Intelligent foam fracturing fluid drag reduction performance test system and method based on Internet of Things

Abstract

The invention relates to the technical field of oil exploitation, in particular to an intelligent foam fracturing fluid drag reduction performance testing system and method based on the Internet of Things, and the system comprises a multi-parameter fluid monitoring module, a foam structure evolution analysis module, a flow pressure dynamic regulation and control module, a foam stability dynamic evaluation module and a drag reduction performance depth calculation module. According to the invention, by synchronously monitoring various fluid characteristics of the foam fracturing fluid, the innovative scheme can accurately capture state changes of foam under different working conditions, so that not only is the ability to analyze dynamic characteristics of a foam microstructure enhanced, but also pressure regulation and control based on real-time data are realized, and then the flow adaptability and stability of the foam are optimized; by comprehensively analyzing the form change, the flow shear resistance condition and the fracture rate of the foam, the drag reduction performance of the foam can be accurately evaluated and improved, and efficient application of the foam fracturing fluid in a complex oil reservoir environment is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of oil extraction, and in particular to an intelligent foam fracturing fluid drag reduction performance testing system and method based on the Internet of Things. Background Art

[0002] The field of oil extraction technology includes many aspects such as exploration, drilling, production, storage and transportation of oil and gas resources. The core content of this technical field is to find and exploit underground oil and gas resources through geological exploration and drilling technology, while adopting various recovery technologies to improve oil and gas recovery rates. During the drilling process, in order to reduce friction resistance and improve operational efficiency, fluid technologies such as foam fracturing fluid are usually required. Foam fracturing fluid is a composite system composed of gas phase, liquid phase and surfactant, which can remain stable in high temperature and high pressure environment and provide good sand carrying and resistance reduction performance. The research focus of this technical field includes the rheological properties of foam fluids, interfacial tension control, stability improvement and adaptability to complex formation conditions, in order to improve the efficiency of oil and natural gas extraction.

[0003] Among them, the intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things refers to a system that uses Internet of Things technology to monitor and test the drag reduction performance of foam fracturing fluid in real time. The system covers technical matters such as foam fluid parameter collection, data transmission, dynamic monitoring and drag reduction performance evaluation. Specifically, the system uses hardware components such as pressure sensors, flow meters, and image acquisition equipment to obtain the changes in foam fluid under different flow rates and pressure conditions, and uses wireless communication technology to transmit the collected data to the data processing terminal. The data processing terminal calculates the drag reduction rate of the foam fracturing fluid based on the analysis model set in the experiment, and displays the test results through a visual interface. In addition, the system combines automatic control devices to dynamically adjust the foam composition and fluid state to optimize the drag reduction performance of the foam fracturing fluid.

[0004] Existing technologies mainly rely on a single sensor to collect fluid data, and are unable to achieve the simultaneous acquisition of multi-dimensional data, resulting in the difficulty of accurately monitoring the rheological properties of foam fluids under different working conditions, affecting the accurate evaluation of the foam's drag reduction performance. The analytical methods for foam microstructure are relatively limited, and can only be inferred through limited physical parameters. It is impossible to conduct real-time quantitative analysis of the evolution of foam morphology and changes in interface characteristics, resulting in limited evaluation of foam stability. The pressure regulation of existing technologies usually adopts a fixed pressure adjustment method, which fails to make adaptive adjustments based on the rheological characteristics of the foam, affecting the adaptability of the foam. For foam stability assessment, existing methods are mostly based on static experiments, which cannot reflect the actual flow conditions of foam in a dynamic environment, making it difficult to directly use experimental data for field applications. The evaluation of drag reduction performance relies on single shear stress or flow rate data, fails to comprehensively consider the complexity of the flow environment, and is difficult to ensure the applicability of foam fracturing fluids under different reservoir conditions. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose an intelligent foam fracturing fluid drag reduction performance testing system and method based on the Internet of Things.

[0006] To achieve the above objectives, the present invention adopts the following technical solutions: An intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things includes: The multi-parameter fluid monitoring module obtains the fluid properties of the foam fracturing fluid, monitors pressure changes in the pipeline, records shear stress under flow gradient, collects foam bubble size and distribution, measures liquid film thickness, records ambient temperature and flow rate changes, and obtains core characteristic data of the foam fluid; The foam structure evolution analysis module analyzes the foam morphology changes, calculates the liquid film thickness fluctuations, measures the force adjustment, identifies the foam burst rate, and obtains the foam structure evolution trend data based on the core characteristic data of the foam fluid; The flow pressure dynamic control module monitors the foam pressure response, adjusts the pressure output, evaluates the flow adaptability, and obtains the optimal foam flow pressure range based on the foam structure evolution trend data; The foam stability dynamic evaluation module measures the attenuation, calculates the stability duration, analyzes the flow shear resistance, evaluates the morphological change trend, and obtains the dynamic characteristic value of stability based on the optimal flow pressure range of the foam; The drag reduction performance depth calculation module measures the flow adaptability, calculates the rupture rate, analyzes the anti-decomposition ability based on the stability dynamic characteristic value, and obtains the foam drag reduction performance quantitative index.

[0007] As a further solution of the present invention, the core characteristic data of the foam fluid include fluid pressure distribution, shear stress change, bubble size range, liquid film thickness difference, ambient temperature influence, and flow velocity fluctuation characteristics; the foam structure evolution trend data include bubble morphology deformation rate, liquid film thickness stability, force adjustment amplitude, and rupture rate distribution; the foam optimal flow pressure range includes pressure response range, pressure control range, and flow adaptability coefficient; the stability dynamic characteristic value includes foam attenuation rate, stable duration, shear resistance limit, and morphology change amplitude; the foam drag reduction performance quantitative index includes flow adaptability, rupture rate distribution, and anti-decomposition strength.

[0008] As a further solution of the present invention, the multi-parameter fluid monitoring module includes a pressure change monitoring submodule, a shear stress recording submodule, a bubble characteristic acquisition submodule, and a flow rate environment measurement submodule.

[0009] The pressure change monitoring submodule obtains the internal pressure data of the pipeline, extracts the pressure change value, calculates the pressure increment per unit time, filters the fluctuation interval, calculates the pressure fluctuation range, and obtains the pressure fluctuation amplitude; The shear stress recording submodule calculates the flow gradient based on the pressure fluctuation amplitude, uses rheological parameters to calculate the shear stress at different flow rates, extracts the shear stress change trend, screens the time period where the change rate exceeds a specific threshold, calculates the shear stress change rate, analyzes the coupling relationship between the flow gradient and the shear stress, and obtains the shear stress change result; The bubble characteristics acquisition submodule identifies the key flow stage based on the shear stress change results, collects foam bubble size and distribution data, calculates the bubble volume ratio, and calculates the size distribution uniformity to obtain the bubble size uniformity; The flow rate environment measurement submodule detects the flow rate and temperature data based on the bubble size uniformity, calculates the flow rate fluctuation intensity, screens the flow rate change trend within the temperature range, and obtains the core characteristic data of the foam fluid.

[0010] As a further solution of the present invention, the shear stress change rate calculation formula is specifically: ; in, represents the rate of change of shear stress, Representatives arrive The summation symbol, Represents the current moment With the previous moment shear stress The absolute difference between represents the variance of turbulence intensity, where Representative The turbulence intensity at a given moment, represents the average value of turbulence intensity, Represents other parameters related to shear stress changes The sum of .

[0011] As a further solution of the present invention, the foam structure evolution analysis module includes a foam morphology analysis submodule, a liquid film thickness measurement submodule, and a foam force calculation submodule.

[0012] The foam morphology analysis submodule extracts foam morphology parameters based on the core characteristic data of the foam fluid, calculates the bubble gap distribution, and calculates the morphology change trend to obtain the foam morphology evolution rate; The liquid film thickness measurement submodule obtains liquid film thickness data based on the foam morphology evolution rate, calculates thickness change values ​​in a time series, screens intervals where the fluctuation amplitude exceeds a threshold, analyzes the liquid film variation trend, analyzes the thickness adjustment characteristics under different bubble structures, and obtains the liquid film thickness fluctuation amplitude; The foam force calculation submodule obtains foam force data based on the liquid film thickness fluctuation amplitude, calculates the force adjustment amplitude, identifies the force change trend, calculates the foam bursting rate, and obtains foam structure evolution trend data.

[0013] As a further solution of the present invention, the thickness change value calculation formula is specifically: ; in, Represents the thickness change value, represents the thickness of the liquid film at the previous time point, Represents the number of measurement points of liquid film thickness in the total observation period, Indicates the average liquid film thickness change during the observation period.

[0014] As a further solution of the present invention, the flow pressure dynamic control module includes a pressure response monitoring submodule, a pressure adjustment control submodule, and a flow adaptation evaluation submodule.

[0015] The pressure response monitoring submodule extracts the foam internal pressure value based on the foam structure evolution trend data, calculates the pressure change rate, screens the abnormal fluctuation interval, and obtains the foam pressure response coefficient; The pressure adjustment control submodule obtains the current pressure output value based on the foam pressure response coefficient, calculates the adjustment amplitude in the time series, screens the over-threshold interval, extracts the pressure correction trend, calculates the degree of adaptation of the foam morphology change to the pressure, and optimizes the pressure distribution interval based on the foam burst rate and fluid stability data to obtain the foam pressure adjustment amplitude; The flow adaptation evaluation submodule calculates the flow adaptability of the foam under different pressures based on the foam pressure adjustment amplitude, screens the stable interval, extracts the optimal flow pressure range, and obtains the optimal flow pressure interval of the foam.

[0016] As a further solution of the present invention, the foam stability dynamic evaluation module includes an attenuation characteristic determination submodule, a stability and persistence calculation submodule, and a flow shear resistance analysis submodule.

[0017] The attenuation characteristic determination submodule obtains the foam attenuation rate based on the foam optimal flow pressure range, calculates the attenuation trend value in the time series, screens the fluctuation range, and obtains the foam attenuation characteristic coefficient; The stability and duration calculation submodule obtains foam duration data based on the foam attenuation characteristic coefficient, calculates the change value of the stable duration, screens the fluctuation interval exceeding the threshold value, extracts the stable trend, calculates the flow stability retention rate based on the foam structure characteristics, evaluates the impact of pressure adjustment on the foam life, and obtains the foam stability duration; The flow shear resistance analysis submodule calculates the shear resistance change at different flow rates based on the foam stability duration, screens the adaptation interval, extracts the morphological change trend, and obtains the dynamic characteristic value of stability.

[0018] As a further solution of the present invention, the drag reduction performance depth calculation module includes a flow adaptation measurement submodule, a foam burst calculation submodule, and an anti-decomposition ability analysis submodule.

[0019] The flow adaptation determination submodule extracts foam flow adaptation parameters based on the stability dynamic characteristic value, calculates the adaptation ratio under different flow rates, screens the stable interval, and obtains the foam flow adaptability; The foam burst calculation submodule obtains foam burst rate data based on the foam flow adaptability, calculates the burst frequency in the time series, screens the burst rate exceeding the threshold interval, extracts the burst trend, analyzes the burst fluctuation range under different pressure conditions, and combines the foam duration and flow stability parameters to evaluate the foam burst change under shear action to obtain the foam burst rate; The anti-decomposition capability analysis submodule calculates the decomposition resistance value of the foam under different fluid environments based on the foam rupture rate, extracts the anti-decomposition variation trend, and obtains the foam drag reduction performance quantitative index.

[0020] A method for testing drag reduction performance of intelligent foam fracturing fluid based on the Internet of Things, comprising the following steps: S1: Obtain the fluid properties of the foam fracturing fluid, monitor the pressure changes in the pipeline, record the shear stress under the flow gradient, collect the size and distribution of foam bubbles, measure the liquid film thickness, and obtain the core characteristic data of the foam fluid; S2: Based on the core characteristic data of the foam fluid, analyze the foam morphology change, calculate the liquid film thickness fluctuation, measure the force adjustment, and obtain the foam structure evolution trend data; S3: Based on the foam structure evolution trend data, monitoring the foam pressure response, adjusting the pressure output, and obtaining the optimal foam flow pressure range; S4: Based on the optimal flow pressure range of the foam, measuring the attenuation, calculating the stability duration, analyzing the flow shear resistance, and obtaining the dynamic characteristic value of stability; S5: Based on the dynamic characteristic value of stability, the flow adaptability is measured, the rupture rate is calculated, and a quantitative index of the foam drag reduction performance is obtained.

[0021] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by synchronously monitoring the various fluid properties of the foam fracturing fluid, this innovative solution can accurately capture the state changes of the foam under different working conditions. It not only enhances the ability to analyze the dynamic characteristics of the foam microstructure, but also realizes pressure regulation based on real-time data, thereby optimizing the flow adaptability and stability of the foam. By comprehensively analyzing the morphological changes, flow shear resistance and rupture rate of the foam, it can accurately evaluate and improve the drag reduction performance of the foam, ensuring the efficient application of foam fracturing fluid in complex reservoir environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a system flow chart of the present invention; Figure 2 It is a submodule flow chart of the present invention; Figure 3 This is a flow chart of the multi-parameter fluid monitoring module of the present invention; Figure 4 This is a flow chart of the foam structure evolution analysis module of the present invention; Figure 5 This is a flow chart of the flow pressure dynamic control module of the present invention; Figure 6 This is a flow chart of the foam stability dynamic evaluation module of the present invention; Figure 7 This is a flow chart of the drag reduction performance depth calculation module of the present invention; Figure 8 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0025] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0026] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0027] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0028] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0029] See also Figure 1 , an intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things includes: The multi-parameter fluid monitoring module obtains the fluid properties of the foam fracturing fluid, monitors pressure changes in the pipeline, records shear stress under flow gradient, collects foam bubble size and distribution, measures liquid film thickness, records ambient temperature and flow rate changes, and obtains core characteristic data of the foam fluid; The foam structure evolution analysis module analyzes foam morphology changes, calculates liquid film thickness fluctuations, measures force adjustment, identifies foam rupture rates, and obtains foam structure evolution trend data based on the core characteristic data of the foam fluid. The flow pressure dynamic control module monitors the foam pressure response, adjusts the pressure output, evaluates the flow adaptation, and obtains the optimal foam flow pressure range based on the foam structure evolution trend data; The foam stability dynamic assessment module measures the foam attenuation, calculates the stability duration, analyzes the flow shear resistance, evaluates the morphological change trend, and obtains the dynamic characteristic value of stability based on the optimal foam flow pressure range; The drag reduction performance in-depth calculation module measures the flow adaptability, calculates the rupture rate, analyzes the anti-decomposition ability based on the dynamic characteristic value of stability, and obtains the quantitative index of the foam drag reduction performance.

[0030] The core characteristic data of foam fluid include fluid pressure distribution, shear stress change, bubble size range, liquid film thickness difference, ambient temperature influence, and flow velocity fluctuation characteristics. The foam structure evolution trend data include bubble morphology deformation rate, liquid film thickness stability, force adjustment amplitude, and rupture rate distribution. The optimal flow pressure range of foam includes pressure response range, pressure control range, and flow adaptability coefficient. The dynamic characteristic values ​​of stability include foam attenuation rate, stable duration, shear resistance limit, and morphology change amplitude. The quantitative index of foam drag reduction performance includes flow adaptability, rupture rate distribution, and anti-decomposition strength.

[0031] See also Figure 3 and Figure 2 ,The multi-parameter fluid monitoring module includes a pressure change monitoring ,submodule, a shear stress recording submodule, a bubble characteristic acquisition ,submodule, and a flow velocity environment measurement ,submodule.

[0032] The pressure change monitoring submodule obtains the internal pressure data of the pipeline, extracts the pressure change value, calculates the pressure increment per unit time, filters the fluctuation interval, calculates the pressure fluctuation range, and obtains the pressure fluctuation amplitude; First, the pressure value inside the pipeline is collected by the sensor at different time points , the sampling frequency is set to , to ensure data accuracy, then calculate the pressure increment between adjacent time points, i.e. , and then for continuous Time window for cumulative calculation of pressure changes , determine the pressure increment per unit time, then filter all the acquired incremental data, first exclude the error exceeding the set threshold The abnormal data is removed so that it does not affect the overall analysis. Subsequently, the extreme value search is performed on the remaining data, and the pressure fluctuation range is identified by detecting the local maximum and minimum values, where the local extreme value points meet and or and , the selected extreme points are used as the boundaries of the fluctuation range, and then the maximum pressure change amplitude within the range is calculated, that is, ,like Greater than the set threshold , then it is determined that there is effective pressure fluctuation in this interval. Assume that the pressure change of a certain pipeline is collected within 10s and the data points are obtained. , calculate the maximum pressure fluctuation amplitude If the threshold is set , then the fluctuation is valid, and the final pressure fluctuation amplitude is 1.2MPa.

[0033] The shear stress recording submodule calculates the flow gradient based on the pressure fluctuation amplitude, uses rheological parameters to calculate the shear stress at different flow rates, extracts the shear stress change trend, screens the time period where the change rate exceeds a specific threshold, calculates the shear stress change rate, analyzes the coupling relationship between the flow gradient and shear stress, and obtains the shear stress change results; The specific calculation formula for shear stress change rate is: ; in, represents the rate of change of shear stress, Representatives arrive The summation symbol, Represents the current moment With the previous moment shear stress The absolute difference between represents the variance of turbulence intensity, where Representative The turbulence intensity at a given moment, represents the average value of turbulence intensity, Represents other parameters related to shear stress changes The sum of .

[0034] Shear stress change rate Used to describe the magnitude of shear stress changes over time, which is related to the fluctuation of flow gradient and pressure level. Through experimental determination, high-frequency measuring equipment is usually used to record the shear stress at different times. The shear stress change is calculated as the sum of the absolute changes in each time interval, that is: ; in, For the The shear stress at each time point, is the shear stress at the previous time point, The total number of time points collected for shear stress. Flow gradient It represents the rate of change of flow measured at different locations and can be obtained by measuring the difference in flow velocity: ; in, For the fluid The flow rate at each location, is the distance to the corresponding position, is the number of flow gradient measurement points, Represents the mean of the flow gradient: ; The flow gradient fluctuation term is calculated as the sum of squares of the deviations of the flow gradient at each measurement point relative to the mean: ; Pressure data Measured by a pressure sensor, it represents the pressure values ​​obtained at different time points. is the number of pressure measurement points, the summation term Indicates the total pressure value at multiple time points.

[0035] Shear stress data was determined as follows: ; Cumulative shear stress change: ; ; Flow gradient data: ; Mean calculation: ; Fluctuation calculation: ; ; ; Pressure measurement data: ; Total pressure: ; Final calculation: ; The results showed that the shear stress change rate was 0.233Pa -1 The higher the value, the more severe the shear stress fluctuation. Combined with this data, we can further analyze the trend of shear stress changing with flow state.

[0036] The bubble characteristics acquisition submodule identifies the key flow stages based on the shear stress change results, collects foam bubble size and distribution data, calculates the bubble volume ratio, and calculates the size distribution uniformity to obtain the bubble size uniformity; First, identify the key flow stage and define the key point of the flow stage as the point where the shear stress changes drastically, that is, the point where the shear stress changes drastically on the time axis. Then, the size distribution data of foam bubbles are collected at this stage, and the bubble size is obtained by image analysis or laser scattering method. , calculate the bubble volume ratio ,in is the total volume of bubbles, is the total volume of the fluid, then the uniformity of the size distribution is calculated and the uniformity index is defined ,in is the standard deviation of bubble size, is the average size of bubbles. If the bubble size data detected in an experiment is , then calculate the average size , standard deviation , uniformity , and finally the bubble size uniformity was 0.052.

[0037] The flow rate environment measurement submodule detects flow rate and temperature data based on bubble size uniformity, calculates flow rate fluctuation intensity, screens flow rate change trends within the temperature range, and obtains core characteristic data of foam fluid; First, obtain the flow rate data and temperature data , screening temperature range Calculate the velocity fluctuation intensity based on the velocity data within ,in is the standard deviation of flow rate, is the average flow velocity. If the measured flow velocity data ,but , , velocity fluctuation intensity , then filter the The velocity variation trend of the foam fluid is finally obtained.

[0038] See also Figure 4 and Figure 2 The foam structure evolution analysis module includes a foam morphology analysis submodule, a liquid film thickness measurement submodule, and a foam force calculation submodule.

[0039] The foam morphology analysis submodule extracts foam morphology parameters based on the core characteristic data of the foam fluid, calculates the bubble gap distribution, and calculates the morphology change trend to obtain the foam morphology evolution rate; First, the bubble size in the foam structure is collected , morphological factors and foam distribution density , the imaging analysis technology is used to obtain the overall contour of the foam and extract the bubble morphology information in different areas. Then, the bubble gap distribution is calculated and the bubble gap is defined. is the shortest distance between adjacent bubbles, that is, for each bubble, the gap can be expressed as ,in is the center coordinate of the bubble. Then, all bubble gap data are counted to calculate the average gap and standard deviation , if the bubble gap is measured in a foam sample , then the average gap , standard deviation Then, the morphological change trend is statistically analyzed and the rate of change of foam morphological parameters over time is calculated, which is defined as the foam morphological evolution rate. ,in is the morphological factor. If the morphological factor changes from becomes , the time interval is ,but , and finally the foam morphology evolution rate is obtained .

[0040] The liquid film thickness measurement submodule obtains liquid film thickness data based on the foam morphology evolution rate, calculates the thickness change value in the time series, screens the interval where the fluctuation amplitude exceeds the threshold, analyzes the liquid film change trend, analyzes the thickness adjustment characteristics under different bubble structures, and obtains the liquid film thickness fluctuation amplitude; The calculation formula for thickness change is: ; in, Represents the thickness change value, represents the thickness of the liquid film at the previous time point, Represents the number of measurement points of liquid film thickness in the total observation period, Indicates the average liquid film thickness change during the observation period.

[0041] Average value of liquid film thickness change The thickness of the liquid film is obtained by calculating the sum of the absolute changes in thickness between consecutive time points and dividing it by the total number of time points. This formula reflects the average change in liquid film thickness over a given time period and is an important parameter for monitoring liquid film stability and dynamic change characteristics.

[0042] This formula is calculated using a specific example, based on actual measured liquid film thickness data. This data should be obtained using specialized equipment such as a laser rangefinder or micrometer under strictly controlled experimental conditions. The following is the liquid film thickness data collected in the experiment: Time point 1: ; Time point 2: ; Time point 3: ; Time point 4: ; Calculation process: Calculate the absolute change in film thickness between consecutive time points: ; ; ; Calculate the total change in film thickness: ; Calculate the average change in film thickness: (number of time points); ; The results indicate that the average thickness variation of the liquid film within a given measurement cycle was 0.027 mm. This data demonstrates that the liquid film exhibits slight thickness variations during continuous measurement, providing a quantitative assessment of the film's stability. This method allows for accurate tracking of liquid film performance during experiments or production to ensure product quality or further process optimization.

[0043] The foam force calculation submodule obtains foam force data based on the fluctuation amplitude of the liquid film thickness, calculates the force adjustment amplitude, identifies the force change trend, calculates the foam burst rate, and obtains the foam structure evolution trend data; First, define the foam forces The external force acting on the foam per unit volume is measured using a micro pressure sensor to measure the pressure at different points inside the foam. , calculate the shear force on the foam ,in is the foam viscosity, is the flow velocity, is the vertical height, and then the force adjustment amplitude is calculated, that is, , filter out the force changes that exceed the set threshold time interval, if the force data of a foam at different time points is , calculate the force adjustment range If the threshold is set , then filter out (i.e. from arrive Then, the stress change trend is identified and the foam burst rate is calculated. ,in is the number of burst bubbles, is the time interval. If 100 bubbles burst within 10s, the burst rate is / s, and finally the foam structure evolution trend data is obtained.

[0044] See also Figure 5 and Figure 2 ,The flow pressure dynamic control module includes a pressure response monitoring submodule, a ,pressure adjustment control submodule, and a flow adaptation evaluation submodule.

[0045] The pressure response monitoring submodule extracts the foam internal pressure value based on the foam structure evolution trend data, calculates the pressure change rate, screens the abnormal fluctuation range, and obtains the foam pressure response coefficient; First, the internal pressure of the foam at different time points is selected Data, using pressure sensors to evenly distribute multiple measuring points inside the foam structure, obtain the instantaneous pressure values ​​of multiple measuring points, and record the time series , then calculate the pressure change rate ,in represents the pressure change at adjacent time points, is the corresponding time interval, assuming that the pressure data of a foam measured at three time points is , time interval , then the pressure change rate ,Then, the pressure change rate in all time windows is screened and the abnormal fluctuation threshold is set , find satisfaction time period, assuming , then filter out The time period is used as the abnormal fluctuation interval, and the pressure response coefficient is finally calculated ,in is the foam morphology evolution rate, assuming that the foam morphology evolution rate is ,but , and finally obtain the foam pressure response coefficient .

[0046] The pressure adjustment control submodule obtains the current pressure output value based on the foam pressure response coefficient, calculates the adjustment range in the time series, screens the over-threshold interval, extracts the pressure correction trend, calculates the degree of foam morphological change to pressure adaptation, combines the foam burst rate and fluid stability data, optimizes the pressure distribution range, and obtains the foam pressure adjustment range; First, collect the current pressure data inside the foam And record the time points, then calculate the pressure adjustment amplitude under the time series, that is, , assuming that the pressure of a foam is adjusted from 2.0MPa to 2.3MPa within 5s, the adjustment range is , then, filter the adjustment range and set the threshold , find satisfaction The interval, assuming the threshold , then the interval is screened as the super-threshold interval, and then the pressure correction trend is extracted to calculate the degree of foam morphology change to pressure adaptation, that is, the pressure adaptation is defined as , assuming the pressure response coefficient , adjustment range ,but , then, combined with the foam collapse rate and fluid stability data , optimize the pressure distribution range, that is, calculate the upper limit of the stable pressure range and lower limit satisfy , ,in is the adjustment coefficient, assuming , , , pcs / s, then , , and finally the foam pressure adjustment range is 0.8MPa.

[0047] The flow adaptation evaluation submodule calculates the flow adaptability of the foam under different pressures based on the foam pressure adjustment amplitude, screens the stable interval, extracts the optimal flow pressure range, and obtains the optimal flow pressure range of the foam; First, define the foam flow adaptability To calculate the morphological stability of foam under different pressure conditions, different pressure levels Corresponding foam morphology change rate ,Right now ,in is the change in morphological factor, is the pressure change, assuming that the morphological factor of a foam changes from 2.0MPa to 2.8MPa: , then calculate , then, screen the stable interval and set the fitness threshold , find satisfaction The interval, assuming , then calculate ,like ,but , meeting the fitness threshold, and finally extracting the optimal flow pressure range and calculating the optimal pressure interval satisfy , and finally the optimal foam flow pressure range is obtained [2.0,2.8]MPa.

[0048] See also Figure 6 and Figure 2 The foam stability dynamic evaluation module includes an attenuation characteristic determination submodule, a stability and continuity calculation submodule, and a flow and shear resistance analysis submodule.

[0049] The attenuation characteristic determination submodule obtains the foam attenuation rate based on the optimal foam flow pressure range, calculates the attenuation trend value in the time series, screens the fluctuation range, and obtains the foam attenuation characteristic coefficient; First, select the foam volume at different time points , measuring time interval , calculate the foam volume decay rate ,in is the foam volume at the current time point, is the foam volume at the previous time point. If the foam volume decreases from 500 cm3 to 450 cm3 within 10 s, the foam decay rate is , then calculate the decay trend value in the time series, that is, define the decay trend change rate , if the decay rate in the previous time period is -4cm3 / s and the current time period is -5cm3 / s, then , then, filter the decay trend values ​​in all time windows and set the fluctuation range threshold , filter to meet time period, assuming the threshold , then this period belongs to the fluctuation range, and the foam attenuation characteristic coefficient is finally calculated ,in is the average value of the optimal foam flow pressure range, assuming ,but , and finally obtain the foam attenuation characteristic coefficient .

[0050] The stability and duration calculation submodule obtains foam duration data based on the foam attenuation characteristic coefficient, calculates the change value of the stable duration, screens the super-threshold fluctuation range, extracts the stable trend, calculates the flow stability retention rate based on the foam structure characteristics, evaluates the impact of pressure adjustment on foam life, and obtains the foam stability duration; First, measure the duration of the bubble at different time points , calculate the stable duration change value , assuming that the duration of a bubble at the previous measurement point is 200s and the current measurement point is 180s, then filter the super-threshold fluctuation interval and set the time stability threshold , find satisfaction The interval, assuming the threshold , then this period belongs to the super-threshold fluctuation range, then the stable trend is extracted and the flow stability retention rate is calculated ,in is the initial bubble duration, assuming ,but , then, evaluate the effect of pressure adjustment on foam life, that is, calculate the influence factor of pressure adjustment on foam life ,in For the pressure adjustment range, if the pressure is adjusted from 2.0MPa to 2.4MPa, the adjustment range is ,but , and finally the foam stability duration was 180s.

[0051] The flow shear resistance analysis submodule calculates the shear resistance changes at different flow rates based on the foam stability duration, screens the adaptation interval, extracts the morphological change trend, and obtains the dynamic characteristic value of stability; First, measure different flow rates Foam shear stress under , calculate the shear stress change rate ,in is the change in flow rate. Assuming that the shear stress of a foam is 0.8Pa and 1.2Pa at flow rates of 1.0m / s and 1.5m / s respectively, then , then, screen the adaptation interval and set the shear resistance adaptation threshold , find satisfaction The interval, assuming the threshold , then the interval belongs to the adaptation interval, then the morphological change trend is extracted and the stability dynamic characteristic value is calculated , assuming that the foam stability duration is 180s, then Finally, the dynamic characteristic value of stability is 225s2·m -1 .

[0052] See also Figure 7 and Figure 2 ,The drag reduction performance in-depth calculation module includes a flow adaptation ,determination submodule, a foam burst calculation submodule, and an anti-decomposition capability ,analysis submodule.

[0053] The flow adaptation determination submodule extracts foam flow adaptation parameters based on the dynamic characteristic value of stability, calculates the adaptation ratio under different flow rates, screens the stable interval, and obtains the foam flow adaptability; First, measure different flow rates Stability parameters of foam , define the foam flow adaptation parameters ,in is the dynamic characteristic value of stability. Assuming that the stability parameters of a foam are 0.75 and 0.65 at flow rates of 1.2m / s and 1.8m / s respectively, the dynamic characteristic value of stability is , then the flow adaptation parameter is calculated as , then, calculate the adaptation ratio at different flow rates ,in is the interval between adjacent flow rates, if , , ,but , then, screen the adaptation ratios at all flow rates and set the stable interval threshold , find satisfaction The flow rate range, assuming , then the interval meets the adaptability conditions, and the foam flow adaptability is finally calculated , assuming that the flow rate range , , ,but , and finally obtain the foam flow adaptability .

[0054] The foam burst calculation submodule obtains foam burst rate data based on the foam flow adaptability, calculates the burst frequency in the time series, screens the burst rate exceeding the threshold interval, extracts the burst trend, analyzes the burst fluctuation range under different pressure conditions, and combines the foam duration and flow stability parameters to evaluate the foam burst changes under shear action to obtain the foam burst rate; First, we need to obtain data on the foam burst rate by evaluating the foam flow adaptability. Specifically, we first use sensors or imaging technology to monitor the foam flow process and obtain the flow characteristics of the foam under different environmental conditions, such as foam volume, shape change, bubble size and other parameters. Assuming that under a certain flow condition, the foam volume change rate is , and the average diameter of the bubbles is , these data can be obtained by real-time monitoring of the bubble behavior in the flow field. During the foam flow process, the formation and rupture of bubbles are affected by factors such as fluid mechanics, surface tension and shear force. The rupture rate of the foam will be adjusted according to the changes in these factors, thereby affecting the stability of the foam. As an example, assume that the initial burst rate of a foam sample is , through continuous measurement, we can get the number of bubbles that burst in 1 second and the corresponding foam volume change value. Next, we calculate the frequency of foam bursting based on these real-time data. Assuming that the bursting events observed in a certain period are times, the frequency of bubble burst The formula Calculate, where By this method, the frequency of foam bursting can be compared with the performance under different flow environments and pressure conditions, and the interval where the burst rate exceeds the threshold can be screened out. For example, when the frequency of foam bursting exceeds the preset threshold When , it can be considered that the foam sample is in an unstable state and the flow conditions need to be optimized or adjusted. On this basis, the rupture trend is further extracted and the fluctuation range of foam rupture under different pressure conditions is identified. and The rupture frequencies obtained are and , then the bubble burst fluctuation range can be Evaluate and calculate the range to be This fluctuation range helps to analyze the effect of different pressures on foam stability. Combining the duration of the foam with the flow stability parameters, we can finally evaluate the changes in foam rupture under shear and evaluate the shear rate. The impact on foam bursting. Assuming that the shear rate of the foam under a certain flow state is , then the foam rupture rate will increase with the increase of shear force. In this process, by analyzing the relationship between rupture frequency and shear rate, a comprehensive evaluation of the foam rupture rate can be obtained. Assuming that at high shear rate, the foam rupture rate increases to , we can conclude that the foam burst rate has a significant relationship with the shear effect. The greater the shear rate, the faster the foam burst rate, which affects the stability and life cycle of the foam. Finally, based on the above analysis, the specific value of the foam burst rate is obtained. Based on multiple factors such as flow conditions, pressure changes, and shear rate, the foam burst rate is obtained. , this result provides an important basis for subsequent foam stability optimization and control.

[0055] The anti-decomposition ability analysis submodule calculates the foam's resistance to decomposition under different fluid environments based on the foam rupture rate, extracts the trend of anti-decomposition changes, and obtains the quantitative index of foam drag reduction performance; First, the collapse time of the foam was measured in different fluid environments. , define the decomposition resistance value , assuming that the bursting time of a foam in two fluid environments is 300s and 250s respectively, and the bursting rate is 6 / s and 7 / s, then , then, extract the decomposition resistance change trend, that is, calculate the decomposition resistance change rate , assuming , , ,but , and finally calculate the quantitative index of foam drag reduction performance , assuming , ,but , and finally the quantitative index of foam drag reduction performance was obtained as -85.7s.

[0056] See also Figure 8 , a method for testing the drag reduction performance of intelligent foam fracturing fluid based on the Internet of Things, comprising the following steps: S1: Obtain the fluid properties of the foam fracturing fluid, monitor the pressure changes in the pipeline, record the shear stress under the flow gradient, collect the size and distribution of foam bubbles, measure the liquid film thickness, and obtain the core characteristic data of the foam fluid; S2: Based on the core characteristic data of the foam fluid, analyze the foam morphology changes, calculate the fluctuation of the liquid film thickness, measure the force adjustment, and obtain the foam structure evolution trend data; S3: Based on the foam structure evolution trend data, monitor the foam pressure response, adjust the pressure output, and obtain the optimal foam flow pressure range; S4: Based on the optimal flow pressure range of the foam, the attenuation is measured, the stability duration is calculated, the flow shear resistance is analyzed, and the dynamic characteristic value of stability is obtained; S5: Based on the dynamic characteristic value of stability, the flow adaptability is measured, the rupture rate is calculated, and the quantitative index of the foam drag reduction performance is obtained.

[0057] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things, characterized by: The system comprises: The multi-parameter fluid monitoring module obtains the fluid properties of the foam fracturing fluid, monitors pressure changes in the pipeline, records shear stress under flow gradient, collects foam bubble size and distribution, measures liquid film thickness, records ambient temperature and flow rate changes, and obtains core characteristic data of the foam fluid; The foam structure evolution analysis module analyzes the foam morphology changes, calculates the liquid film thickness fluctuations, measures the force adjustment, identifies the foam burst rate, and obtains the foam structure evolution trend data based on the core characteristic data of the foam fluid; The flow pressure dynamic control module monitors the foam pressure response, adjusts the pressure output, evaluates the flow adaptability, and obtains the optimal foam flow pressure range based on the foam structure evolution trend data; The foam stability dynamic evaluation module measures the attenuation, calculates the stability duration, analyzes the flow shear resistance, evaluates the morphological change trend, and obtains the dynamic characteristic value of stability based on the optimal flow pressure range of the foam; The drag reduction performance depth calculation module measures the flow adaptability, calculates the rupture rate, analyzes the anti-decomposition ability based on the stability dynamic characteristic value, and obtains the foam drag reduction performance quantitative index.

2. The intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things according to claim 1 is characterized in that: The core characteristic data of the foam fluid include fluid pressure distribution, shear stress changes, bubble size range, liquid film thickness difference, ambient temperature influence, and flow velocity fluctuation characteristics. The foam structure evolution trend data include bubble morphology deformation rate, liquid film thickness stability, force adjustment amplitude, and rupture rate distribution. The foam optimal flow pressure range includes pressure response range, pressure control range, and flow adaptability coefficient. The stability dynamic characteristic value includes foam attenuation rate, stable duration, shear resistance limit, and morphology change amplitude. The foam drag reduction performance quantitative index includes flow adaptability, rupture rate distribution, and anti-decomposition strength.

3. The intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things according to claim 1 is characterized in that: The multi-parameter fluid monitoring module includes a pressure change monitoring submodule, a shear stress recording submodule, a bubble characteristic acquisition submodule, and a flow rate environment measurement submodule; The pressure change monitoring submodule obtains the internal pressure data of the pipeline, extracts the pressure change value, calculates the pressure increment per unit time, filters the fluctuation interval, calculates the pressure fluctuation range, and obtains the pressure fluctuation amplitude; The shear stress recording submodule calculates the flow gradient based on the pressure fluctuation amplitude, uses rheological parameters to calculate the shear stress at different flow rates, extracts the shear stress change trend, screens the time period where the change rate exceeds a specific threshold, calculates the shear stress change rate, analyzes the coupling relationship between the flow gradient and the shear stress, and obtains the shear stress change result; The bubble characteristics acquisition submodule identifies the key flow stage based on the shear stress change results, collects foam bubble size and distribution data, calculates the bubble volume ratio, and calculates the size distribution uniformity to obtain the bubble size uniformity; The flow rate environment measurement submodule detects the flow rate and temperature data based on the bubble size uniformity, calculates the flow rate fluctuation intensity, screens the flow rate change trend within the temperature range, and obtains the core characteristic data of the foam fluid.

4. The intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things according to claim 3 is characterized by: The shear stress change rate calculation formula is specifically: ； in, represents the rate of change of shear stress, Representatives arrive The summation symbol, Represents the current moment With the previous moment shear stress The absolute difference between represents the variance of turbulence intensity, where Representative The turbulence intensity at a given moment, represents the average value of turbulence intensity, Represents other parameters related to shear stress changes The sum of .

5. The intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things according to claim 1 is characterized in that: The foam structure evolution analysis module includes a foam morphology analysis submodule, a liquid film thickness measurement submodule, and a foam force calculation submodule; The foam morphology analysis submodule extracts foam morphology parameters based on the core characteristic data of the foam fluid, calculates the bubble gap distribution, and calculates the morphology change trend to obtain the foam morphology evolution rate; The liquid film thickness measurement submodule obtains liquid film thickness data based on the foam morphology evolution rate, calculates thickness change values ​​in a time series, screens intervals where the fluctuation amplitude exceeds a threshold, analyzes the liquid film variation trend, analyzes the thickness adjustment characteristics under different bubble structures, and obtains the liquid film thickness fluctuation amplitude; The foam force calculation submodule obtains foam force data based on the liquid film thickness fluctuation amplitude, calculates the force adjustment amplitude, identifies the force change trend, calculates the foam bursting rate, and obtains foam structure evolution trend data.

6. The intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things according to claim 5 is characterized by: The thickness change calculation formula is specifically: ； in, Represents the thickness change value, represents the thickness of the liquid film at the previous time point, Represents the number of measurement points of liquid film thickness in the total observation period, Indicates the average liquid film thickness change during the observation period.

7. The intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things according to claim 1 is characterized in that: The flow pressure dynamic control module includes a pressure response monitoring submodule, a pressure adjustment control submodule, and a flow adaptation evaluation submodule; The pressure response monitoring submodule extracts the foam internal pressure value based on the foam structure evolution trend data, calculates the pressure change rate, screens the abnormal fluctuation interval, and obtains the foam pressure response coefficient; The pressure adjustment control submodule obtains the current pressure output value based on the foam pressure response coefficient, calculates the adjustment amplitude in the time series, screens the over-threshold interval, extracts the pressure correction trend, calculates the degree of adaptation of the foam morphology change to the pressure, and optimizes the pressure distribution interval based on the foam burst rate and fluid stability data to obtain the foam pressure adjustment amplitude; The flow adaptation evaluation submodule calculates the flow adaptability of the foam under different pressures based on the foam pressure adjustment amplitude, screens the stable interval, extracts the optimal flow pressure range, and obtains the optimal flow pressure interval of the foam.

8. The intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things according to claim 1 is characterized in that: The foam stability dynamic evaluation module includes an attenuation characteristic determination submodule, a stability and continuity calculation submodule, and a flow and shear resistance analysis submodule. The attenuation characteristic determination submodule obtains the foam attenuation rate based on the foam optimal flow pressure range, calculates the attenuation trend value in the time series, screens the fluctuation range, and obtains the foam attenuation characteristic coefficient; The stability and duration calculation submodule obtains foam duration data based on the foam attenuation characteristic coefficient, calculates the change value of the stable duration, screens the fluctuation interval exceeding the threshold value, extracts the stable trend, calculates the flow stability retention rate based on the foam structure characteristics, evaluates the impact of pressure adjustment on the foam life, and obtains the foam stability duration; The flow shear resistance analysis submodule calculates the shear resistance change at different flow rates based on the foam stability duration, screens the adaptation interval, extracts the morphological change trend, and obtains the dynamic characteristic value of stability.

9. The intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things according to claim 1, characterized in that: The drag reduction performance depth calculation module includes a flow adaptation determination submodule, a foam burst calculation submodule, and an anti-decomposition ability analysis submodule. The flow adaptation determination submodule extracts foam flow adaptation parameters based on the stability dynamic characteristic value, calculates the adaptation ratio under different flow rates, screens the stable interval, and obtains the foam flow adaptability; The foam burst calculation submodule obtains foam burst rate data based on the foam flow adaptability, calculates the burst frequency in the time series, screens the burst rate exceeding the threshold interval, extracts the burst trend, analyzes the burst fluctuation range under different pressure conditions, and combines the foam duration and flow stability parameters to evaluate the foam burst change under shear action to obtain the foam burst rate; The anti-decomposition capability analysis submodule calculates the decomposition resistance value of the foam under different fluid environments based on the foam rupture rate, extracts the anti-decomposition variation trend, and obtains the foam drag reduction performance quantitative index.

10. A method for testing drag reduction performance of intelligent foam fracturing fluid based on the Internet of Things, characterized in that: The intelligent foam fracturing fluid drag reduction performance testing system based on the Internet of Things according to any one of claims 1 to 9 comprises the following steps: S1: Obtain the fluid properties of the foam fracturing fluid, monitor the pressure changes in the pipeline, record the shear stress under the flow gradient, collect the size and distribution of foam bubbles, measure the liquid film thickness, and obtain the core characteristic data of the foam fluid; S2: Based on the core characteristic data of the foam fluid, analyze the foam morphology change, calculate the liquid film thickness fluctuation, measure the force adjustment, and obtain the foam structure evolution trend data; S3: Based on the foam structure evolution trend data, monitoring the foam pressure response, adjusting the pressure output, and obtaining the optimal foam flow pressure range; S4: Based on the optimal flow pressure range of the foam, measuring the attenuation, calculating the stability duration, analyzing the flow shear resistance, and obtaining the dynamic characteristic value of stability; S5: Based on the dynamic characteristic value of stability, the flow adaptability is measured, the rupture rate is calculated, and a quantitative index of the foam drag reduction performance is obtained.

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