface is clean and free of contamination, providing a good foundation for subsequent processes.

[0075] Second, a laser confocal scanning system is used to acquire real core pore structure data with a scanning resolution at the micrometer level. The bitmap obtained from the scan is then converted into vector graphics using vectorization software, providing accurate graphic data for subsequent etching processes.

[0076] Third, SU-8 photoresist was used for coating. A spin coater was used to rotate the coating at 4500 rpm for 10 minutes to ensure a uniform coating layer. Ultraviolet exposure was then performed with an exposure energy controlled at 200 mJ / cm². After exposure, development was performed for 60 seconds.

[0077] Fourth, wet chemical etching is performed using hydrofluoric acid buffer solution, with the etching time controlled at 30 minutes and the etching depth maintained within the range of 50-100 μm. This process can accurately replicate the pore structure of the core, ensuring the authenticity of the model.

[0078] Fifth, select a glass cover plate, evenly coat the surface with UV-curable adhesive, press it firmly with the etched substrate, and irradiate it with a UV lamp for 1 hour to complete the curing; use a 1mm diameter glass drill bit to drill holes at the injection end and the extraction end to complete the chip fabrication.

[0079] The equipment used in the experimental system is as follows:

[0080] The fluorescence microscope in the microscopic imaging module is equipped with a DAPI / FITC / TRITC filter group, with a frame rate of up to 30fps. The CCD camera has a resolution of no less than 2048×2048 pixels and supports multi-band fluorescence imaging to distinguish four-phase fluids of oil, water, polymers and particles.

[0081] The system employs a dual-channel high-precision syringe pump with a flow rate range of 0.1-1000 μL / min and a flow control accuracy of ±0.5%. It is also equipped with a precision temperature control device with an accuracy of ±0.1℃, ensuring stable experimental conditions.

[0082] Equipped with independently developed residual oil quantitative analysis software, it features image segmentation and quantitative analysis capabilities. The software boasts real-time processing speed and micron-level analysis accuracy, enabling automatic identification and quantification of various residual oil distribution types.

[0083] The fluid configuration for the experimental system is as follows:

[0084] The binary composite displacement solution was prepared by dissolving two solutions in deionized water: a 1000 mg / L HPAM polymer solution with a molecular weight of 25 million and a 0.2% petroleum sulfonate solution. The solutions were thoroughly dissolved and mixed uniformly using a stirrer, with the temperature controlled at 20-30℃ and the pH value kept neutral.

[0085] The kaolinite suspension was prepared by selecting high-purity kaolin ore, which was dried and ground to prepare a 30% concentration suspension. An ultrasonic processor was used to ensure full dispersion of the kaolin, the pH was adjusted to 4, and finally, large particulate impurities were removed by filtration.

[0086] The crude oil sample selected had a density of 0.8626 g / cm³ and a kinematic viscosity of 18.65 mm² / s. The crude oil was preheated to the experimental temperature before the experiment to ensure its fluidity met the experimental requirements.

[0087] The experimental procedure is as follows:

[0088] Chip pretreatment: The prepared microfluidic chip is mounted in a dedicated holder and subjected to vacuum treatment for 30 minutes to ensure complete saturation of the pores. The chip's sealing and light transmittance are checked to ensure that the experimental conditions meet the requirements.

[0089] Fluid saturation: Inject the prepared kaolinite suspension from the inlet to fully saturate the pore space. If necessary, crude oil can be injected to prepare a mixture, which is then allowed to stand at a constant temperature to reach a stable state.

[0090] Displacement experiment: The experimental temperature was set at a constant 46℃, and the displacement solution was injected at a constant flow rate using a syringe pump. The exhaustive method was used for displacement, and the injection volumes were set to 10PV, 20PV, and 80PV, respectively. The displacement process was recorded completely.

[0091] Real-time observation: Images are acquired in real time using a fluorescence microscope, with a full-field image captured every 30 seconds. The formation, migration, and deposition processes of flocs are recorded in detail to obtain complete experimental data.

[0092] The above data analysis methods

[0093] Dynamic behavior analysis of flocs: The formation, migration and deposition processes of flocs are analyzed through time-series images to obtain quantitative data such as floc size distribution, migration velocity and deposition location.

[0094] Quantitative Analysis of Residual Oil Distribution: The image processing module's residual oil quantitative analysis software uses image segmentation algorithms to identify six types of residual oil distribution: pore surface film, particle adsorption, corner, throat, cluster, and intergranular adsorption residual oil. It can also calculate the area percentage (Sj), number density (nj), and average scale (Sj) for each type of residual oil. ) and average thickness ( ), interface contact degree and type distribution ratio;

[0095] The formula for calculating the area percentage (Sj) is as follows:

[0096]

[0097]

[0098]

[0099] in: - Total area of ​​type j (μm) 2 ); - The actual area represented by a single pixel (μm) 2 ); - Residual oil mask of type j: 1 if a pixel belongs to oil of type j, 0 otherwise; -The effective pore area of ​​the entire observation area; - Solid framework / particle mask: Solid is 1, porosity is 0; - Area percentage of type j;

[0100] The formula for calculating the number density (nj) is as follows:

[0101]

[0102] in: - The number of oil droplets / spots of type j; - The set of all oil-connected domains (each C is an oil cluster); - Connected region C; - The number density per unit area reflects the density of this type of oil;

[0103] mean scale ( ) and average thickness ( The calculation formula is as follows:

[0104]

[0105]

[0106] in: -The average scale of type j; - The equivalent diameter of the connected region C: the diameter of a circle of equal area, representing the size of the oil globule; - Average thickness of type j; - Average thickness of oil spot C;

[0107] The formula for calculating interface contact degree is as follows:

[0108]

[0109]

[0110]

[0111] in: - The total contact length between the oil stain of type j and the solid wall surface; - The length of the interface between oil stain C and the solid wall / particle; - The total contact length between the oil spot of type j and the aqueous phase; -Length of the interface between oil spot C and the aqueous phase; - The proportion of oil spots of type j that are "encased" by solids; - The perimeter of oil spot C;

[0112] Formula for calculating the distribution ratio of types:

[0113]

[0114]

[0115] in: - The proportion of type j in all remaining oil; - Total area of ​​type j; - The total area of ​​residual oil of all types.

[0116] Oil displacement efficiency calculation: The oil displacement efficiency is calculated based on the changes in image grayscale values ​​using existing technology. Oil displacement efficiency curves are plotted under different injection volumes, and the variation law of displacement effect is analyzed.

[0117] Parameter optimization analysis: Through comparative analysis of multiple sets of experiments, a correlation model between process parameters and displacement effect is established to provide optimal injection parameter suggestions for field applications.

[0118] It also includes a produced fluid collection device, which is connected to the production end of the microfluidic chip module and equipped with a volume measurement sensor. This device can record the produced fluid volume in real time and transmit the data to the image processing module, which is used to correct the calculated value of oil displacement efficiency by combining the image analysis results.

Claims

1. A monitoring and identification system for oil-water multiphase flow based on microfluidics and fluorescence microscopy, including: Microfluidic chip module, fluid injection control module, microscopic imaging module, image processing module; The microfluidic chip module is a glass chip formed by laser confocal scanning and vector processing based on real core CT data and then etching it. It is used to simulate the pore structure of real cores, and its injection end is connected to the fluid injection control module. The fluid injection control module includes a high-precision injection pump and a temperature control system. The high-precision injection pump is connected to the injection end of the microfluidic chip module to inject a binary composite displacement fluid into the chip. The temperature control system acts directly on the microfluidic chip module to maintain the constant temperature environment required for the experiment. The microscopic imaging module includes a fluorescence microscope and a CCD camera. The fluorescence microscope is aimed at the pore structure region of the microfluidic chip module to acquire fluorescence images of oil, water, chemical agents and particle transport within the chip in real time, and transmits the acquired image data to the image processing module. The image processing module is a residual oil quantitative analysis software equipped with a self-developed algorithm. It establishes data interaction with the microscopic imaging module to receive image data, and realizes multiphase fluid identification and residual oil distribution quantitative analysis through the algorithm. The analysis results can be used to adjust the injection parameters of the fluid injection control module.

2. The oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy according to claim 1, characterized in that: The glass chip fabrication method for the microfluidic chip module is as follows: First, the glass substrate is used as the base material. It is ultrasonically cleaned with alcohol and acetone for 15 minutes, then thoroughly rinsed with deionized water, and finally dried at 50°C for later use. Second, a laser confocal scanning system was used to obtain real core pore structure data with a scanning resolution of micrometer level. The bitmap obtained from the scan was then converted into vector graphics using vectorization software. Third, SU-8 photoresist was used for coating. A spin coater was used to rotate the coating at 4500 rpm for 10 minutes to ensure a uniform coating layer. Ultraviolet exposure was then performed with an exposure energy controlled at 200 mJ / cm². After exposure, development was performed for 60 seconds. Fourth, wet chemical etching is performed using hydrofluoric acid buffer solution, with the etching time controlled at 30 minutes and the etching depth maintained within the range of 50-100 μm; Fifth, select a glass cover plate, evenly coat the surface with UV-curable adhesive, press it firmly with the etched substrate, and irradiate it with a UV lamp for 1 hour to complete the curing; use a 1mm diameter glass drill bit to drill holes at the injection end and the extraction end to complete the chip fabrication.

3. The oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy according to claim 2, characterized in that: The binary composite displacement solution was prepared by dissolving two solutions in deionized water: a 1000 mg / L HPAM polymer solution with a molecular weight of 25 million and a 0.2% petroleum sulfonate solution. The solutions were then thoroughly dissolved and mixed using a stirrer, with the temperature controlled at 20-30°C and the pH value kept neutral.

4. The oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy according to claim 2, characterized in that: The image processing module's residual oil quantitative analysis software can identify six types of residual oil distribution: pore surface film, particulate adsorption, corner, throat, cluster, and intergranular adsorption residual oil. It can also calculate the area percentage (Sj), number density (nj), and average size (Sj) of each type of residual oil. ) and average thickness ( ), interface contact degree, and type distribution ratio.

5. The oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy according to claim 4, characterized in that: The formula for calculating the area percentage (Sj) is as follows: in: - Total area of ​​type j (μm) 2 ); - The actual area represented by a single pixel (μm) 2 ); - Residual oil mask of type j: 1 if a pixel belongs to oil of type j, 0 otherwise; -The effective pore area of ​​the entire observation area; - Solid framework / particle mask: Solid is 1, porosity is 0; - Area percentage of type j; The formula for calculating the number density (nj) is as follows: in: - The number of oil droplets / spots of type j; - The set of all oil-connected domains (each C is an oil cluster); - Connected region C; - The number density per unit area reflects the density of this type of oil; mean scale ( ) and average thickness ( The calculation formula is as follows: in: -The average scale of type j; - The equivalent diameter of the connected region C: the diameter of a circle of equal area, representing the size of the oil globule; - Average thickness of type j; - Average thickness of oil spot C; The formula for calculating interface contact degree is as follows: in: - The total contact length between the oil stain of type j and the solid wall surface; - The length of the interface between oil stain C and the solid wall / particle; - The total contact length between the oil spot of type j and the aqueous phase; -Length of the interface between oil spot C and the aqueous phase; - The proportion of oil spots of type j that are "encased" by solids; - The perimeter of oil spot C; Formula for calculating the distribution ratio of types: in: - The proportion of type j in all remaining oil; - Total area of ​​type j; - The total area of ​​residual oil of all types.

6. The oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy according to claim 1, characterized in that: It also includes a produced fluid collection device, which is connected to the production end of the microfluidic chip module and equipped with a volume measurement sensor. This device can record the produced fluid volume in real time and transmit the data to the image processing module, which is used to correct the calculated value of oil displacement efficiency by combining the image analysis results.

7. The oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy according to claim 6, characterized in that: The fluid injected by the fluid injection control module also includes kaolinite suspension and crude oil. The kaolinite suspension has a concentration of 30% and a pH value of 4. The crude oil has a density of 0.8626 g / cm³ and a kinematic viscosity of 18.65 mm² / s. The crude oil needs to be preheated to the experimental set temperature before injection.

8. The oil-water multiphase flow monitoring and identification system based on microfluidics and fluorescence microscopy according to claim 1, characterized in that: The fluorescence microscope of the microscopic imaging module is equipped with a DAPI / FITC / TRITC filter group, and the CCD camera has a resolution of no less than 2048×2048 pixels, supporting multi-band fluorescence imaging to distinguish four-phase fluids of oil, water, polymer and particulate matter.

Images