A method and system for determining the optimal blending ratio of a binary composite oil displacement system

Abstract

This application relates to the field of intelligent sensor technology, specifically to a method and system for determining the optimal compound ratio of a binary composite oil displacement system. The method includes: conducting an emulsification experiment on a binary composite oil displacement system of octylphenol polyoxyethylene ether and polyacrylamide; analyzing the degree and difference in peak temperature changes at the upper and lower bottom of the oven wall collected by intelligent sensors to obtain the local peak anomaly degree; and then combining this with the discreteness of the temperature difference change between the upper and lower bottom to obtain the disturbance disorder degree; obtaining high-frequency interference characteristic values ​​based on the high-frequency variation characteristics of the disturbance disorder degree in the frequency domain during emulsification; and then adjusting the proportional coefficient of the PID controller in the oven to control the temperature inside the oven; finally, determining the optimal compound ratio of the binary composite oil displacement system by the water separation rate of the binary composite oil displacement system during emulsification. This application can improve the oven temperature control accuracy during the emulsification process of the binary composite oil displacement system, thereby improving the accuracy of determining the optimal compound ratio.

Description

Technical Field

[0001] This application relates to the field of intelligent sensor technology, specifically to a method and system for determining the optimal blending ratio of a binary composite oil displacement system. Background Technology

[0002] Currently, the optimal blending ratio of binary composite oil displacement system can be quickly determined by emulsification experiments of octylphenol polyoxyethylene ether and polymer system. During the emulsification experiment, the temperature inside the oven needs to be controlled by using a PID controller (Proportion Integration Differentiation) to simulate the thermal settling process and observe the water separation rate of the binary composite oil displacement system.

[0003] However, traditional PID controllers use fixed proportional parameters to control the temperature inside the oven, which means they cannot respond in a timely manner to changes in the oven temperature due to the influence of the external environment. This results in poor accuracy in controlling the oven temperature, which can easily affect the determination of the optimal compound ratio in the subsequent binary composite oil displacement system. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for determining the optimal blending ratio of a binary composite oil displacement system. The specific technical solution adopted is as follows:

[0005] This application provides a method for determining the optimal blending ratio of a binary composite oil displacement system, including the following steps:

[0006] Emulsification experiments were conducted on a binary composite oil displacement system of octylphenol polyoxyethylene ether and polyacrylamide, and the upper and lower temperatures of the oven wall during the emulsification experiment were obtained by intelligent sensors.

[0007] The degree of change and difference of the peak temperature of the upper and lower bottom of the oven inner wall collected by the intelligent sensor are analyzed to obtain the local peak anomaly degree at each collection time. Then, the degree of interference disorder at each collection time is obtained by combining the discreteness of the temperature difference change between the upper and lower bottom.

[0008] Based on the high-frequency variation characteristics of the disturbance disorder during the emulsification process in the frequency domain, and combined with the disturbance disorder to obtain the high-frequency interference characteristic values ​​at each acquisition time, the proportional coefficient of the PID controller in the oven is adjusted by the change of the high-frequency interference characteristic values ​​to control the temperature in the oven. Then, the optimal compounding ratio of the binary composite oil displacement system is determined by the water separation rate data of the binary composite oil displacement system during the emulsification process.

[0009] Preferably, the process for obtaining the local peak anomaly degree at each acquisition time is as follows: In the formula, Let be the local peak anomaly degree at the t-th acquisition time. Let be the DTW distance between the upper and lower peak sequences at the t-th acquisition time. and denoted as the mean of the elements in the first-order difference sequence of the upper and lower peak positions, respectively, at the t-th acquisition time.

[0010] Preferably, the multiple acquisition times closest to the time interval of each acquisition time are recorded as the local acquisition times of each acquisition time. The upper and lower bottom temperatures collected by the smart sensor at each acquisition time and its local acquisition times are arranged in chronological order to form the upper and lower bottom temperature sequences of each acquisition time. The position order of all peaks in the upper and lower bottom temperature sequences is extracted and arranged in ascending order to form the upper bottom peak position sequence and the lower bottom peak position sequence of each acquisition time.

[0011] Preferably, the process for obtaining the degree of interference at each acquisition time is as follows: In the formula, Let be the degree of disturbance at the t-th acquisition time. Let be the local peak anomaly degree at the t-th acquisition time. denoted as the degree of dispersion of all elements within the temperature difference step sequence at the t-th acquisition time.

[0012] Preferably, the upper and lower temperature sequences at each acquisition time are subtracted, and the first-order difference sequence of the subtracted sequence is used as the temperature difference step sequence at each acquisition time.

[0013] Preferably, the interference disorder of each acquisition time and its local acquisition time is arranged in chronological order to obtain the interference feature sequence of each acquisition time. The amplitude spectrum of the positive frequency part is extracted by frequency domain transformation. The frequencies corresponding to all non-zero amplitude values ​​in the amplitude spectrum of the positive frequency part are used to form the local frequency set of each acquisition time.

[0014] Preferably, the local frequency set at each acquisition time is segmented by a threshold, and the frequencies in the local frequency set that are greater than the corresponding segmentation threshold are formed into a local high-frequency set at each acquisition time.

[0015] Preferably, the process of obtaining the high-frequency interference feature value at each acquisition time is as follows: the normalized result of the product between the number of elements in the local high-frequency set at each acquisition time and the degree of interference disorder is used as the high-frequency interference feature value at each acquisition time.

[0016] Preferably, the process of adjusting the proportional coefficient of the PID controller inside the oven is as follows: In the formula, This represents the expected scaling factor at the current data acquisition moment. This is the preset initial scaling factor. and These are the high-frequency interference characteristic values ​​at the current acquisition time and the previous acquisition time, respectively.

[0017] This application also provides a system for determining the optimal blending ratio of a binary composite oil displacement system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the method for determining the optimal blending ratio of a binary composite oil displacement system described above.

[0018] As can be seen from the above, the method and system for determining the optimal blending ratio of a binary composite oil displacement system provided in this application have at least the following beneficial effects:

[0019] This application conducts an in-depth study on the emulsifying ability of the nonionic surfactant octylphenol polyoxyethylene ether. Emulsifying ability is positively correlated with improved oil displacement efficiency. Oil displacement experimental data shows that the oil recovery rate can be increased by 7.95 percentage points when octylphenol polyoxyethylene ether is combined with a polymer system compared to the polymer system alone. Besides its applications in detergent removal, octylphenol polyoxyethylene ether, when compounded with a polymer, can also be widely used in oil displacement.

[0020] This application analyzes the degree of abnormality of local peak positions in the temperature changes of the upper and lower bottom of the oven wall during the emulsification experiment, and combines the step-variability characteristics of the temperature difference changes between the upper and lower bottom of the oven wall to accurately measure the abnormal and chaotic characteristics of the oven temperature caused by severe interference from the external ambient temperature. This more clearly reflects the severity of the oven temperature being affected by the external ambient temperature, which is beneficial for subsequent accurate real-time fine-tuning of the proportional parameters of the PID controller.

[0021] This application extracts the high-frequency variation components of the disturbance disorder during the emulsification process using Fourier transform, and combines this with the abnormal disorder characteristics of the unsteady state generated by the temperature inside the oven. It accurately measures the high-frequency interference characteristics of the oven temperature affected by the ambient temperature, and precisely fine-tunes the proportional parameters of the PID controller by the changes in the high-frequency interference characteristics. This allows the adjusted PID controller to respond promptly to temperature changes inside the oven under the influence of the external ambient temperature, and ensures the stability of the temperature control system, thereby avoiding affecting the emulsification effect of the binary composite oil displacement system. Attached Figure Description

[0022] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating the steps of a method for determining the optimal blending ratio of a binary composite oil displacement system provided in this application. Detailed Implementation

[0024] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and system for determining the optimal blending ratio of a binary composite oil displacement system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0025] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0026] The following, in conjunction with the accompanying drawings, details the method for determining the optimal blending ratio and the specific system scheme of the binary composite oil displacement system provided in this application.

[0027] Please see Figure 1 It illustrates a flowchart of the steps in a method for determining the optimal blending ratio of a binary composite oil displacement system according to an embodiment of this application, including the following steps:

[0028] Step 1: An emulsification experiment was conducted on the binary composite oil displacement system of octylphenol polyoxyethylene ether and polyacrylamide, and the upper and lower temperatures of the oven wall during the emulsification experiment were obtained by intelligent sensors.

[0029] In this embodiment, octylphenol polyoxyethylene ether (OXE) nonionic surfactants at concentrations of 0.05%, 0.1%, and 0.2% were compounded with a polymer concentration of 1500 mg / L. The thoroughly mixed solutions were then subjected to a hand-shaking emulsification experiment, and the water separation rate was calculated by reading the aqueous phase volume. The calculation process is existing technology and will not be elaborated in this embodiment. The emulsifying effect of different concentrations of OXE was evaluated by the water separation rate, where the polymer was polyacrylamide. The water separation rate data for different concentrations are shown in Table 1.

[0030] Table 1

[0031]

[0032] According to the table above, the experimental data from the hand-cranked emulsification experiment showed that the water separation rate was lowest when the concentration of octylphenol polyoxyethylene ether was 0.1%, therefore, the emulsification effect was the best at this concentration, making it the optimal binary composite oil displacement system.

[0033] Furthermore, emulsification experiments were conducted on the binary composite oil displacement system of octylphenol polyoxyethylene ether (0.1%) and the polymer at oil-water ratios of 1:1, 3:7, and 1:9. In this experiment, the dehydrated crude oil and the prepared system solution were placed in a 100mL stoppered graduated cylinder, vigorously shaken, and then placed in an oven to simulate a thermal settling process. The interface between the oil and the composite system was observed. In this embodiment, the preset temperature in the oven was 45°C.

[0034] To reduce the influence of ambient temperature on the oven temperature, the oven temperature needs to be accurately regulated and controlled using a PID controller (Proportion Integration Differentiation) during the emulsification experiment to avoid affecting the determination of the optimal blending ratio in the subsequent binary composite oil displacement system.

[0035] To regulate and control the temperature inside the oven using a PID controller, a smart sensor is needed to monitor the upper and lower temperatures of the oven's inner wall in real time. The smart sensor includes a sensing unit, a microprocessor unit, and a communication unit. The sensing unit uses a thermocouple temperature sensor to sense the temperature of the upper and lower bottom of the oven's inner wall. The microprocessor unit uses a Savitzky-Golay filtering algorithm to filter the upper and lower temperature data. The communication unit transmits the filtered upper and lower temperature data to the data storage unit in real time via a wireless Bluetooth module for further data storage and analysis.

[0036] Therefore, the upper and lower temperature data of the inner wall of the oven during the emulsification experiment are collected by the intelligent sensor. In this embodiment, the intelligent sensor collects data at a frequency of 1Hz, so as to obtain the upper and lower temperature data of the inner wall of the oven at each collection time during the emulsification experiment.

[0037] Step 2: Analyze the degree of change and difference of the peak temperature of the upper and lower bottom of the oven wall collected by the intelligent sensor to obtain the local peak anomaly degree at each collection time. Then, combine the discreteness of the temperature difference change between the upper and lower bottom to obtain the interference disorder degree at each collection time.

[0038] Traditional PID controllers use fixed proportional parameters to control the temperature inside the oven. This results in a failure to respond promptly to temperature changes influenced by the external environment, making it difficult to ensure temperature stability within the oven and potentially affecting the determination of the optimal blending ratio in the subsequent binary composite oil displacement system. Therefore, to accurately determine the optimal blending ratio of the binary composite oil displacement system, it is necessary to fine-tune the proportional parameters in the PID controller in real time. This improves the accuracy of temperature control within the oven and avoids affecting the emulsification effect of the binary composite oil displacement system.

[0039] To analyze the influence of ambient temperature on the upper and lower temperatures of the oven during short periods of time, the K collection times with the closest time interval to each collection time were recorded as the K local collection times for each collection time, where K was set to 60. Then, the upper and lower temperature data collected by the smart sensor at each collection time and its K local collection times were arranged in chronological order to obtain the upper temperature sequence and lower temperature sequence for each collection time.

[0040] Furthermore, the upper and lower temperature sequences at each acquisition time are used as inputs to the AMPD (Automatic Multiscale Peak Detection) algorithm. The AMPD algorithm obtains the positional order of all peaks in the upper and lower temperature sequences, respectively. The sequences formed by arranging all peak positions in the upper and lower temperature sequences in ascending order are recorded as the upper and lower peak position sequences at each acquisition time, reflecting the peak position change characteristics of the upper and lower temperatures collected by the smart sensor under the influence of external ambient temperature interference.

[0041] Generally, the smaller the difference between adjacent elements in the upper or lower peak sequence, the more frequent the peak fluctuations in the upper or lower temperatures collected by the smart sensor are due to interference from the external ambient temperature. At the same time, the greater the difference between the upper and lower peak sequences, the higher the degree of abnormality in the peak sequence of the upper and lower temperatures collected by the smart sensor in a local short period of time. This reflects that the upper and lower temperatures of the oven wall are more severely affected by the interference from the external ambient temperature. In this case, the stability of the temperature at different locations in the oven cannot be effectively guaranteed, which will affect the emulsification effect of the binary composite oil displacement system.

[0042] Based on the above analysis, the local peak position anomaly at each acquisition time is calculated: In the formula, Let be the local peak anomaly degree at the t-th acquisition time. denoted as DTW distance (Dynamic Time Warping) between the upper and lower peak sequences at the t-th acquisition time. and These are the mean values ​​of elements within the first-order difference sequences of the upper and lower peak positions at the t-th acquisition time, respectively. The calculation of the DTW dynamic programming distance and the first-order difference sequence are well-known techniques, and the specific process will not be elaborated here.

[0043] Among them, the local peak position anomaly reflects the degree of abnormality of the local peak position in the temperature changes of the upper and lower bottom collected by the intelligent sensor. The greater the local peak position anomaly, the more serious the interference of the external temperature on the temperature of the upper and lower bottom of the oven wall. At this time, it is more necessary to effectively regulate and control the temperature inside the oven through the PID controller, so as to respond in a timely manner to the temperature changes inside the oven under the influence of the external ambient temperature, and avoid affecting the emulsification effect of the binary composite oil displacement system.

[0044] Meanwhile, in order for the PID controller to respond promptly to the complex changes in the inconsistent temperatures between the upper and lower parts of the oven, it is necessary to combine the complex characteristics of these temperature variations to more accurately fine-tune the proportional parameters of the PID controller. Therefore, the difference between the upper and lower temperature sequences at each sampling time is calculated, and the first-order difference sequence of the resulting sequence is recorded as the temperature difference step change sequence at each sampling time, reflecting the step-variability characteristics of temperature difference changes under the influence of the external ambient temperature.

[0045] Generally, the higher the degree of anomaly in the local peak position of the temperature change between the upper and lower bottoms collected by the smart sensor, and the more complex the step change characteristics of the temperature difference between the upper and lower bottoms in the oven, the more reliably it can be explained that the temperature inside the oven is affected by the interference of the external ambient temperature, causing an unsteady abnormal change in the temperature inside the oven. In this case, it is more necessary to accurately adjust the proportional parameter in the PID controller so that the PID controller can respond more quickly to the unsteady abnormal temperature change inside the oven, thereby avoiding affecting the emulsification effect of the binary composite oil displacement system.

[0046] Based on the above analysis, the degree of interference at each acquisition time is calculated: In the formula, Let be the degree of disturbance at the t-th acquisition time. Let represent the degree of dispersion of all elements within the temperature difference step sequence at the t-th acquisition time. Methods for measuring the degree of dispersion include variance, standard deviation, or coefficient of variation. In this embodiment, the coefficient of variation is used to measure the degree of dispersion.

[0047] Based on the above process, it can be understood that the disturbance disorder reflects the abnormal disorder characteristics of the oven temperature caused by severe interference from the external ambient temperature. The greater the disturbance disorder, the more significant the abnormal disorder characteristics of the oven temperature caused by severe interference from the external ambient temperature. At this time, the risk of temperature runaway in the oven is more likely to increase. Therefore, it is more necessary to accurately adjust the proportional parameter in the PID controller to adjust and control the oven temperature in a timely and accurate manner, so as to avoid affecting the emulsification effect of the binary composite oil displacement system.

[0048] Step 3: Based on the high-frequency variation characteristics of the disturbance disorder during the emulsification process in the frequency domain, and combined with the disturbance disorder, the high-frequency interference characteristic values ​​at each acquisition time are obtained. The proportional coefficient of the PID controller in the oven is adjusted by the change of the high-frequency interference characteristic values ​​to control the temperature in the oven. Then, the optimal compounding ratio of the binary composite oil displacement system is determined by the water separation rate data of the binary composite oil displacement system during the emulsification process.

[0049] In order to accurately analyze the interference characteristics of the temperature data collected by the smart sensor due to the influence of the external environment, and thus more accurately adjust the proportional parameters in the PID controller, the interference disorder of each acquisition moment and its K local acquisition moments are arranged in chronological order to obtain the interference characteristic sequence of each acquisition moment, which reflects the change characteristics of the unsteady abnormal disorder of the oven in a local short period of time.

[0050] Furthermore, the frequency components in the disturbed feature sequence are analyzed. The disturbed feature sequence at each acquisition time is used as the input of the Fourier transform. The Fourier transform can be either a fast Fourier transform or a discrete Fourier transform. In this embodiment, the discrete Fourier transform is used to obtain the amplitude spectrum of the positive frequency part in the disturbed feature sequence at each acquisition time. The set of frequencies corresponding to all non-zero amplitudes in the amplitude spectrum of the positive frequency part is recorded as the local frequency set at each acquisition time. This reflects the frequency characteristics of the non-steady-state abnormal disorder in the temperature data collected by the smart sensor in a local short time. The Fourier transform is a well-known technique, and the specific process will not be described in detail.

[0051] Generally, if there are more high-frequency components in the local frequency set at a certain acquisition moment, and the temperature acquired by the smart sensor at this time exhibits greater non-steady-state abnormal disorder characteristics, it can better reflect the high-frequency change characteristics of the disturbance disorder during the emulsification process. This indicates that the non-steady-state high-frequency change of the temperature in the oven is more significant at this time. In this case, the proportional parameter in the PID controller should be increased so that the PID controller can respond in a timely manner to the high-frequency changes affected by the external ambient temperature.

[0052] Therefore, the local frequency set at each acquisition time is used as the input of the Otsu's inter-class variance algorithm. The segmentation threshold is obtained through the Otsu's inter-class variance algorithm. The set of frequencies in the local frequency set that are greater than the segmentation threshold is recorded as the local high-frequency set at each acquisition time, which reflects the high-frequency change components of the disturbance disorder during the emulsification process. The Otsu's inter-class variance algorithm is a well-known technology, and the specific process will not be described in detail.

[0053] Furthermore, in this embodiment, preferably, the Min-Max normalized result of the product between the number of elements in the local high-frequency set at each acquisition moment and the degree of disturbance is recorded as the high-frequency interference characteristic value at each acquisition moment. This reflects the high-frequency interference characteristics of the oven temperature affected by the ambient temperature during the emulsification process. If the change in the high-frequency interference characteristic value between adjacent acquisition moments increases, it indicates that the high-frequency interference of the oven temperature affected by the ambient temperature is more severe. In this case, the proportional parameter in the PID controller should be increased to respond promptly to the temperature change in the oven affected by the external ambient temperature. Conversely, if the change in the high-frequency interference characteristic value between adjacent acquisition moments decreases, it indicates that the high-frequency interference of the oven temperature affected by the ambient temperature is weakening. To avoid the proportional parameter being too large and affecting the stability of the temperature control system, the proportional parameter in the PID controller should be decreased to improve the stability of the temperature control system.

[0054] Based on the above analysis, the expected proportion coefficient at the current acquisition time is calculated: In the formula, This represents the expected scaling factor at the current data acquisition moment. The initial scaling factor is a preset value that takes values ​​within the range of (10, 15). In this embodiment, the preset initial scaling factor is 12. and These are the high-frequency interference characteristic values ​​at the current acquisition time and the previous acquisition time, respectively.

[0055] By fine-tuning the proportional coefficient of the PID controller inside the oven, the adjusted PID controller can respond promptly to temperature changes inside the oven under the influence of the external ambient temperature, and can ensure the stability of the temperature control system, thereby improving the accuracy of temperature control inside the oven.

[0056] Therefore, the desired proportional coefficient after real-time fine-tuning is used as the proportional parameter of the PID controller in the oven. The integral and derivative parameters of the PID controller in the oven are in the range of (0, 2). In this embodiment, the preset integral and derivative parameters are 1.2 and 1.5, respectively. The actual temperature of the inner wall of the oven collected by the smart sensor and the preset temperature in the oven are transmitted to the PID controller. The PID controller calculates the control signal through the temperature error between the actual temperature and the preset temperature value, and transmits the control signal to the heating unit in the oven to realize real-time adjustment and control of the temperature in the oven, so as to avoid affecting the emulsification process of the binary composite oil displacement system. The actual temperature is the average of the upper bottom temperature and the lower bottom temperature.

[0057] Therefore, by using a PID controller to regulate and control the temperature inside the oven, the emulsification experiment of the binary composite oil displacement system can be achieved more effectively. Simultaneously, the interface between the oil and the compound system can be observed to read the volume and calculate the water separation rate. The water separation rate data for the octylphenol polyoxyethylene ether binary system are shown in Table 2.

[0058] Table 2

[0059]

[0060] Experimental data shows that the emulsification effect of the octylphenol polyoxyethylene ether / polymer binary composite oil displacement system is better than that of the system without the addition of the binary composite oil displacement system. Furthermore, the octylphenol polyoxyethylene ether / polymer binary composite oil displacement system exhibits the most stable emulsion and the best emulsification effect at an oil-water ratio of 1:1, with a water separation rate of 4% after 4 hours. At an oil-water ratio of 1:9, the water separation rate after 4 hours is 67.67%, indicating instability in the emulsion system. However, the experiment shows that the intermediate transition layer area of ​​the composite system is large. Therefore, even when the water content of the oil reservoir is high, the system can still improve oil washing efficiency and increase the system's flow capacity within a certain time range, thereby improving the oil recovery rate.

[0061] Therefore, the optimal compounding system is octylphenol polyoxyethylene ether (0.1%), polymer concentration of 1500 mg / L, and oil-water ratio of 1:1. The compounding ratio of the optimal compounding system is the optimal compounding ratio of the binary composite oil displacement system.

[0062] The optimal blending system, octylphenol polyoxyethylene ether (0.1%), and the polymer were used in indoor core displacement experiments, and compared with the polymer system alone. The oil displacement experimental data are shown in Table 3.

[0063] Table 3

[0064]

[0065] As shown in Table 3, the optimal compound system in this embodiment improved the oil recovery rate by 7.95 percentage points compared to the single polymer system. Analysis of the experimental study in this embodiment shows that when the concentration of the nonionic surfactant octylphenol polyoxyethylene ether is 0.1%, it has a strong emulsifying effect and can achieve a certain emulsifying effect for oil layers with different water contents, thereby improving the oil displacement effect.

[0066] Based on the same inventive concept as the above method, this application embodiment also provides an optimal blending ratio determination system for a binary composite oil displacement system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the method for determining the optimal blending ratio of a binary composite oil displacement system described above.

[0067] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0068] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0069] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A method for determining the optimum matching ratio of a binary combination of oil displacement system, characterized in that, Includes the following steps: Emulsification experiments were conducted on a binary composite oil displacement system of octylphenol polyoxyethylene ether and polyacrylamide, and the upper and lower temperatures of the oven wall during the emulsification experiment were obtained by intelligent sensors. The degree of change and difference of the peak temperature of the upper and lower bottom of the oven inner wall collected by the intelligent sensor are analyzed to obtain the local peak anomaly degree at each collection time. Then, the degree of interference disorder at each collection time is obtained by combining the discreteness of the temperature difference change between the upper and lower bottom. Based on the high-frequency variation characteristics of the disturbance disorder during the emulsification process in the frequency domain, and combined with the disturbance disorder, the high-frequency interference characteristic values ​​at each acquisition time are obtained. The proportional coefficient of the PID controller in the oven is adjusted by the change of the high-frequency interference characteristic values ​​to control the temperature in the oven. Then, the optimal compounding ratio of the binary composite oil displacement system is determined by the water separation rate data of the binary composite oil displacement system during the emulsification process. The acquisition process of the local peak position anomaly degree of each acquisition moment is as follows: In the formula, is the local peak position anomaly degree of the tth acquisition moment, is the DTW distance between the upper bottom peak position sequence and the lower bottom peak position sequence of the tth acquisition moment, and are respectively the element mean in the first-order difference sequence of the upper bottom peak position sequence and the lower bottom peak position sequence of the tth acquisition moment. The process of obtaining the degree of interference at each acquisition time is as follows: In the formula, Let be the degree of disturbance at the t-th acquisition time. Let be the local peak anomaly degree at the t-th acquisition time. The degree of dispersion of all elements in the temperature difference step sequence at the t-th acquisition time; The process of obtaining the high-frequency interference feature value at each acquisition time is as follows: the normalized result of the product between the number of elements in the local high-frequency set at each acquisition time and the degree of interference disorder is used as the high-frequency interference feature value at each acquisition time. The process of adjusting the proportional coefficient of the PID controller inside the oven is as follows: In the formula, This represents the expected scaling factor at the current data acquisition moment. This is the preset initial scaling factor. and These are the high-frequency interference characteristic values ​​at the current acquisition time and the previous acquisition time, respectively.

2. The method for determining the optimal blending ratio of a binary composite oil displacement system as described in claim 1, characterized in that, The multiple acquisition times closest to each acquisition time interval are recorded as the local acquisition times of each acquisition time. The upper and lower bottom temperatures collected by the smart sensor at each acquisition time and its local acquisition times are arranged in chronological order to form the upper and lower bottom temperature sequences of each acquisition time. The position order of all peaks in the upper and lower bottom temperature sequences is extracted and arranged in ascending order to form the upper bottom peak position sequence and the lower bottom peak position sequence of each acquisition time.

3. The method for determining the optimal blending ratio of a binary composite oil displacement system as described in claim 1, characterized in that, The upper and lower temperature sequences at each acquisition time are subtracted, and the first-order difference sequence of the subtracted sequence is used as the temperature difference step sequence at each acquisition time.

4. The method for determining the optimal blending ratio of a binary composite oil displacement system as described in claim 1, characterized in that, The interference disorder of each acquisition time and its local acquisition time is arranged in time sequence to obtain the interference feature sequence of each acquisition time. The amplitude spectrum of the positive frequency part is extracted by frequency domain transformation. The frequencies corresponding to all non-zero amplitude values ​​in the amplitude spectrum of the positive frequency part are used to form the local frequency set of each acquisition time.

5. The method for determining the optimal blending ratio of a binary composite oil displacement system as described in claim 4, characterized in that, The local frequency sets at each acquisition time are segmented by a threshold, and the frequencies in the local frequency sets that are greater than the corresponding segmentation threshold are formed into local high frequency sets at each acquisition time.

6. A system for determining the optimal blending ratio of a binary composite oil displacement system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for determining the optimal blending ratio of a binary composite oil displacement system as described in any one of claims 1-5.

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