A method for determining the properties of emulsion polymers suitable for use in oil field flooding

Abstract

The application relates to a method for measuring the performance of an emulsion polymer suitable for oil displacement in an oil field. The method comprises: obtaining a viscosity sequence and a conductivity sequence corresponding to each period; determining an emulsion dissolution coefficient of the period according to the segmentation result of the viscosity sequence and the difference between the viscosity sequence corresponding to the period and the viscosity sequence corresponding to the previous period; segmenting to obtain a plurality of conductivity subsequences of the period, and performing linear analysis on the plurality of conductivity subsequences to obtain a slope corresponding to each conductivity subsequence; obtaining a dissolution distortion index of the period according to the difference between the average value of the target conductivity subsequence and the average value of the conductivity sequence corresponding to the next period, the ratio of the first slope of the period to the maximum slope of the slopes of the other conductivity subsequences of the period; and verifying the dissolution distortion index of the target period based on the dissolution distortion indexes of all periods to determine the dissolution period of the emulsion polymer. The application can accurately test the performance of the emulsion polymer.

Description

Technical Field

[0001] This application relates to the field of performance testing technology, and in particular to a method for determining the performance of emulsion polymers suitable for oilfield flooding. Background Technology

[0002] In the field of oil extraction, especially in tertiary oil recovery technology, emulsion polymers have attracted widespread attention as a novel oil displacement agent due to their good thickening properties, shear stability, and excellent emulsifying performance. Emulsion polymers can effectively improve oil displacement efficiency by reducing oil-water interfacial tension and improving the mobility ratio. However, if the performance of the emulsion polymer does not meet the requirements, it will not only fail to achieve the expected oil displacement effect but may also cause problems such as injection difficulties and pore throat blockage, ultimately reducing the recovery rate.

[0003] The solubility and dispersibility of emulsion polymers are crucial factors affecting their injection performance. To ensure successful application of emulsion polymers in formations and avoid pore throat blockage caused by sediments, precise measurement of their dissolution efficiency is essential. Current techniques typically utilize viscometers to intermittently measure the viscosity of the emulsion polymer solution and determine dissolution time by analyzing the stability of the liquid viscosity. However, existing techniques do not adequately consider the susceptibility of viscometers to the effects of bubbles and flow fields, leading to data distortion. Furthermore, this intermittent measurement method suffers from measurement lag, resulting in errors or delays in the determination of emulsion polymer dissolution performance and poor performance testing results. Therefore, a suitable method for measuring the performance of emulsion polymers in oilfield flooding is needed to accurately test their properties and ensure the stability of oil displacement. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method for determining the properties of emulsion polymers suitable for oilfield flooding. The specific technical solution adopted is as follows: In a first aspect, a method for determining the properties of emulsion polymers suitable for oilfield flooding is provided, the method comprising: During the dissolution process of the emulsion polymer, the viscosity sequence and conductivity sequence corresponding to each cycle are obtained; the viscosity sequence corresponding to each cycle includes the viscosity data of the current cycle and the previous cycles, and the conductivity sequence corresponding to each cycle includes the conductivity data of the current cycle; Based on the segmentation results of the viscosity sequence corresponding to each period and the difference between the viscosity sequence corresponding to this period and the viscosity sequence corresponding to the previous period, the emulsion solubility coefficient of this period is determined; the emulsion solubility coefficient is used to characterize whether the emulsion polymer of this period is completely dissolved. The conductivity sequence of each period is segmented to obtain multiple conductivity subsequences of that period. Linear analysis is then performed on each of the multiple conductivity subsequences of that period to obtain the slope corresponding to each conductivity subsequence of that period. The dissolution distortion index for a given period is obtained by considering the difference between the mean of the target conductivity subsequence for each period and the mean of the conductivity subsequence for the next period, the ratio of the first slope of that period to the largest slope among the slopes of other conductivity subsequences in that period, and the target conductivity subsequence being the conductivity subsequence located at a preset position among multiple conductivity subsequences in each period. The first slope indicates the slope corresponding to the target conductivity subsequence for that period. The dissolution distortion index is used to characterize the reliability of judging whether the emulsion polymer is completely dissolved based on viscosity data. Based on the dissolution distortion index of all cycles, the dissolution distortion index of the target cycle is verified to determine the dissolution cycle of the emulsion polymer according to the verification results; the target cycle is selected from multiple cycles based on the emulsion solubility coefficient.

[0005] Optionally, during the dissolution process of the emulsion polymer, the viscosity sequence and conductivity sequence corresponding to each cycle are obtained, including: During the dissolution process of the emulsion polymer, viscosity data is acquired once and conductivity data is acquired multiple times in each cycle, resulting in one viscosity data and multiple conductivity data for that cycle. By arranging one viscosity data point from each period and multiple viscosity data points from previous periods in chronological order, the viscosity sequence corresponding to that period is obtained. The conductivity data for each period are sorted in chronological order to obtain the conductivity sequence corresponding to that period.

[0006] Optionally, the emulsion solubility coefficient for each period is determined based on the segmentation results of the viscosity sequence corresponding to each period and the difference between the viscosity sequence corresponding to the current period and the viscosity sequence corresponding to the previous period, including: The viscosity sequence corresponding to each period is segmented to obtain multiple viscosity subsequences for that period. These multiple viscosity subsequences are then sorted in chronological order, and the last viscosity subsequence is determined as the target viscosity subsequence for that period. The emulsion solubility coefficient for each period is determined based on the dispersion among all viscosity data in the target viscosity subsequence for each period and the difference between the viscosity sequence corresponding to this period and the viscosity sequence corresponding to the previous period. The segmentation result of the viscosity sequence corresponding to each period includes the dispersion among all viscosity data in the target viscosity subsequence.

[0007] Optionally, the emulsion solubility coefficient for each period is determined based on the segmentation results of the viscosity sequence corresponding to each period and the difference between the viscosity sequence corresponding to the current period and the viscosity sequence corresponding to the previous period, including: The viscosity sequence corresponding to each period is segmented to obtain multiple viscosity subsequences for that period. These multiple viscosity subsequences are then sorted in chronological order, and the last viscosity subsequence is determined as the target viscosity subsequence for that period. The emulsion solubility coefficient for each period is determined based on the dispersion among all viscosity data in the target viscosity subsequence for each period and the difference between the viscosity sequence corresponding to this period and the viscosity sequence corresponding to the previous period. The segmentation result of the viscosity sequence corresponding to each period includes the dispersion among all viscosity data in the target viscosity subsequence.

[0008] Optionally, the emulsion solubility coefficient for a given period is determined based on the normalized viscosity discrete value for each period, the first difference for that period, and the number of multiple viscosity subsequences for that period, including: The emulsion solubility coefficient for each period is determined by multiplying the first difference of each period by the number of multiple viscosity subsequences in that period, and by the normalized viscosity discrete value of that period.

[0009] Optionally, the conductivity sequence of each period is segmented to obtain multiple conductivity subsequences for that period, and linear analysis is performed on each of the multiple conductivity subsequences for that period to obtain the slope corresponding to each conductivity subsequence for that period, including: The conductivity sequence of each period is divided into multiple conductivity subsequences for that period. Using the sampling time of each conductivity data point as the horizontal axis and the conductivity data corresponding to the sampling time as the vertical axis, a straight line is fitted to each conductivity subsequence, and the absolute value of the slope of the fitted line is calculated to obtain the slope of each conductivity subsequence in that period.

[0010] Optionally, the dissolution distortion index for a period is obtained based on the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence corresponding to the next period, and the ratio of the first slope of that period to the largest slope among the slopes of other conductivity subsequences of that period, including: Multiple conductivity subsequences for each period are sorted in chronological order, and the last conductivity subsequence is determined as the target conductivity subsequence for that period. Calculate the average of the slopes corresponding to multiple conductivity subsequences in the next cycle of each cycle to obtain the second slope of the next cycle. Calculate the average of the first slope of each cycle and the second slope of the next cycle to obtain the average slope of that cycle. From the slopes corresponding to multiple other conductivity subsequences in each cycle, the largest slope is selected, and the ratio of the first slope of the cycle to the largest slope is calculated to obtain the normalized first slope of the cycle. The dissolution distortion index of a period is obtained by analyzing the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence of the next period, the mean slope of that period, and the normalized first slope of that period.

[0011] Optionally, the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence corresponding to the next period, the mean slope of that period, and the normalized first slope of that period are analyzed to obtain the dissolution distortion index of that period, including: Calculate the mean of multiple conductivity data in the target conductivity subsequence for each period to obtain the mean of the target conductivity subsequence for that period; Calculate the mean of multiple conductivity data in the conductivity sequence corresponding to the next cycle of each cycle to obtain the mean of the conductivity sequence corresponding to the next cycle. The absolute difference between the mean of the target conductivity subsequence for each period and the mean of the conductivity sequence for the next period is calculated to obtain the second difference for that period. Based on the difference between the means of conductivity sequences of multiple other adjacent periods, the second difference of each period is normalized to obtain the normalized second difference of that period; the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence corresponding to the next period includes the normalized second difference of that period. The dissolution distortion index for each period is obtained by summing the normalized second difference, the mean slope of the period, and the normalized first slope of the period.

[0012] Optionally, before verifying the dissolution distortion index of the target period based on the dissolution distortion index of all periods, and before determining the dissolution time of the emulsion polymer based on the verification results, the method further includes: According to the Otsu threshold method, the emulsion solubility coefficients of multiple cycles preceding each cycle are segmented to obtain the segmentation threshold of that cycle, and the number of multiple cycles is determined as the total number of cycles for that cycle. The period to which the emulsion solubility coefficient is greater than the segmentation threshold is determined as the first period, and the period to which the emulsion solubility coefficient is less than or equal to the segmentation threshold is determined as the second period; the emulsion polymer in the first period is in a completely dissolved state, while the emulsion polymer in the second period is not completely dissolved; Calculate the sum of the number of multiple first cycles preceding each cycle and the number of multiple second cycles to obtain the segmentation and value of that cycle; In response to the fact that the sum of the values ​​of each period is not equal to the total number of periods in that period, multiple first periods are sorted in ascending order according to the emulsion solubility, and the first period that is first after sorting is determined as the next nearest period; the next nearest period indicates the next nearest period after the emulsion polymer is completely dissolved. Multiple periods are sorted in chronological order, and the period preceding the next adjacent period is determined as the target period; the target period indicates the period in which the calculated emulsion polymer is completely dissolved.

[0013] Optionally, based on the dissolution distortion index of all cycles, the dissolution distortion index of the target cycle is validated to determine the dissolution cycle of the emulsion polymer according to the validation results, including: An anomaly detection algorithm is used to detect outliers in the dissolution distortion index for all cycles, and outliers are obtained. If the period corresponding to the outlier is the target period, then the target period is determined as the dissolution period of the emulsion polymer; the dissolution period indicates the actual period during which the emulsion polymer completely dissolves.

[0014] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this application.

[0015] This application offers the following advantages: In each cycle, a viscosity sequence containing current and historical data and a conductivity sequence containing only current cycle data are acquired simultaneously. Based on the segmentation results of the viscosity sequence and its difference from the viscosity sequence of the previous cycle, an emulsion solubility coefficient is constructed to characterize whether the emulsion polymer has completely dissolved in that cycle. Simultaneously, the conductivity sequence is sub-sequenced and linear analysis is performed. Combining the difference between the mean of the target conductivity sub-sequence and the mean of the conductivity sequence of the next cycle, and the ratio of the slope of the target sub-sequence to the maximum slope of the remaining sub-sequences, a dissolution distortion index is constructed to verify the reliability of the viscosity judgment results. This not only overcomes the problem of misjudgment of the dissolution endpoint caused by bubble interference or viscosity response lag in existing technologies, but also accurately locates the actual dissolution completion cycle through the synergistic mechanism of the emulsion solubility coefficient and the dissolution distortion index. The entire process relies on automated data acquisition and intelligent algorithm analysis, achieving multi-dimensional, high-precision, and minimally human-interventional evaluation of dissolution performance, significantly outperforming traditional testing methods that rely on a single parameter, and providing more reliable technical support for oilfield applications. Attached Figure Description

[0016] 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.

[0017] Figure 1 This is a flowchart of a method for determining the properties of an emulsion polymer suitable for oilfield flooding, as described in one embodiment. Figure 2 This is a schematic diagram of the structure of an emulsion polymer performance testing system suitable for oilfield flooding in one embodiment; Figure 3 This is a schematic diagram of the structure of an electronic device in one embodiment. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for determining the properties of emulsion polymers suitable for oilfield flooding 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.

[0019] Unless otherwise defined, 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.

[0020] The following, in conjunction with the accompanying drawings, details a specific scheme for determining the properties of emulsion polymers suitable for oilfield flooding, as provided in this application. For example... Figure 1 As shown, the method includes: S11. During the dissolution process of the emulsion polymer, obtain the viscosity sequence and conductivity sequence corresponding to each cycle.

[0021] The viscosity sequence for each period includes viscosity data from that period and previous periods, and the conductivity sequence for each period includes conductivity data from that period. The duration of each period can be 1 minute.

[0022] Viscosity data is acquired using a viscometer, and conductivity data is acquired using a conductivity meter. Both the conductivity meter and viscometer used in this application fall under the category of intelligent sensors: intelligent sensors integrate sensing elements, embedded processing modules, local caches, and communication interfaces, thus enabling the construction of a complete closed-loop processing structure of "sensing-processing-storage-transmission." During operation, the intelligent sensor can not only collect physical quantities of the liquid, such as conductivity and viscosity data, in real time, but also perform preliminary filtering and missing value filling operations on the raw signal locally, thereby improving the quality of the collected data. Simultaneously, the communication unit of the intelligent sensor can achieve timestamp-synchronized transmission with the host computer, ensuring that the two heterogeneous data points of conductivity and viscosity are aligned on a unified time axis, thereby providing high-quality data support for subsequent performance determination of the emulsion polymer.

[0023] In one embodiment, during the dissolution process of the emulsion polymer, obtaining the viscosity sequence and conductivity sequence corresponding to each cycle includes: During the dissolution process of the emulsion polymer, viscosity data is acquired once and conductivity data is acquired multiple times in each cycle, resulting in one viscosity data and multiple conductivity data for that cycle. By arranging one viscosity data point from each period and multiple viscosity data points from previous periods in chronological order, the viscosity sequence corresponding to that period is obtained. The conductivity data for each period are sorted in chronological order to obtain the conductivity sequence corresponding to that period.

[0024] The emulsion polymer used in this embodiment is a purchased finished product. First, 500-700g (600g in this embodiment) of water is weighed into a beaker, and then the beaker is placed in a water bath at a constant temperature of 55-70°C (65°C in this embodiment). Next, the speed of the stirrer is set to a constant value of 350-450r / min (400r / min in this embodiment), and the probe of the conductivity meter is completely immersed in the liquid in the beaker.

[0025] Then turn on the stirrer switch. When the rotor is rotating at a constant speed, add 2.5-3.5g (3g in this example) of emulsion polymer. From this moment on, measure the conductivity of the liquid every second using a conductivity meter and the viscosity of the liquid every minute using a rotational viscometer.

[0026] Understandably, 60 conductivity data points are acquired in each cycle, and the 60 conductivity data points of the cycle are sorted in chronological order to obtain the conductivity sequence corresponding to that cycle.

[0027] Viscosity data is acquired once per cycle. The viscosity data from that cycle and the viscosity data from previous cycles are arranged in chronological order to obtain the viscosity sequence corresponding to that cycle. For example, from the first cycle to the i-th cycle, the viscosity values ​​acquired in each cycle are 200 (first cycle), 300 (second cycle), 500 (third cycle), ..., 2500 (i-th cycle), so the viscosity sequence corresponding to the i-th cycle is [200, 300, 500, ..., 2500].

[0028] S12. Based on the segmentation results of the viscosity sequence corresponding to each period and the difference between the viscosity sequence corresponding to this period and the viscosity sequence corresponding to the previous period, determine the emulsion solubility coefficient of this period.

[0029] The emulsion solubility coefficient is used to characterize whether the emulsion polymer is completely dissolved in this cycle.

[0030] During the dissolution process of the emulsion polymer, the liquid viscosity data collected by the smart sensor exhibits distinct phased changes: in the initial stage, the emulsion polymer particles gradually disperse, causing the liquid viscosity to rise rapidly; however, once the emulsion polymer is completely dissolved in water, the liquid reaches a stable structure, and the viscosity begins to fluctuate steadily with smaller fluctuations. Therefore, by analyzing whether the liquid viscosity in the current cycle has begun to stabilize, a preliminary assessment can be made as to whether the emulsion polymer has completely dissolved in water in the current cycle.

[0031] In one embodiment, the emulsion solubility coefficient for a given period is determined based on the segmentation result of the viscosity sequence corresponding to each period and the difference between the viscosity sequence of that period and the viscosity sequence of the previous period, including: The viscosity sequence corresponding to each period is segmented to obtain multiple viscosity subsequences for that period. These multiple viscosity subsequences are then sorted in chronological order, and the last viscosity subsequence is determined as the target viscosity subsequence for that period. The emulsion solubility coefficient for each period is determined based on the dispersion among all viscosity data in the target viscosity subsequence for each period and the difference between the viscosity sequence corresponding to this period and the viscosity sequence corresponding to the previous period. The segmentation result of the viscosity sequence corresponding to each period includes the dispersion among all viscosity data in the target viscosity subsequence.

[0032] Taking the i-th period as an example, the viscosity sequence corresponding to the i-th period is used as the input to the sequence segmentation algorithm for sequence segmentation, resulting in... A viscosity subsequence. (The following is a list of viscosity subsequences.) The viscosity subsequences are sorted in chronological order, and the viscosity subsequence containing the viscosity value element collected in the i-th period is recorded as the target viscosity subsequence. That is, the last viscosity subsequence is the target viscosity subsequence of the i-th period. It can represent the number of viscosity subsequences that can be divided into the viscosity sequence corresponding to the i-th period. The larger the value, the more likely there are multiple data change trends. The smaller the value, the more likely there is only one upward trend, and the greater the probability that the emulsion polymer has not completely dissolved in the i-th period. Sequence segmentation algorithms include, but are not limited to, the BG segmentation algorithm (Brodsky–Georgiev, change point detection algorithm) and the MK segmentation algorithm (Mann–Kendal test, MK change point detection algorithm). It should be noted that: one viscosity value is collected and a viscosity sequence is constructed for each period, so the viscosity value element corresponding to the i-th period must be the last element in the viscosity sequence.

[0033] In one embodiment, the emulsion solubility coefficient for a given period is determined based on the dispersion among all viscosity data in the target viscosity subsequence for each period and the difference between the viscosity sequence for that period and the viscosity sequence for the previous period, including: Calculate the dispersion among all viscosity data in the target viscosity subsequence for each period to obtain the viscosity dispersion value for that period; the dispersion can be calculated using any of the following methods: standard deviation, root mean square deviation, or mean deviation; Calculate the dispersion among all viscosity data in other viscosity subsequences of each period to obtain the dispersion of other viscosity subsequences, and normalize the viscosity discrete value of the period based on the dispersion of multiple other viscosity subsequences of the period to obtain the normalized viscosity discrete value of the period. Calculate the difference between the viscosity data of each period and the viscosity data of the previous period to obtain the first difference for that period; the viscosity sequence for each period includes the viscosity data of that period; The emulsion solubility coefficient for each period is determined based on the normalized viscosity discrete value for each period, the first difference for that period, and the number of multiple viscosity subsequences for that period.

[0034] Specifically, the emulsion solubility coefficient for each period is determined based on the normalized viscosity discrete value for each period, the first difference for that period, and the number of multiple viscosity subsequences for that period, including: The emulsion solubility coefficient for each period is determined by multiplying the first difference of each period by the number of multiple viscosity subsequences in that period, and by the normalized viscosity discrete value of that period.

[0035] Calculate the dispersion among all elements of the target viscosity subsequence in the i-th period. The dispersion calculation is not limited to standard deviation, root mean square deviation, or mean deviation, to obtain the viscosity dispersion value for the i-th period. Calculate the dispersion of each other viscosity subsequence in the i-th period in the same way to obtain the dispersion of that other viscosity subsequence. Based on the dispersion of all other viscosity subsequences in the i-th period, normalize the viscosity dispersion value of the i-th period using the maximum value normalization method to obtain the normalized viscosity dispersion value for the i-th period. The normalized viscosity dispersion value for the i-th period reflects whether the polymer dissolution process has begun to stabilize in the i-th period. The smaller the normalized viscosity dispersion value for the i-th period, the more stable the change in liquid viscosity in the current period, and the greater the probability that the polymer dissolution is complete.

[0036] Calculate the difference between the viscosity value (viscosity data) of the i-th period and the viscosity value of the (i-1)-th period to obtain the first difference value for the i-th period. Calculate the first difference value for each period in the same way, and based on the first difference values ​​of other periods, normalize the first difference value for the i-th period using maximum value normalization to obtain the normalized first difference value for the i-th period. The normalized first difference value for the i-th period reflects the degree of change in liquid viscosity compared to the previous period. The smaller or even negative the normalized first difference value for the i-th period, the greater the increase in liquid viscosity in the i-th period, and the less likely the polymer is to completely dissolve. Conversely, the larger the normalized first difference value for the i-th period, the more likely the liquid viscosity has begun to decrease compared to the previous period, and the more likely the polymer has already completely dissolved. The emulsion solubility coefficient for the i-th period... The calculation formula is: ; In the formula, Let be the number of multiple viscosity subsequences in the i-th period. The normalized viscosity discrete value for the i-th period. This is the first difference after normalization in the i-th period. To avoid the denominator being 0, the parameter coefficients are taken from the empirical range of (0.005, 0.01). The value has little impact on the calculation and can be ignored. In this embodiment, 0.008 is used.

[0037] The right side of the formula denoted as the number of viscosity subsequences, and denoted as a dimensionless numerical value. Characterizing the degree of dispersion between viscosity data, To characterize the differences between viscosity data, and to avoid the problem that excessive differences between parameters would lead to a smaller influence on other parameters, maximum value normalization is performed, resulting in... and The data is normalized, and since normalization results in dimensionless values, the final product is also dimensionless.

[0038] Emulsion solubility is assessed by using the number of subsequences to reflect the viscosity trends during the current period, the viscosity discrete value to reflect the stability of the liquid viscosity, the first difference to reflect the trend of the liquid viscosity, and finally by multiplying these values ​​to comprehensively evaluate the degree of polymer solubility.

[0039] Emulsion solubility This reflects the likelihood that the liquid viscosity will stabilize during the i-th period, and thus indicates whether the emulsion polymer may have completely dissolved. Emulsion solubility The larger the value, the more likely the liquid viscosity data shows multiple trends in the i-th period, and the greater the possibility that the liquid concentration in the i-th period has stabilized or decreased slightly compared to the previous period. This, in turn, indicates that the emulsion polymer is more likely to have completely dissolved in the i-th period.

[0040] Since viscosity data is measured indirectly through smart sensors, this type of intermittent measurement method has an inherent judgment delay. For example, even if the emulsion polymer has completely dissolved in the (i-1)th cycle, the stable change characteristics of the liquid viscosity can usually not be captured until the i-th cycle. Therefore, further analysis is needed to obtain more accurate solubility test results.

[0041] S13. Divide the conductivity sequence of each period to obtain multiple conductivity subsequences of that period, and perform linear analysis on each of the multiple conductivity subsequences of that period to obtain the slope corresponding to each conductivity subsequence of that period.

[0042] During the dissolution process of emulsion polymers, stirring can easily generate bubbles, which may interfere with the stability of the liquid shear stress in the beaker. This can cause the liquid viscosity measured by the smart sensor to deviate from the true level, resulting in data errors and affecting the accuracy of performance determination of the emulsion polymer. However, because emulsion polymers usually contain a certain concentration of electrolyte ions, they have strong conductivity. As the dissolution process progresses, new ionization occurs continuously, causing the liquid's conductivity to increase until the emulsion polymer is completely dissolved, at which point the liquid conductivity tends to stabilize. Therefore, the change in liquid conductivity data can be used to verify the reliability of the viscosity data collected by the smart sensor.

[0043] Therefore, in one embodiment, the conductivity sequence of each period is segmented to obtain multiple conductivity subsequences for that period, and linear analysis is performed on each of the multiple conductivity subsequences for that period to obtain the slope corresponding to each conductivity subsequence for that period, including: The conductivity sequence of each period is divided into multiple conductivity subsequences for that period. Using the sampling time of each conductivity data point as the horizontal axis and the conductivity data corresponding to the sampling time as the vertical axis, a straight line is fitted to each conductivity subsequence, and the absolute value of the slope of the fitted line is calculated to obtain the slope of each conductivity subsequence in that period.

[0044] The conductivity sequence corresponding to each period is used as input to the sequence segmentation algorithm, resulting in multiple conductivity subsequences for that period. Then, based on the position of the first element within each subsequence within the overall conductivity sequence, the subsequences are sorted in ascending order, and the last sorted subsequence is determined as the target conductivity subsequence for that period. Sequence segmentation algorithms include, but are not limited to, the BG segmentation algorithm (Brodsky–Georgiev, change point detection algorithm) and the MK segmentation algorithm (Mann–Kendal test, MK change point detection algorithm).

[0045] Each conductivity subsequence of each cycle is used as input to a straight line fitting algorithm to perform straight line fitting. Then, the absolute value of the slope of the fitted straight line corresponding to each conductivity subsequence is obtained to obtain the slope corresponding to each conductivity subsequence of that cycle.

[0046] S14. Based on the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence corresponding to the next period, and the ratio of the first slope of the period to the largest slope among the slopes of other conductivity subsequences of the period, the dissolution distortion index of the period is obtained.

[0047] The dissolution distortion index is used to characterize the reliability of judging whether an emulsion polymer is completely dissolved based on viscosity data.

[0048] The target conductivity subsequence is the conductivity subsequence located at a preset position among multiple conductivity subsequences in each period. The multiple conductivity subsequences in each period can be sorted chronologically, and the last conductivity subsequence is determined as the target conductivity subsequence for that period. The first slope indicates the slope corresponding to the target conductivity subsequence for that period.

[0049] In one embodiment, the dissolution distortion index for a period is obtained based on the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity subsequence corresponding to the next period, and the ratio of the first slope of the period to the largest slope among the slopes of other conductivity subsequences of the period, including: Multiple conductivity subsequences for each period are sorted in chronological order, and the last conductivity subsequence is determined as the target conductivity subsequence for that period. Calculate the average of the slopes corresponding to multiple conductivity subsequences in the next cycle of each cycle to obtain the second slope of the next cycle. Calculate the average of the first slope of each cycle and the second slope of the next cycle to obtain the average slope of that cycle. From the slopes corresponding to multiple other conductivity subsequences in each cycle, the largest slope is selected, and the ratio of the first slope of the cycle to the largest slope is calculated to obtain the normalized first slope of the cycle. The dissolution distortion index of a period is obtained by analyzing the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence of the next period, the mean slope of that period, and the normalized first slope of that period.

[0050] Specifically, the dissolution distortion index of each cycle is obtained by analyzing the difference between the mean of the target conductivity subsequence of each cycle and the mean of the conductivity sequence of the next cycle, the mean slope of that cycle, and the normalized first slope of that cycle, including: Calculate the mean of multiple conductivity data in the target conductivity subsequence for each period to obtain the mean of the target conductivity subsequence for that period; Calculate the mean of multiple conductivity data in the conductivity sequence corresponding to the next cycle of each cycle to obtain the mean of the conductivity sequence corresponding to the next cycle. The absolute difference between the mean of the target conductivity subsequence for each period and the mean of the conductivity sequence for the next period is calculated to obtain the second difference for that period. Based on the difference between the means of conductivity sequences of multiple other adjacent periods, the second difference of each period is normalized to obtain the normalized second difference of that period; the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence corresponding to the next period includes the normalized second difference of that period. The dissolution distortion index for each period is obtained by summing the normalized second difference, the mean slope of the period, and the normalized first slope of the period.

[0051] The absolute value of the slope of the fitted straight line corresponding to the target conductivity subsequence is recorded as the first slope of that period. The first slope can reflect the overall trend of the data change of the target conductivity subsequence. The smaller the first slope, the more gradual the change of liquid conductivity in this stage, and the closer it is to the steady-state characteristic of ion release tending to saturate after the polymer is completely dissolved.

[0052] The conductivity sequence corresponding to the next cycle of each cycle is used as the input to the straight line fitting algorithm to perform straight line fitting, and the absolute value of the slope of the fitted straight line is recorded as the second slope of the next cycle.

[0053] The average slope of each cycle is calculated by taking the first slope of each cycle and the second slope of the next cycle. This average slope of the cycle reflects the average trend of the two sets of conductivity data. The smaller the value, the more stable the average trend of the liquid conductivity data in the two stages, and thus the more consistent the change of liquid conductivity is with the change characteristics after the polymer is completely dissolved.

[0054] Then, based on the absolute values ​​of the slopes of all conductivity subsequences in each period, the first slope of the target conductivity subsequence for that period is normalized to its maximum value. Specifically, from the slopes corresponding to multiple other conductivity subsequences in each period, the largest slope is selected, and the ratio of the first slope of that period to the largest slope is calculated to obtain the normalized first slope of that period. The normalized first slope reflects the degree of drastic change in the conductivity rate of the target conductivity subsequence among all conductivity subsequences. The smaller the value, the smaller the change trend of the target conductivity subsequence compared to other conductivity subsequences, and the closer it is to the steady-state characteristics after dissolution is completed. This indicates a higher probability that the period is the end of dissolution and a greater likelihood that it is not a misjudgment. Conversely, a larger value indicates that the period is less likely to be the end of dissolution.

[0055] If the emulsion polymer achieves complete dissolution in a certain cycle, and the ion concentration of the liquid tends to stabilize, then the conductivity values ​​of the target conductivity subsequence for that cycle should be relatively consistent with the conductivity sequence in the following cycle. Therefore, the mean of multiple conductivity data in the target conductivity subsequence for each cycle is calculated to obtain the mean s1 of the target conductivity subsequence for that cycle. The mean of multiple conductivity data in the conductivity sequence for the following cycle is then calculated to obtain the mean s2 of the conductivity sequence for the following cycle. The absolute difference between the two means s1 and s2 is then calculated to obtain the second difference for that cycle. The second difference reflects whether the conductivity of the liquid is consistent in the two stages. The smaller the second difference, the more consistent the conductivity, and thus the greater the likelihood that the polymer has completely dissolved.

[0056] The mean of the conductivity sequence for each period is calculated, and the absolute difference between the means of the conductivity sequences of adjacent periods is calculated sequentially to reflect the degree of change in liquid conductivity between adjacent periods. Then, based on the calculated absolute difference of all conductivity sequence means, the second difference of the target conductivity subsequence for each period is normalized to its maximum value, resulting in the normalized second difference for that period, thus reflecting the significance of the second difference among the conductivity data differences of all adjacent periods. The dissolution distortion index for each period is also calculated. The calculation formula is: ; in, The dissolution distortion index for each period, This represents the average slope of the period. This represents the normalized first slope of the period. This is the normalized second difference for that period.

[0057] D is the mean slope, which reflects the average trend of the target conductivity subsequence and the conductivity sequence in the next period. It has no physical dimension. Therefore, the normalized first slope G calculated by the slope also has no physical dimension. The normalized second difference H eliminates the physical dimension of conductivity by normalizing the maximum value. Therefore, the final sum also has no physical dimension.

[0058] The dissolution distortion index reflects the average drastic change in conductivity between two stages by using the mean slope. The normalized first slope reflects the drastic change in the target conductivity subsequence among all conductivity subsequences. The second difference reflects the degree of difference in conductivity between the two stages. Finally, the sum is used to comprehensively reflect whether the data change characteristics of liquid conductivity are consistent with the characteristics after complete polymer dissolution, thereby characterizing the reliability of the data collected by the smart sensor.

[0059] The dissolution distortion index, by combining the characteristics of changes in liquid conductivity, can further analyze the likelihood of complete dissolution of emulsion polymers, and thus reflect the degree of distortion in the data collected by smart sensors. A smaller dissolution distortion index indicates that the evolution of liquid conductivity during the period when viscosity begins to stabilize more closely matches the characteristics of complete polymer dissolution. This reflects a lower likelihood of interference with the viscosity and conductivity data collected by the smart sensor, and a higher confidence level in confirming that this period represents a complete polymer dissolution cycle.

[0060] S15. Based on the dissolution distortion index of all cycles, verify the dissolution distortion index of the target cycle to determine the dissolution cycle of the emulsion polymer according to the verification results.

[0061] The target cycle is selected from multiple cycles based on the emulsion solubility coefficient.

[0062] Specifically, before verifying the dissolution distortion index of the target period based on the dissolution distortion index of all periods, and determining the dissolution time of the emulsion polymer based on the verification results, the process also includes: According to the Otsu threshold method, the emulsion solubility coefficients of multiple cycles preceding each cycle are segmented to obtain the segmentation threshold of that cycle, and the number of multiple cycles is determined as the total number of cycles for that cycle. The period to which the emulsion solubility coefficient is greater than the segmentation threshold is determined as the first period, and the period to which the emulsion solubility coefficient is less than or equal to the segmentation threshold is determined as the second period; the emulsion polymer in the first period is in a completely dissolved state, while the emulsion polymer in the second period is not completely dissolved; Calculate the sum of the number of multiple first cycles preceding each cycle and the number of multiple second cycles to obtain the segmentation and value of that cycle; In response to the fact that the sum of the values ​​of each period is not equal to the total number of periods in that period, multiple first periods are sorted in ascending order according to the emulsion solubility, and the first period that is first after sorting is determined as the next nearest period; the next nearest period indicates the next nearest period after the emulsion polymer is completely dissolved. Multiple periods are sorted in chronological order, and the period preceding the next adjacent period is determined as the target period; the target period indicates the period in which the calculated emulsion polymer is completely dissolved.

[0063] Because the viscosity of the liquid continues to increase before the emulsion polymer is completely dissolved, the emulsion solubility will generally be low. However, after the polymer is completely dissolved, the emulsion solubility will generally be high in each period. Therefore, the emulsion solubility in each collection period will undergo significant phased changes before and after the solution polymer is completely dissolved.

[0064] Since the dissolution time of emulsion polymers is usually more than 10 minutes, the i-th cycle is usually the cycle after 10 minutes. The emulsion solubility of all cycles before the i-th cycle is used as the input of Otsu's threshold method to obtain the segmentation threshold of the i-th cycle. All cycles with emulsion solubility greater than the segmentation threshold output by Otsu's threshold method are recorded as the first cycle, representing the cycle after complete dissolution; all cycles with emulsion solubility less than or equal to the segmentation threshold are recorded as the second cycle, representing the cycle during dissolution.

[0065] Considering that the segmentation threshold output by the Otsu thresholding method may not be the true value, but rather the theoretically optimal segmentation solution, the sum of the number of multiple first periods and the number of multiple second periods is calculated and denoted as the segmentation sum of the i-th period. If the segmentation sum of the i-th period has the same data length as the viscosity sequence corresponding to the i-th period, it indicates that the segmentation threshold is not the true value. In this case, no processing is performed on the segmentation threshold to avoid errors.

[0066] If the sum of the segments in the i-th period is inconsistent with the data length of the viscosity sequence corresponding to the i-th period, the multiple first periods are sorted from smallest to largest according to the emulsion solubility. That is, among all the first periods, the period with the smallest position is recorded as the next nearest neighbor period, representing the next nearest neighbor period after the emulsion polymer is completely dissolved. Then, the period preceding the next nearest neighbor period among all the sorted periods is determined as the target period, representing the period in which the emulsion polymer has achieved complete dissolution.

[0067] Compared to raw viscosity data, emulsion solubility incorporates multi-dimensional features such as stage evolution and fluctuation changes, providing stronger differentiation and anti-interference capabilities for the polymer's dissolution state. It can avoid misjudgments caused by slow viscosity increases, local disturbances, or bubble interference. Thus, emulsion solubility clustering can more clearly distinguish data clusters before and after polymer dissolution is complete, and more accurately locate the cycle corresponding to the moment of dissolution completion.

[0068] In one embodiment, the dissolution distortion index of the target period is verified based on the dissolution distortion index of all periods to determine the dissolution period of the emulsion polymer according to the verification result, including: An anomaly detection algorithm is used to detect outliers in the dissolution distortion index for all cycles, and outliers are obtained. If the period corresponding to the outlier is the target period, then the target period is determined as the dissolution period of the emulsion polymer; the dissolution period indicates the actual period during which the emulsion polymer completely dissolves.

[0069] Because the dissolution distortion index incorporates the changing characteristics of emulsion polymer dissolution, it tends to be higher during periods of polymer dissolution and after complete dissolution, and lower only during periods of complete polymer dissolution. This results in significant instantaneous variations and substantial differences from other periods. Therefore, the dissolution distortion index is calculated for each period, and all dissolution distortion indices are used as input to the Local Outlier Factor (LOF) algorithm for outlier detection, thus identifying outliers.

[0070] If the period corresponding to the outlier is the target period, it is determined that the data collected by the smart sensor during this performance measurement is relatively accurate and has not been subject to a large error. The target period is indeed the period during which the emulsion polymer is completely dissolved. The target period is then determined as the dissolution period of the emulsion polymer. At this time, the data acquisition time corresponding to the first element in the target conductivity subsequence of the target period is determined as the current dissolution time of the emulsion polymer, thereby completing the measurement of the dissolution performance of the emulsion polymer.

[0071] If no outlier is detected in the dissolution distortion index of all current cycles, it is determined that the target cycle in the current test process is not the cycle in which the polymer is completely dissolved. This indicates that the possibility of false detection is greater due to errors in the data collected by the smart sensor. Data collection needs to continue until an outlier is detected, and then the dissolution time test should be carried out according to the above procedure to achieve accurate determination of the dissolution performance of the emulsion polymer.

[0072] Complete dissolution of the emulsion polymer is fundamental to subsequent performance tests. Accurate performance testing can only be performed after the emulsion polymer has fully and uniformly dissolved; otherwise, testing errors will occur. Therefore, in subsequent performance testing, the degree of dissolution of the emulsion polymer in various fused liquids is measured according to the aforementioned method for determining complete dissolution. Only after complete dissolution is achieved can subsequent tests be conducted, thereby improving the accuracy of subsequent performance tests.

[0073] In addition to measuring the solubility of the emulsion polymer, the properties of the emulsion polymer can also be determined in the following ways.

[0074] Interfacial tension testing: 1. Using a rotating drop interfacial tension meter, mix the emulsion polymer with crude oil to form an emulsion. 2. Adjust the instrument parameters and start the test. 3. Using computer image analysis technology, capture the shape changes of the rotating drop in real time and automatically calculate the interfacial tension value. 4. Record the interfacial tension value to evaluate the emulsifying properties of the emulsion polymer.

[0075] Emulsion stability experiment: 1. Using a high-speed shear emulsification device, the emulsion polymer was mixed with crude oil to prepare an emulsion. 2. The prepared emulsion was placed in a centrifuge for centrifugation separation. 3. Using an intelligent data acquisition system, the stratification of the emulsion during centrifugation was monitored in real time, and parameters such as stratification time and degree of stratification were automatically recorded. 4. The stability of the emulsion was evaluated.

[0076] Salt resistance test: 1. Mix the emulsion polymer with brine of different mineralization. 2. Measure the viscosity retention and stability of the mixture. 3. Utilize an intelligent data analysis system to automatically analyze the viscosity changes of the emulsion polymer at different mineralization levels and generate salt resistance curves. 4. Evaluate the salt resistance of the emulsion polymer.

[0077] Dynamic oil displacement experiment: 1. Using a core flooding experimental apparatus, the emulsion polymer was injected into the core sample. 2. The experimental apparatus was started, and parameters such as pressure, flow rate, and recovery rate were recorded during the oil displacement process. 3. Using a real-time data acquisition and analysis system, various parameters during the oil displacement process were automatically recorded, and the performance changes of the emulsion polymer at different stages of oil displacement were analyzed using computer simulation technology. 4. The oil displacement efficiency and performance changes of the emulsion polymer were evaluated.

[0078] Comprehensive Performance Evaluation: 1. The performance of the emulsion polymer is comprehensively evaluated by combining the results of solubility tests, interfacial tension tests, emulsion stability experiments, salt resistance tests, and dynamic oil displacement experiments. 2. Using an intelligent data analysis platform, the test results are comprehensively analyzed to generate a performance evaluation report and propose optimization suggestions, providing a scientific basis for oilfield oil displacement applications.

[0079] The above testing methods are simple and easy to implement, and the required equipment and tools are all common laboratory equipment. Combined with intelligent auxiliary means, they can provide a scientific basis for oilfield flooding, improve oilfield recovery rate, and have important application value.

[0080] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0081] This application also provides a system for testing the properties of emulsion polymers suitable for oilfield flooding, such as... Figure 2 As shown, the system includes: The acquisition module 21 is used to acquire the viscosity sequence and conductivity sequence corresponding to each cycle during the dissolution process of the emulsion polymer; the viscosity sequence corresponding to each cycle includes the viscosity data of the current cycle and the previous cycles, and the conductivity sequence corresponding to each cycle includes the conductivity data of the current cycle; The determination module 22 is used to determine the emulsion solubility coefficient of a period based on the segmentation result of the viscosity sequence corresponding to each period and the difference between the viscosity sequence corresponding to the current period and the viscosity sequence corresponding to the previous period; the emulsion solubility coefficient is used to characterize whether the emulsion polymer of the current period is completely dissolved. The segmentation module 23 is used to segment the conductivity sequence of each period to obtain multiple conductivity subsequences of that period, and to perform linear analysis on the multiple conductivity subsequences of that period to obtain the slope corresponding to each conductivity subsequence of that period. The calculation module 24 is used to obtain the dissolution distortion index of a period based on the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity subsequence of the next period, the ratio of the first slope of the period to the largest slope among the slopes of other conductivity subsequences of the period; the target conductivity subsequence is the conductivity subsequence located at a preset position among multiple conductivity subsequences of each period, the first slope indicates the slope corresponding to the target conductivity subsequence of the period, and the dissolution distortion index is used to characterize the reliability of judging whether the emulsion polymer is completely dissolved based on viscosity data; The verification module 25 is used to verify the dissolution distortion index of the target period based on the dissolution distortion index of all periods, so as to determine the dissolution period of the emulsion polymer according to the verification result; the target period is selected from multiple periods based on the emulsion solubility coefficient.

[0082] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs.

[0083] Figure 3 This is a schematic diagram of the structure of an electronic device according to an example embodiment of this application. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the method described in any of the above embodiments. Figure 3 The electronic device 30 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0084] like Figure 3 As shown, the electronic device 30 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0085] Bus 33 includes a data bus, an address bus, and a control bus.

[0086] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0087] The memory 32 may also include a program tool 325 (or utility) having a set (at least one) program module 324, such program module 324 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0088] The processor 31 executes various functional applications and data processing, such as the methods provided in any of the above embodiments, by running computer programs stored in the memory 32.

[0089] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, electronic device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 36. As shown, network adapter 36 communicates with other modules of electronic device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0090] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0091] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in any of the above embodiments.

[0092] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0093] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0094] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the above embodiments.

[0095] The program code for executing the computer program product of this application can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0096] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0098] 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.

Claims

1. A method for determining the properties of emulsion polymers suitable for oilfield flooding, characterized in that, The method includes: During the dissolution process of the emulsion polymer, the viscosity sequence and conductivity sequence corresponding to each cycle are obtained; the viscosity sequence corresponding to each cycle includes the viscosity data of the current cycle and the previous cycles, and the conductivity sequence corresponding to each cycle includes the conductivity data of the current cycle; Based on the segmentation results of the viscosity sequence corresponding to each period and the difference between the viscosity sequence corresponding to this period and the viscosity sequence corresponding to the previous period, the emulsion solubility coefficient of this period is determined; the emulsion solubility coefficient is used to characterize whether the emulsion polymer of this period is completely dissolved. The conductivity sequence of each period is segmented to obtain multiple conductivity subsequences of that period. Linear analysis is then performed on each of the multiple conductivity subsequences of that period to obtain the slope corresponding to each conductivity subsequence of that period. The dissolution distortion index for a given period is obtained by comparing the difference between the mean of the target conductivity subsequence for each period and the mean of the conductivity subsequence for the next period, and by the ratio of the first slope of that period to the largest slope among the slopes of other conductivity subsequences in that period. The target conductivity subsequence is the conductivity subsequence located at a preset position among multiple conductivity subsequences in each period. The first slope indicates the slope corresponding to the target conductivity subsequence for that period. The dissolution distortion index is used to characterize the reliability of judging whether the emulsion polymer is completely dissolved based on viscosity data. Based on the dissolution distortion index of all cycles, the dissolution distortion index of the target cycle is verified, and the dissolution cycle of the emulsion polymer is determined according to the verification results; the target cycle is selected from multiple cycles based on the emulsion solubility coefficient. The step of determining the emulsion solubility coefficient for a given period based on the segmentation results of the viscosity sequence corresponding to each period and the difference between the viscosity sequence of that period and the viscosity sequence of the previous period includes: The viscosity sequence corresponding to each period is segmented to obtain multiple viscosity subsequences for that period. These multiple viscosity subsequences are then sorted in chronological order, and the last viscosity subsequence is determined as the target viscosity subsequence for that period. The emulsion solubility coefficient for each period is determined based on the dispersion among all viscosity data in the target viscosity subsequence for each period and the difference between the viscosity sequence corresponding to this period and the viscosity sequence corresponding to the previous period. The segmentation result of the viscosity sequence corresponding to each period includes the dispersion among all viscosity data in the target viscosity subsequence.

2. The method for determining the properties of emulsion polymers suitable for oilfield flooding as described in claim 1, characterized in that, The process of obtaining the viscosity sequence and conductivity sequence corresponding to each cycle during the dissolution of the emulsion polymer includes: During the dissolution process of the emulsion polymer, viscosity data is acquired once and conductivity data is acquired multiple times in each cycle, resulting in one viscosity data and multiple conductivity data for that cycle. By arranging one viscosity data point from each period and multiple viscosity data points from previous periods in chronological order, the viscosity sequence corresponding to that period is obtained. The conductivity data for each period are sorted in chronological order to obtain the conductivity sequence corresponding to that period.

3. The method for determining the properties of emulsion polymers suitable for oilfield flooding as described in claim 1, characterized in that, The step of determining the emulsion solubility coefficient for a given period based on the dispersion among all viscosity data in the target viscosity subsequence of each period and the difference between the viscosity sequence corresponding to that period and the viscosity sequence corresponding to the previous period includes: Calculate the dispersion among all viscosity data in the target viscosity subsequence for each period to obtain the viscosity dispersion value for that period; the dispersion can be calculated using any of the following methods: standard deviation, root mean square deviation, or mean deviation; Calculate the dispersion among all viscosity data in other viscosity subsequences of each period to obtain the dispersion of other viscosity subsequences, and normalize the viscosity discrete value of the period based on the dispersion of multiple other viscosity subsequences of the period to obtain the normalized viscosity discrete value of the period. Calculate the difference between the viscosity data of each period and the viscosity data of the previous period to obtain the first difference for that period; the viscosity sequence for each period includes the viscosity data of that period; The emulsion solubility coefficient for each period is determined based on the normalized viscosity discrete value for each period, the first difference for that period, and the number of multiple viscosity subsequences for that period.

4. The method for determining the properties of emulsion polymers suitable for oilfield flooding as described in claim 3, characterized in that, The step of determining the emulsion solubility coefficient for a given period based on the normalized viscosity discrete value for each period, the first difference for that period, and the number of multiple viscosity subsequences for that period includes: The emulsion solubility coefficient for each period is determined by multiplying the first difference of each period by the number of multiple viscosity subsequences in that period, and by the normalized viscosity discrete value of that period.

5. The method for determining the properties of emulsion polymers suitable for oilfield flooding as described in claim 1, characterized in that, The conductivity sequence for each period is segmented to obtain multiple conductivity subsequences for that period. Linear analysis is then performed on each of these subsequences to obtain the slope corresponding to each subsequence. This includes: The conductivity sequence of each period is divided into multiple conductivity subsequences for that period. Using the sampling time of each conductivity data point as the horizontal axis and the conductivity data corresponding to the sampling time as the vertical axis, a straight line is fitted to each conductivity subsequence, and the absolute value of the slope of the fitted line is calculated to obtain the slope of each conductivity subsequence in that period.

6. The method for determining the properties of emulsion polymers suitable for oilfield flooding as described in claim 5, characterized in that, The dissolution distortion index for a given period is obtained by calculating the difference between the mean of the target conductivity subsequence for each period and the mean of the conductivity subsequence for the next period, and the ratio of the first slope of that period to the largest slope among the slopes of other conductivity subsequences in that period. This includes: Multiple conductivity subsequences for each period are sorted in chronological order, and the last conductivity subsequence is determined as the target conductivity subsequence for that period. Calculate the average of the slopes corresponding to multiple conductivity subsequences in the next cycle of each cycle to obtain the second slope of the next cycle. Calculate the average of the first slope of each cycle and the second slope of the next cycle to obtain the average slope of that cycle. From the slopes corresponding to multiple other conductivity subsequences in each cycle, the largest slope is selected, and the ratio of the first slope of the cycle to the largest slope is calculated to obtain the normalized first slope of the cycle. The dissolution distortion index of a period is obtained by analyzing the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence of the next period, the mean slope of that period, and the normalized first slope of that period.

7. The method for determining the properties of emulsion polymers suitable for oilfield flooding as described in claim 6, characterized in that, The analysis of the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence of the next period, the mean slope of that period, and the normalized first slope of that period yields the dissolution distortion index of that period, including: Calculate the mean of multiple conductivity data in the target conductivity subsequence for each period to obtain the mean of the target conductivity subsequence for that period; Calculate the mean of multiple conductivity data in the conductivity sequence corresponding to the next cycle of each cycle to obtain the mean of the conductivity sequence corresponding to the next cycle. The absolute difference between the mean of the target conductivity subsequence for each period and the mean of the conductivity sequence for the next period is calculated to obtain the second difference for that period. Based on the difference between the means of conductivity sequences of multiple other adjacent periods, the second difference of each period is normalized to obtain the normalized second difference of that period; the difference between the mean of the target conductivity subsequence of each period and the mean of the conductivity sequence corresponding to the next period includes the normalized second difference of that period. The dissolution distortion index for each period is obtained by summing the normalized second difference, the mean slope of the period, and the normalized first slope of the period.

8. The method for determining the properties of emulsion polymers suitable for oilfield flooding as described in claim 1, characterized in that, Before verifying the dissolution distortion index of the target period based on the dissolution distortion index of all periods, and determining the dissolution time of the emulsion polymer according to the verification result, the method further includes: According to the Otsu threshold method, the emulsion solubility coefficients of multiple cycles preceding each cycle are segmented to obtain the segmentation threshold of that cycle, and the number of multiple cycles is determined as the total number of cycles for that cycle. The period to which the emulsion solubility coefficient is greater than the segmentation threshold is determined as the first period, and the period to which the emulsion solubility coefficient is less than or equal to the segmentation threshold is determined as the second period; the emulsion polymer in the first period is in a completely dissolved state, while the emulsion polymer in the second period is not completely dissolved; Calculate the sum of the number of multiple first cycles preceding each cycle and the number of multiple second cycles to obtain the segmentation and value of that cycle; In response to the fact that the sum of the values ​​of each period is not equal to the total number of periods in that period, multiple first periods are sorted in ascending order according to the emulsion solubility, and the first period that is first after sorting is determined as the next nearest period; the next nearest period indicates the next nearest period after the emulsion polymer is completely dissolved. Multiple periods are sorted in chronological order, and the period preceding the next adjacent period is determined as the target period; the target period indicates the period in which the calculated emulsion polymer is completely dissolved.

9. The method for determining the properties of emulsion polymers suitable for oilfield flooding as described in claim 8, characterized in that, The process of verifying the dissolution distortion index of the target period based on the dissolution distortion index of all periods, and determining the dissolution period of the emulsion polymer based on the verification results, includes: An anomaly detection algorithm is used to detect outliers in the dissolution distortion index for all cycles, and outliers are obtained. If the period corresponding to the outlier is the target period, then the target period is determined as the dissolution period of the emulsion polymer; the dissolution period indicates the actual period during which the emulsion polymer completely dissolves.

Images