Middle-phase microemulsion emulsification effect determination method, system and equipment based on nuclear magnetic resonance, medium and product
By acquiring and processing signals using nuclear magnetic resonance technology and combining them with a multi-peak Gaussian fitting model, the problem of accurately identifying the emulsification effect of mid-phase microemulsions was solved, and the precise calculation of the emulsification effect was achieved.
Patent Information
- Application Number
- CN202510922358.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-04
- Publication Date
- 2025-10-28
AI Technical Summary
In complex environments, it is difficult to accurately identify the emulsification effect of middle phase microemulsions in core samples using nuclear magnetic resonance (NMR) technology. In particular, the overlapping of transverse relaxation times of different components leads to signal overlap, making it impossible to accurately determine the emulsification effect.
The spin echo intensity signal and attenuation intensity signal were obtained by nuclear magnetic resonance, and baseline correction, noise removal, normalization and inverse Laplace transform were performed to obtain the joint distribution diagram of effective diffusion coefficient and effective transverse relaxation time. The multi-peak Gaussian fitting model and least squares method were used to calculate the emulsification effect of the middle phase microemulsion.
The accuracy of the emulsification effect of the middle phase microemulsion is improved. By accurately distinguishing different phases, the accuracy of the simulated oil peak area calculation is ensured, avoiding misjudgment caused by signal overlap in traditional methods.
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Figure CN120847159A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of petroleum extraction technology, and in particular to a method, system, equipment, medium and product for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance. Background Technology
[0002] Mesophase microemulsions (i.e., Winsor III type microemulsions) are intermediate phases between the aqueous and oil phases. They possess ultra-low interfacial tension and highly efficient displacement capabilities, significantly improving displacement efficiency and sweep efficiency. This makes them crucial for enhancing oil and gas recovery and has become a key technological development direction in the field of chemical flooding. Therefore, in-depth research into the impact of Winsor III type microemulsions on crude oil emulsification processes within core samples and their oil displacement mechanisms is of paramount importance.
[0003] Nuclear Magnetic Resonance (NMR) technology, as a non-destructive detection method, has been successfully applied to the detection of parameters such as porosity, permeability, water saturation, oil saturation, and emulsion particle size in core samples. However, in some cases, when using NMR to analyze the emulsification of emulsions, transverse relaxation time is often used as an indicator. But under complex environmental conditions, the transverse relaxation times of different components may overlap, making it difficult to accurately identify the fluid type and its distribution in each phase within the core. This leads to signal overlap between phases, ultimately resulting in an inability to accurately determine the emulsification effect of the mid-phase microemulsion within the core. Summary of the Invention
[0004] The purpose of this application is to provide a method, system, equipment, medium, and product for determining the emulsification effect of a mid-phase microemulsion based on nuclear magnetic resonance, which can improve the accuracy of calculating the emulsification effect of the mid-phase microemulsion to be tested.
[0005] To achieve the above objectives, this application provides the following solution:
[0006] In a first aspect, this application provides a method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance, including:
[0007] The spin echo intensity signal and attenuation intensity signal of the middle phase microemulsion under test were obtained by nuclear magnetic resonance.
[0008] After baseline correction, noise removal, normalization, and inverse Laplace transform of the spin echo intensity signal, the joint distribution map of effective diffusion coefficient and effective transverse relaxation time is obtained.
[0009] Projecting the joint distribution map of effective diffusion coefficient and effective transverse relaxation time along the direction of effective diffusion coefficient yields the distribution curve of effective diffusion coefficient.
[0010] Based on the attenuation intensity signal, the effective diffusion coefficient and effective transverse relaxation time of different components are determined;
[0011] Based on the effective diffusion coefficients of different components, the distribution curves of the effective diffusion coefficients are solved using a multi-peak Gaussian fitting model and the least squares method to obtain the range of effective diffusion coefficients.
[0012] Within the range of the effective diffusion coefficient, the effective diffusion coefficient-effective transverse relaxation time joint distribution map is integrated to obtain the effective transverse relaxation time distribution curve;
[0013] The effective transverse relaxation time distribution curve is integrated, and the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase are determined based on the effective transverse relaxation time of different components.
[0014] The emulsification effect of the medium phase microemulsion to be tested is calculated based on the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase.
[0015] Secondly, this application provides a system for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance, comprising:
[0016] The acquisition module is used to acquire the spin echo intensity signal and attenuation intensity signal of the middle phase microemulsion under test by nuclear magnetic resonance.
[0017] The preprocessing module is used to perform baseline correction, noise removal, normalization, and inverse Laplace transform on the spin echo intensity signal to obtain the joint distribution map of effective diffusion coefficient and effective transverse relaxation time.
[0018] The projection module is used to project the joint distribution map of effective diffusion coefficient and effective transverse relaxation time in the direction of effective diffusion coefficient to obtain the distribution curve of effective diffusion coefficient.
[0019] The determination module is used to determine the effective diffusion coefficient and effective transverse relaxation time of different components based on the attenuation intensity signal.
[0020] The solution module is used to solve the effective diffusion coefficient distribution curve based on the effective diffusion coefficient of different components using a multi-peak Gaussian fitting model and the least squares method, so as to obtain the effective diffusion coefficient range.
[0021] The integration module is used to integrate the joint distribution map of effective diffusion coefficient and effective transverse relaxation time within the range of the effective diffusion coefficient to obtain the distribution curve of effective transverse relaxation time.
[0022] The area calculation module is used to integrate the effective transverse relaxation time distribution curve and determine the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase based on the effective transverse relaxation time of different components.
[0023] The emulsification effect calculation module is used to calculate the emulsification effect of the medium phase microemulsion to be tested based on the simulated oil peak area in the dual continuous phase and the simulated oil peak area in the oil phase.
[0024] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for determining the emulsification effect of mid-phase microemulsion based on nuclear magnetic resonance as described above.
[0025] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining the emulsification effect of mid-phase microemulsion based on nuclear magnetic resonance as described above.
[0026] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance described above.
[0027] According to the specific embodiments provided in this application, this application has the following technical effects:
[0028] This application obtains a joint distribution map of effective diffusion coefficient and effective transverse relaxation time by performing baseline correction, noise removal, normalization, and inverse Laplace transform on the spin echo intensity signal of the mid-phase microemulsion. In addition to the distribution map generated using only transverse relaxation time, the effective diffusion coefficient is added as another indicator to generate the joint distribution map, distinguishing the overlap of effective transverse relaxation times in the mid-phase microemulsion and improving the accurate identification of different phases (aqueous phase, bicontinuous phase, and oil phase). A multi-peak Gaussian fitting model is used to solve the distribution curve of the effective diffusion coefficient, ultimately obtaining the effective diffusion coefficient range. This ensures accurate calculation of the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase, thereby improving the accuracy of calculating the emulsification effect of the tested mid-phase microemulsion. Attached Figure Description
[0029] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments 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.
[0030] Figure 1 A flowchart illustrating a method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance, provided as an embodiment of this application;
[0031] Figure 2 A joint distribution diagram of effective diffusion coefficient and effective transverse relaxation time provided for an embodiment of this application;
[0032] Figure 3 A schematic diagram of the effective transverse relaxation time distribution curve provided in an embodiment of this application;
[0033] Figure 4 A joint distribution diagram of effective diffusion coefficient and effective transverse relaxation time provided for another embodiment of this application;
[0034] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0036] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0037] In an exemplary embodiment, Figure 1 As shown, a method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S8. Wherein:
[0038] Step S1: Obtain the spin echo intensity signal and attenuation intensity signal of the microemulsion to be tested by nuclear magnetic resonance.
[0039] Specifically, the core was vacuumed, then saturated simulated oil was added into the core, and after aging for 72 hours, water flooding was performed until the outlet water cut was 98%. Then, 0.7 PV of chemical flooding was injected at a displacement rate of 0.3 mL / min. The core after chemical flooding was tested using a MesoMR23-060H-I nuclear magnetic resonance analyzer to obtain the spin echo intensity signal and attenuation intensity signal of the middle phase microemulsion in the core.
[0040] Step S2: After noise removal, baseline correction, normalization, and inverse Laplace transform of the spin echo intensity signal, the joint distribution map of effective diffusion coefficient and effective transverse relaxation time is obtained.
[0041] Specifically, the spin echo intensity signal is first subjected to Savitzky-Golay filtering to smooth high-frequency noise and preserve peak characteristics. A second-order polynomial baseline is fitted along the time axis to the filtered spin echo intensity signal, and the baseline term is subtracted to obtain the corrected spin echo intensity signal. This corrected spin echo intensity signal is then further normalized to obtain the normalized spin echo intensity signal. Finally, the normalized spin echo intensity signal undergoes an inverse Laplace transform to obtain the joint distribution map of the effective diffusion coefficient and effective transverse relaxation time, as shown below. Figure 2 As shown, the simulated oil signal in the oil phase appears in D. e 3.03×10 -9 ms 2 s -1 and T 2e At 64.71 ms, the signal appears at position D in the dual continuous phase. e =1.79×10 - 9 ms 2 s -1 T 2e = 113.28 × 10 ms. The specific formula is as follows:
[0042] The expression for the baseline:
[0043] B(t) = a0 + a1t + a2t 2 .
[0044] Where B(t) is the baseline at relaxation time t; a0 is the baseline offset; a1t is the linear drift; a2t is the linear shift. 2 The trend is non-linear; t is the relaxation time.
[0045] The expression for the corrected spin echo intensity signal:
[0046] S c (b,t)=S(b,t)-B(t).
[0047] Among them, S c (b,t) is the corrected spin echo intensity signal when the diffusion factor is b at relaxation time t; S(b,t) is the filtered spin echo intensity signal.
[0048] The expression for the normalized spin echo intensity signal:
[0049]
[0050] in, The normalized spin echo intensity signal at relaxation time t with diffusion factor b; maxS c (b,t) represents the maximum value of the corrected spin echo intensity signal when the diffusion factor is b at relaxation time t.
[0051] According to the integral formula right Performing an inverse Laplace transform, the expression for the joint distribution map of effective diffusion coefficient and effective transverse relaxation time is obtained as follows:
[0052]
[0053] Where, f(D) e ,T 2e (L) is the joint distribution diagram of effective diffusion coefficient and effective transverse relaxation time; -1 This is the inverse Laplace transform; The normalized spin echo intensity signal is the result of normalization when the diffusion factor is b at relaxation time t.
[0054] Step S3: Project the effective diffusion coefficient-effective transverse relaxation time joint distribution map along the effective diffusion coefficient direction to obtain the effective diffusion coefficient distribution curve.
[0055] Specifically, the peak area of the NMR signal is directly proportional to the number of hydrogen nuclei in the sample, and the intensity signal of each component reflects its hydrogen nuclei concentration. The proportion of simulated oil in the oil phase and the bicontinuous phase can be quantified by the peak area of the NMR signal.
[0056] Along T 2e Extracting the effective diffusion coefficient distribution curve S(D) from slices e To determine the number of potential overlapping peaks in the projection curve along the effective diffusion coefficient direction, a qualitative analysis of the signal curve was performed using the first derivative method combined with the effective diffusion coefficients of different components in Table 1. This method is based on the S'(D) at the peak point. e If the value is 0, it is considered a potential peak. This point is the center position of the effective diffusion coefficient signal of the potential component. Combined with the effective diffusion coefficients of different components in Table 1, the number of potential peaks in the signal can be quickly identified, and the number of components in the Gaussian peak can be obtained, providing initial parameters for subsequent Gaussian function fitting.
[0057]
[0058] Among them, S'(D e The effective diffusion coefficient distribution curve S(D) is shown in Figure 1. e The peak point of ).
[0059] Step S4: Determine the effective diffusion coefficient and effective transverse relaxation time of different components based on the attenuation intensity signal. The attenuation intensity signal includes: diffusion attenuation intensity signal and relaxation attenuation intensity signal.
[0060] Further, step S4 specifically includes: determining the effective diffusion coefficient of different components based on the diffusion attenuation intensity signal; and determining the effective transverse relaxation time of different components based on the relaxation attenuation intensity signal.
[0061] Specifically, the expression for the diffusion attenuation intensity signal is:
[0062]
[0063] Where M(b) is the diffusion attenuation intensity signal when the diffusion coding factor is b; n is the number of components; A i Let b be the signal amplitude of the i-th component; b is the diffusion coding factor. Let be the effective diffusion coefficient of the i-th component.
[0064] The expression for the relaxation attenuation intensity signal is:
[0065]
[0066] Where M(t) is the relaxation decay intensity signal at relaxation time t; is the effective transverse relaxation time of the i-th component.
[0067] The simulated crude oil signal location in the bicontinuous phase occurs at an effective diffusion coefficient of 1.79 × 10⁻⁶. -9 ms 2 s -1 The simulated oil signal appears at an effective transverse relaxation time of 113.28 ms. The simulated crude oil signal location in the oil phase is at point D. e =3.03×10 -9 ms 2 s -1 and T 2e = at 64.71 ms. This is mainly because after the simulated oil enters the pores, the molecular motion, rotation, and diffusion are restricted by the pore structure, leading to enhanced dipole-dipole interactions between molecules. T 2e The value decreases. Simultaneously, within the narrow pores, molecules frequently collide with the pore walls, leading to a shortened diffusion path, D... e The value decreases. However, it is still possible to distinguish between the simulated crude oil in the oil phase and the bicontinuous phase. The T values of water, simulated oil, sodium dodecyl sulfonate, and n-butanol in the core are also present. 2e and D e The values are shown in Table 1.
[0068] Table 1
[0069]
[0070] Step S5: Based on the effective diffusion coefficients of different components, the effective diffusion coefficient distribution curves are solved using a multi-peak Gaussian fitting model and the least squares method to obtain the effective diffusion coefficient range.
[0071] Further, step S5 specifically includes: based on the effective diffusion coefficients of different components, using a multi-peak Gaussian fitting model and the least squares method to solve the distribution curve of the effective diffusion coefficient, and obtaining the peak width standard deviation; based on the peak width standard deviation, calculating the range of the effective diffusion coefficient.
[0072] Furthermore, the expression for the effective diffusion coefficient distribution curve is as follows:
[0073]
[0074] The expression for the least squares method is:
[0075]
[0076] Among them, S(D e ) represents the effective diffusion coefficient distribution curve; n represents the number of components; A i D represents the signal amplitude of the i-th component. e The effective diffusion coefficient; σ is the effective diffusion coefficient of the i-th component; i S(D) represents the standard deviation of the peak width of the i-th component; C1 and C2 are both constants; k is the number of diffusion coefficient sampling points; x is the number of diffusion coefficient sampling points; S(D) e,k D is the effective diffusion coefficient distribution curve for the k-th sampling point; e,k is the effective diffusion coefficient of the k-th sampling point.
[0077] Step S6: Within the effective diffusion coefficient range, integrate the effective diffusion coefficient-effective transverse relaxation time joint distribution map to obtain the effective transverse relaxation time distribution curve.
[0078] Furthermore, the expression for the effective transverse relaxation time distribution curve is:
[0079]
[0080] in, The effective transverse relaxation time distribution curve; T 2e For effective lateral relaxation time; This represents the maximum effective diffusion coefficient of the i-th component; The minimum effective diffusion coefficient of the i-th component is given by f(·); f(·) is the joint distribution diagram of effective diffusion coefficient and effective transverse relaxation time; D eThe effective diffusion coefficient; σ is the effective diffusion coefficient of the i-th component; i Let be the standard deviation of the peak width of the i-th component.
[0081] Specifically, the effective diffusion coefficient of the i-th component ranges from 1 to 10. to Using the joint distribution plot of effective diffusion coefficient-effective transverse relaxation time f(D) e ,T 2e )exist to D within the range e -T 2e Each column of the matrix (fixed T) 2e All D within the target area e Integrating the amplitude of the wave, we obtain the effective transverse relaxation time distribution curve, as shown in the figure. Figure 3 As shown.
[0082] Step S7: Integrate the effective transverse relaxation time distribution curve, and determine the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase based on the effective transverse relaxation time of different components.
[0083] Step S8: Calculate the emulsification effect of the medium phase microemulsion to be tested based on the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase.
[0084] Furthermore, the formula for calculating the emulsification effect of the tested middle-phase microemulsion is as follows:
[0085]
[0086] Where R represents the emulsification effect of the medium phase microemulsion to be tested; Z1 represents the simulated oil peak area in the bicontinuous phase; and Z2 represents the simulated oil peak area in the oil phase.
[0087] Specifically, by integrating the effective transverse relaxation time distribution curve, the simulated oil peak area Z1 in the bicontinuous phase and the simulated oil peak area Z2 in the oil phase are obtained respectively. The emulsification effect in the core is determined by using the calculation formula of the microemulsion emulsion effect of the middle phase to be tested.
[0088] In another embodiment, a verification experiment was conducted to verify the distribution of the mesophase microemulsion in step S2.
[0089] First, a medium-phase microemulsion was prepared, consisting of 1 mL of a mixed solution and an equal volume of simulated crude oil. After mixing the mixed solution and the simulated crude oil, the mixture was stirred at 1000 revolutions per minute (RPM) for 30 minutes and then allowed to stand for 24 hours to reach equilibrium, thus obtaining the medium-phase microemulsion. The mixed solution was prepared by adding 0.45 g of sodium dodecyl sulfate and 0.9 g of n-butanol to 100 mL of purified water, along with 30 g of salt, and stirring. The simulated crude oil was a simulated oil.
[0090] Then, NMR tests were performed on the mesophase microemulsion at the 1H core's resonance frequency of 12.99 MHz. The transverse relaxation times of the oil, mesophase, and aqueous phases in the mesophase microemulsion were measured using a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence. The effective transverse relaxation time distribution was determined by combining a multi-exponential fitting formula. The diffusion coefficient was measured using a stimulated echo (STE) pulse sequence, and the effective diffusion coefficient distribution was determined by combining a diffusion attenuation formula.
[0091] The multi-exponential fitting formula is as follows:
[0092]
[0093] Where G(t) is the transverse magnetization signal directly acquired at relaxation time t; A is the signal amplitude; t is the relaxation time; T 2e For effective lateral relaxation time.
[0094] The diffusion attenuation formula is:
[0095]
[0096] Where H(τ1,τ2) is the original signal directly output from the STE pulse sequence at diffusion time τ1 and storage time τ2; H(τ 1,min τ2) represents the baseline raw signal for diffusion measurement, used to eliminate relaxation background and highlight the contribution of diffusion effect to signal attenuation; γ is the gyromagnetic ratio of the hydrogen nucleus; G is the magnetic field gradient intensity; D e τ is the effective diffusion coefficient; τ1 is the diffusion time; τ2 is the storage time; T1 is the longitudinal relaxation time; T2 is the transverse relaxation time.
[0097] The combined distribution of effective diffusion coefficient and effective transverse relaxation time in the NMR detection results of directly configured Winsor III solution is shown in the figure below. Figure 4 As shown, the oil phase only appears in D e 3.78×10 -9 ms 2 s -1and T 2e A signal for simulated oil appeared at 73.43 ms, and no other component signals were detected. In the bicontinuous phase, water, sodium dodecyl sulfate, n-butanol, and the type of simulated oil were detected, respectively. The simulated oil signal appeared at position D in the bicontinuous phase. e =1.94×10 -9 ms 2 s -1 T 2e =122.98ms. The simulated oil signal appears at different locations in the bicontinuous phase and the oil phase. This is mainly because the bicontinuous phase is a continuous network formed by the interpenetration of the oil and water phases. When simulated oil molecules are in this structure, they may form a looser and more dynamic microenvironment due to the interface curvature or the effect of surfactants. Compared to the tightly packed molecular arrangement in the pure oil phase, the movement of simulated oil molecules in the bicontinuous phase is less restricted, resulting in a weakening of the dipole-dipole interactions between molecules, thereby prolonging T0. 2e The value. Simultaneously, the bicontinuous phase, consisting of an oil phase and an aqueous phase interpenetrating to form a continuous network structure, requires hexane molecules to frequently bypass the aqueous phase or interfacial regions during diffusion. This tortuous path significantly increases diffusion resistance, leading to D... e The decline. NMR tests in different phases demonstrated that it could be achieved through D... e -T 2e The data distinguishes the simulated oil signals in different phases.
[0098] Table 2
[0099]
[0100] The beneficial effects of the method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance proposed in this application are mainly reflected in the following aspects:
[0101] (1) By performing baseline correction, noise removal, normalization and inverse Laplace transform on the spin echo intensity signal of the mid-phase microemulsion, the effective diffusion coefficient-effective transverse relaxation time joint distribution map is obtained, which solves the problem of transverse relaxation time overlap between different components in the mid-phase microemulsion. By increasing the effective diffusion coefficient, the accuracy of identification between different phases (aqueous phase, bicontinuous phase and oil phase) in the mid-phase microemulsion is improved.
[0102] (2) The effective diffusion coefficient distribution curve is solved by using a multi-peak Gaussian fitting model, and the effective diffusion coefficient range is finally obtained. This makes the calculation of the simulated oil peak area in the dual continuous phase and the simulated oil peak area in the oil phase accurate, and realizes the quantification of the emulsification effect. By utilizing the different effective diffusion coefficients of different components, the contribution of the two can be distinguished by diffusion difference after projection, avoiding the misjudgment caused by the overlap of component signals when using the transverse relaxation time to judge the components in the traditional way, thereby improving the accuracy of the emulsification effect of the middle phase microemulsion.
[0103] Based on the same inventive concept, this application also provides a system for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance (NMR). The solution provided by this system is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more embodiments of the system for determining the emulsification effect of mid-phase microemulsions based on NMR provided below can be found in the limitations of the method for determining the emulsification effect of mid-phase microemulsions based on NMR described above, and will not be repeated here.
[0104] In one exemplary embodiment, a system for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance is provided, comprising:
[0105] The acquisition module is used to acquire the spin echo intensity signal and attenuation intensity signal of the middle phase microemulsion under test by nuclear magnetic resonance.
[0106] The preprocessing module is used to perform baseline correction, noise removal, normalization, and inverse Laplace transform on the spin echo intensity signal to obtain the joint distribution map of effective diffusion coefficient and effective transverse relaxation time.
[0107] The projection module is used to project the joint distribution map of effective diffusion coefficient and effective transverse relaxation time in the direction of effective diffusion coefficient to obtain the distribution curve of effective diffusion coefficient.
[0108] The determination module is used to determine the effective diffusion coefficient and effective transverse relaxation time of different components based on the attenuation intensity signal.
[0109] The solution module is used to solve the effective diffusion coefficient distribution curve based on the effective diffusion coefficient of different components using a multi-peak Gaussian fitting model and the least squares method, so as to obtain the effective diffusion coefficient range.
[0110] The integration module is used to integrate the effective diffusion coefficient-effective transverse relaxation time joint distribution map within the effective diffusion coefficient range to obtain the effective transverse relaxation time distribution curve.
[0111] The area calculation module is used to integrate the effective transverse relaxation time distribution curve and determine the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase based on the effective transverse relaxation time of different components.
[0112] The emulsification effect calculation module is used to calculate the emulsification effect of the medium phase microemulsion to be tested based on the simulated oil peak area in the dual continuous phase and the simulated oil peak area in the oil phase.
[0113] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores the emulsification effects of mid-phase microemulsions. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for determining the emulsification effects of mid-phase microemulsions based on nuclear magnetic resonance.
[0114] Those skilled in the art will understand that Figure 5 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0115] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0116] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0117] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0118] 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, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0119] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0120] 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.
[0121] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance, characterized in that, The method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance includes: The spin echo intensity signal and attenuation intensity signal of the middle phase microemulsion under test were obtained by nuclear magnetic resonance. After noise removal, baseline correction, normalization, and inverse Laplace transform of the spin echo intensity signal, the joint distribution map of effective diffusion coefficient and effective transverse relaxation time is obtained. Projecting the joint distribution map of effective diffusion coefficient and effective transverse relaxation time along the direction of effective diffusion coefficient yields the distribution curve of effective diffusion coefficient. Based on the attenuation intensity signal, the effective diffusion coefficient and effective transverse relaxation time of different components are determined; Based on the effective diffusion coefficients of different components, the distribution curves of the effective diffusion coefficients are solved using a multi-peak Gaussian fitting model and the least squares method to obtain the range of effective diffusion coefficients. Within the range of the effective diffusion coefficient, the effective diffusion coefficient-effective transverse relaxation time joint distribution map is integrated to obtain the effective transverse relaxation time distribution curve; The effective transverse relaxation time distribution curve is integrated, and the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase are determined based on the effective transverse relaxation time of different components. The emulsification effect of the medium phase microemulsion to be tested is calculated based on the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase.
2. The method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance according to claim 1, characterized in that, Based on the effective diffusion coefficients of different components, the distribution curves of the effective diffusion coefficients are solved using a multi-peak Gaussian fitting model and the least squares method to obtain the range of effective diffusion coefficients, specifically including: Based on the effective diffusion coefficients of different components, the distribution curves of the effective diffusion coefficients are solved using a multi-peak Gaussian fitting model and the least squares method to obtain the peak width standard deviation. Calculate the effective diffusion coefficient range based on the peak width standard deviation.
3. The method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance according to claim 1, characterized in that, The attenuation intensity signal includes: a diffusion attenuation intensity signal and a relaxation attenuation intensity signal; Based on the attenuation intensity signal, the effective diffusion coefficient and effective transverse relaxation time of different components are determined, specifically including: The effective diffusion coefficients of different components are determined based on the diffusion attenuation intensity signal. Based on the relaxation decay intensity signal, the effective transverse relaxation time of different components is determined.
4. The method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance according to claim 1, characterized in that, The expression for the effective diffusion coefficient distribution curve is: Among them, V(D) e ) represents the effective diffusion coefficient distribution curve; n represents the number of components; A i D represents the signal amplitude of the i-th component. e The effective diffusion coefficient; σ is the effective diffusion coefficient of the i-th component; i Let be the standard deviation of the peak width of the i-th component; C is a constant.
5. The method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance according to claim 1, characterized in that, The expression for the effective transverse relaxation time distribution curve is: in, The effective transverse relaxation time distribution curve; T 2e For effective lateral relaxation time; This represents the maximum effective diffusion coefficient of the i-th component; The minimum effective diffusion coefficient of the i-th component is given by f(·); f(·) is the joint distribution diagram of effective diffusion coefficient and effective transverse relaxation time; D e The effective diffusion coefficient; σ is the effective diffusion coefficient of the i-th component; i Let be the standard deviation of the peak width of the i-th component.
6. The method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance according to claim 1, characterized in that, The formula for calculating the emulsification effect of the medium-phase microemulsion to be tested is as follows: Where R represents the emulsification effect of the medium phase microemulsion to be tested; Z1 represents the simulated oil peak area in the bicontinuous phase; and Z2 represents the simulated oil peak area in the oil phase.
7. A system for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance, characterized in that, The system for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance includes: The acquisition module is used to acquire the spin echo intensity signal and attenuation intensity signal of the middle phase microemulsion under test by nuclear magnetic resonance. The preprocessing module is used to perform baseline correction, noise removal, normalization, and inverse Laplace transform on the spin echo intensity signal to obtain the joint distribution map of effective diffusion coefficient and effective transverse relaxation time. The projection module is used to project the joint distribution map of effective diffusion coefficient and effective transverse relaxation time in the direction of effective diffusion coefficient to obtain the distribution curve of effective diffusion coefficient. The determination module is used to determine the effective diffusion coefficient and effective transverse relaxation time of different components based on the attenuation intensity signal. The solution module is used to solve the effective diffusion coefficient distribution curve based on the effective diffusion coefficient of different components using a multi-peak Gaussian fitting model and the least squares method, so as to obtain the effective diffusion coefficient range. The integration module is used to integrate the joint distribution map of effective diffusion coefficient and effective transverse relaxation time within the range of the effective diffusion coefficient to obtain the distribution curve of effective transverse relaxation time. The area calculation module is used to integrate the effective transverse relaxation time distribution curve and determine the simulated oil peak area in the bicontinuous phase and the simulated oil peak area in the oil phase based on the effective transverse relaxation time of different components. The emulsification effect calculation module is used to calculate the emulsification effect of the medium phase microemulsion to be tested based on the simulated oil peak area in the dual continuous phase and the simulated oil peak area in the oil phase.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for determining the emulsification effect of a mid-phase microemulsion based on nuclear magnetic resonance as described in any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance as described in any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for determining the emulsification effect of mid-phase microemulsions based on nuclear magnetic resonance as described in any one of claims 1-6.