A method and apparatus for analyzing ion content in shale formation water

Abstract

The present application relates to ion content analysis technical field, specifically to a kind of shale original formation water ion content analysis method and device.The present application first obtains the spectrum signal of low alcohol extract of shale sample and the ion chromatogram signal of ultrapure water extract;Further, the ion chromatogram signal is wavelet decomposed, the noise pollution synergy coefficient of each detail component is obtained, and then the wavelet threshold of corresponding detail component is determined and denoised, to reconstruct the ion chromatogram signal;Finally, the ion content of preset metal ion in low alcohol extract is obtained based on spectrum signal, and the ion content of the rest of ion in ultrapure water extract is obtained based on reconstructed ion chromatogram signal.The present application carries out complementary analysis through double extraction system, and at the same time, the ion chromatogram data is wavelet decomposed, the noise influence is evaluated to adaptively suppress noise, so as to reconstruct the ion chromatogram signal, improve the analysis accuracy of ion content in shale original underground water.

Description

Technical Field

[0001] This invention relates to the field of ion content analysis technology, specifically to a method and apparatus for analyzing the ion content in shale primary formation water. Background Technology

[0002] The content and ionic characteristics (especially the type and content of anions and cations) of primary formation water in shale reservoirs are important basic data for studying shale diagenesis, reservoir formation, hydrocarbon generation, hydrocarbon accumulation, and solid-liquid interface coupling. This data helps to gain a deeper understanding of the physical properties, pore structure, and reservoir capacity of shale. Therefore, accurate analysis of the ion content in primary formation water of shale is crucial.

[0003] Currently, shale samples are typically ground and extracted, and the ion content in the extract is analyzed and measured using ion chromatography. However, noise interference may occur during ion chromatography analysis, which can affect the accuracy of subsequent ion content analysis. Traditional methods generally use wavelet thresholding algorithms to process ion chromatography data to reduce the impact of noise. Traditional wavelet thresholding algorithms usually use a fixed threshold, which can lead to over-smoothing, potentially eliminating some important signal features or data fluctuations. This is especially true when the ion concentration changes are small or complex, which may result in inaccurate measurement of the ion content in the original shale groundwater and affect the reliability of the analytical results. Summary of the Invention

[0004] To address the technical problem of unsatisfactory analytical results in analyzing the ion content of raw shale groundwater, the present invention aims to provide a method and apparatus for analyzing the ion content of raw shale formation water. The specific technical solution adopted is as follows:

[0005] This invention proposes a method for analyzing the ion content in the original shale formation water, the method comprising:

[0006] Lower alcohol extract and ultrapure water extract of shale samples were obtained respectively, and the spectral signal of the lower alcohol extract and the ion chromatographic signal of the ultrapure water extract were obtained.

[0007] Wavelet decomposition is performed on the ion chromatography signal of the ultrapure water extract to obtain all detail components and approximate components of the ion chromatography signal; based on the amplitude distribution characteristics of each detail component, the noise contamination coefficient of each detail component is obtained, and combined with the amplitude distribution difference between each detail component and the other detail components, the noise contamination co-coefficient of each detail component is obtained.

[0008] Based on the noise contamination cooperability coefficient, the wavelet threshold corresponding to the detail component is determined and the corresponding detail component is denoised; based on the spectral signal, the ion content of the preset metal ion in the low alcohol extract is obtained; based on the approximate component and all denoised detail components, the ion chromatography signal is reconstructed, and the ion content of the remaining ions in the ultrapure water extract, excluding the preset metal ion, is obtained.

[0009] Furthermore, the method for obtaining the noise pollution coefficient includes:

[0010] In each of the detail components, a preset medium amplitude range is determined based on the amplitude distribution characteristics of the detail component, and the amplitude at the preset percentile is used as the detail reference amplitude; the left endpoint of the preset medium amplitude range is used as the first reference amplitude, and the right endpoint of the preset medium amplitude range is used as the second reference amplitude.

[0011] Based on the total number of amplitudes in each detail component that are within a preset medium amplitude range, and the total number of amplitudes that are lower than the first reference amplitude, the distribution characteristic coefficients of the corresponding detail components are obtained.

[0012] The noise pollution coefficient corresponding to each detail component is obtained based on the total number of amplitudes lower than the second reference amplitude in each detail component, the average amplitude, and the distribution characteristic coefficient.

[0013] Furthermore, the methods for obtaining the preset medium amplitude range include:

[0014] In each of the detailed components, the average of the maximum amplitude and the minimum amplitude is taken as the first amplitude, half of the maximum amplitude is taken as the second amplitude, and the first amplitude and the second amplitude are taken as the interval endpoints to determine a preset medium amplitude interval.

[0015] Furthermore, the method for obtaining the distribution characteristic coefficients includes:

[0016] In each of the detailed components, the sum of the total number of amplitudes within the preset medium amplitude range and the preset positive parameter is used as the numerator, the sum of the total number of amplitudes below the first reference amplitude and the preset non-zero positive parameter is used as the denominator, and the fractional ratio is used as the distribution characteristic parameter.

[0017] Further, based on the total number and mean amplitudes of amplitudes lower than the second reference amplitude in each of the detail components, and the distribution characteristic coefficients, the noise contamination coefficients corresponding to the detail components are obtained, including:

[0018] In each of the detailed components, the total number of amplitudes lower than the first reference amplitude is used as the suspected noise interference weight, and the average amplitude lower than the first reference amplitude is used as the suspected noise interference intensity. The suspected noise interference intensity is weighted using the suspected noise interference weight, and the noise interference parameter is obtained by adding a preset adjustment factor to the weighting result.

[0019] By fusing the noise interference parameters and the distribution characteristic coefficients, the noise pollution coefficient corresponding to the detail component is obtained.

[0020] Furthermore, the method for obtaining the noise pollution coordination coefficient includes:

[0021] Take any one of the aforementioned detail components as the target component, and use the remaining detail components as reference components;

[0022] Based on the relationship between the detailed reference amplitude of the target component and the second reference amplitude of each reference component, the signal-to-noise gap coefficient between the target component and each reference component is determined.

[0023] Based on the distribution characteristics of the signal-to-noise gap coefficients between the target component and all reference components, and the noise pollution coefficient of the target component, the noise pollution coefficient of the target component is obtained.

[0024] Furthermore, the method for obtaining the signal-to-noise gap coefficient includes:

[0025] When the detailed reference amplitude of the target component is less than the second reference amplitude of the reference component, the signal-to-noise gap coefficient is set to zero.

[0026] When the detail reference amplitude of the target component is greater than or equal to the second reference amplitude of the reference component, the signal-to-noise gap coefficient is set to the difference between the detail reference amplitude of the target component and the second reference amplitude of the reference component.

[0027] Furthermore, the method for obtaining the wavelet threshold and denoising the corresponding detail components includes:

[0028] For each detail component, a threshold adjustment weight is determined based on the noise contamination cooperability coefficient. The preset benchmark threshold is weighted using the threshold adjustment weight to obtain a wavelet threshold. The wavelet threshold is then used as a soft threshold to perform wavelet noise reduction on the corresponding detail component.

[0029] Furthermore, the metal ions are presumably at least potassium ions and sodium ions.

[0030] The present invention also proposes an analytical device for the ion content in shale primary formation water. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the analytical method for the ion content in shale primary formation water.

[0031] The present invention has the following beneficial effects:

[0032] This invention first obtains low-grade alcohol extract and ultrapure water extract from shale samples for complementary analysis using a dual extraction system. The spectral signals of the low-grade alcohol extract and the ion chromatography signals of the ultrapure water extract are then acquired to provide a data foundation for subsequent noise reduction analysis. Further, wavelet decomposition is performed on the ion chromatography signal of the ultrapure water extract to obtain all detail components and approximate components for subsequent noise impact assessment. Then, based on the difference between the noise and the corresponding detail features of the true chromatographic peaks, the amplitude distribution characteristics of each detail component are analyzed to obtain the noise contamination coefficient of each detail component. Combining the amplitude distribution differences between each detail component and the others, the synergistic effect of noise on different detail components is assessed, thereby obtaining the noise contamination synergistic coefficient for each detail component. Subsequently, based on the noise contamination synergistic coefficient, the wavelet threshold of the corresponding detail component is determined, and noise reduction is applied to the corresponding detail component. The ion content of a preset metal ion in the low-grade alcohol extract is obtained based on the spectral signal. The ion chromatography signal is reconstructed based on the approximate components and all noise-reduced detail components, and the ion content of other ions besides the preset metal ions in the ultrapure water extract is obtained. This invention utilizes a dual extraction system for complementary analysis, avoiding the interaction between ultrapure water and certain cations such as potassium in a single ultrapure water extraction system. + and Na + Ion exchange occurs, affecting the accuracy of ion content analysis. At the same time, wavelet decomposition is performed on the ion chromatography data. Based on the difference between the noise and the detailed features corresponding to the true chromatographic peaks, the influence of noise is evaluated and noise is adaptively suppressed, thereby reconstructing a high-quality ion chromatography signal and improving the accuracy of ion content analysis in the original shale groundwater. Attached Figure Description

[0033] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart illustrating a method for analyzing ion content in primary shale formation water, provided as an embodiment of the present invention;

[0035] Figure 2 This is a flowchart illustrating a method for obtaining the noise pollution coefficient according to an embodiment of the present invention. Detailed Implementation

[0036] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and apparatus for analyzing the ion content in shale primary formation water according to the present invention. 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.

[0037] 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 invention pertains.

[0038] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method and apparatus for analyzing the ion content in the original shale formation water provided by this invention.

[0039] Please see Figure 1 The diagram illustrates a flowchart of a method for analyzing ion content in primary shale formation water according to an embodiment of the present invention, specifically including:

[0040] Step S1: Obtain the lower alcohol extract and the ultrapure water extract of the shale sample, and obtain the spectral signal of the lower alcohol extract and the ion chromatographic signal of the ultrapure water extract.

[0041] It should be noted that shale is a dense rock with nanoscale pores and extremely low permeability, making it difficult to obtain pure, undisturbed original formation water samples through simple sampling. Therefore, this embodiment of the invention will crush and extract shale cores to indirectly analyze the ion content in the original formation water of shale.

[0042] Given that ultrapure water is an extremely strong solvent, capable of efficiently and completely extracting anions and some cations from the original formation water in shale pores; however, ultrapure water is not only a good solvent but may also participate in chemical reactions. When ultrapure water undergoes weak ionization, the ionized H+... + Hydrogen ions may undergo cation exchange reactions with clay minerals (such as montmorillonite and illite) in shale, thereby extracting some metal cations such as Na from the clay minerals. + (Sodium ions) and K + (Potassium ions), which in turn affect the ion extraction results of the original formation water in the pores;

[0043] Furthermore, considering that lower alcohols are organic solvents, their interaction with clay minerals is very weak, and they are unlikely to trigger cation exchange reactions, thus hindering the extraction of Na. + K + The content can accurately reflect the original ion content of the original formation water in the shale pores; however, lower alcohols affect the content of certain salts (such as those containing Ca). 2+ Mg 2+ Salts (often referred to as "salts") have a lower solubility than water, so the extraction efficiency may not be complete.

[0044] Based on this, this embodiment of the invention will use a dual extraction system of ultrapure water extraction and low-purity alcohol extraction to synergistically analyze the ion content in the original formation water of shale. In this embodiment of the invention, the low-purity alcohol extract and the ultrapure water extract of the shale sample are obtained respectively, and the spectral signal of the low-purity alcohol extract and the ion chromatographic signal of the ultrapure water extract are obtained.

[0045] In one embodiment of the present invention, shale cores are first extracted by liquid nitrogen freezing and pressure-controlled coring to preserve the state of the shale cores to the maximum extent, prevent the loss and changes of moisture and ions, and avoid affecting the accuracy of subsequent analysis. Two shale samples (shale cores) from the same source are respectively placed in low alcohol and ultrapure water for crushing and extraction to prepare low alcohol extract and ultrapure water extract. The low alcohol is a mixed solution of methanol and ethanol, and the mass ratio of methanol to ethanol is between 1:1 and 2:1. In this embodiment of the present invention, the value is 1:1.

[0046] It should be noted that methanol is used to dissolve precipitated salts in the original formation water. When used to analyze core samples with high original mineralization of formation water, such as marine shale, the proportion of methanol used can be appropriately increased. In this case, the mass ratio of methanol to ethanol in the lower alcohol can also be 1:1.5 or 1:2, etc.

[0047] Specifically, prepare two core crushing devices. After cleaning the inside of the crushing devices, soak the inside of the tanks in a 3% dilute hydrochloric acid solution for no less than 24 hours. After soaking, rinse them repeatedly with ultrapure water no less than 5 times, and dry them for later use. After weighing the two shale samples to determine their mass (the mass does not need to be exactly the same), put one shale sample into a crushing device containing 30ml of ultrapure water for closed crushing, and put the other shale sample into a crushing device containing 30ml of low alcohol for crushing to obtain ultrapure water preliminary extract and low alcohol preliminary extract.

[0048] After rinsing the corresponding crushing device with ultrapure water and low alcohol at least 3 times (3 times in this embodiment), prepare two clean beakers, transfer the two preliminary extracts into the clean beakers respectively, and place the two beakers in an ultrasonic generator for ultrasonic treatment for at least 2 hours (2 hours in this embodiment). During the ultrasonic treatment, the beaker containing the low alcohol preliminary extract is sealed with sealing film to prevent the extract from evaporating. Then, use qualitative filter paper to filter the ultrasonically treated ultrapure water preliminary extract and low alcohol preliminary extract respectively, and dilute the collected filtered extracts to a certain volume, such as 100 ml, in a volumetric flask and shake well to obtain the final prepared low alcohol extract and ultrapure water extract.

[0049] Considering that ion chromatography (IC) can be used for ion analysis, and that ultrapure water extracts, using ultrapure water as the solvent matrix, can stably separate ions for accurate detection of ion content; however, the organic solvents in low-grade alcohol extracts can not only damage the chromatographic column but may also change the ion retention time, affecting the analytical results; and considering that inductively coupled plasma optical emission spectrometry (ICP-OES) is more suitable for the detection of major metal elements, high-temperature plasma can stably evaporate and atomize organic solvents (alcohols) and accurately detect metal elements;

[0050] Based on this, in one embodiment of the present invention, ion chromatography is used to obtain the ion chromatographic signal of the ultrapure water extract, and inductively coupled plasma atomic emission spectrometry is used to obtain the spectral signal of the lower alcohol extract.

[0051] It should be noted that, due to the different separation mechanisms of cations and anions, the ultrapure water extract needs to be divided into two portions for ion chromatography analysis. The noise reduction methods for cation chromatography signals and anion chromatography signals are the same. Here, we will use the cation chromatography signal as an example for analysis and description. Subsequent references to ion chromatography signals and detailed components will also refer to the cation chromatography signal.

[0052] Specifically, the test parameters of the ion chromatograph are first configured as follows: a cation exchange column, such as a medium-capacity sulfonic acid cation exchange column, is used; the current range of the suppressor is 40~90mA, and 45mA is used in this embodiment; the eluent flow rate is 1.0~2.0mL / min, and 1.0mL is used in this embodiment; the data acquisition time is 30min; the column temperature is 30°C, with an error of ±0.1°C; the ion chromatography data acquisition frequency is 10Hz; the implementer can also adjust the above configuration according to the actual application; then the ion chromatography signal of the ultrapure water extract is acquired through the ion chromatography detector.

[0053] It should be noted that when there are missing values ​​in the ion chromatography signal, they can be interpolated to complete them. This is a well-known technique and will not be elaborated further.

[0054] Specifically, the lower alcohol extract was enriched by nitrogen purging, and then diluted to volume with dilute nitric acid (0.5%–2%). In this embodiment, the nitric acid concentration was 1%. Inductively coupled plasma atomic emission spectrometry (ICP-AES) was used to analyze the lower alcohol extract after dilute nitric acid treatment, obtaining spectral signals to inform subsequent analysis of potential metal ions such as Na+ in the lower alcohol extract. + K + Prepare for the ion content.

[0055] In another embodiment of the present invention, an additional portion of the lower alcohol extract can be prepared, and the water content can be detected using a gas chromatograph to analyze the ion concentration in conjunction with the ion content. Alternatively, a certain amount, such as half of the lower alcohol extract, can be taken for detection before acquiring the spectral signal. Specifically, nitrogen purging is used until nearly dry, and if oil droplets are seen, the oil is removed. After oil removal, the volume is adjusted with 1% dilute nitric acid. In other embodiments, the water content can also be detected by microcoulometric method. Known technical means will not be described in detail.

[0056] It should be noted that the preparation of the lower alcohol extract and ultrapure water extract of the above shale samples, as well as the acquisition of the spectral signal of the lower alcohol extract and the ion chromatography signal of the ultrapure water extract, are all well-known techniques and will not be described in detail here.

[0057] Step S2: Perform wavelet decomposition on the ion chromatography signal of the ultrapure water extract to obtain all detail components and approximate components of the ion chromatography signal; based on the amplitude distribution characteristics of each detail component, obtain the noise contamination coefficient of each detail component, and combine the amplitude distribution differences between each detail component and the other detail components to obtain the noise contamination co-coefficient of each detail component.

[0058] Considering that the ion chromatography detection of ultrapure water extract is often affected by conductivity detector drift, pump pulsation, temperature fluctuations, etc., which may produce baseline drift and high-frequency random noise, causing the ion chromatography signal to deviate from the true value; therefore, it is necessary to perform noise reduction and correction on the ion chromatography data before the ion content can be accurately analyzed by the ion chromatography signal.

[0059] Furthermore, considering that wavelet decomposition can recursively decompose the ion chromatography signal of ultrapure water extract multiple times to obtain different frequency band components, mainly including low-frequency and high-frequency components; the low-frequency component characterizes the main trend or outline of the ion chromatography signal, while the high-frequency component can characterize the local and abrupt details in the ion chromatography signal. These details may correspond to the edges, peaks, or noise of the chromatographic peaks, preparing for subsequent analysis and evaluation of noise interference based on the high-frequency component for adaptive noise reduction;

[0060] Based on this, the embodiments of the present invention first perform wavelet decomposition on the ion chromatography signal of the ultrapure water extract to obtain all detail components and approximate components; wherein the detail components correspond to the high-frequency part, and the approximate components correspond to the low-frequency part.

[0061] In one embodiment of the present invention, the wavelet decomposition layer number is set to 4-8, and in this embodiment it is set to 6, that is, 6 detail components and 1 approximate component are obtained; in other embodiments, the implementer may also adjust the wavelet decomposition layer number according to the actual situation, for example, perform spectral analysis on historical ion chromatography signals, evaluate the frequency distribution of noise, and then determine the appropriate wavelet decomposition layer number by combining the frequency range covered by the decomposition layer corresponding to the detail components.

[0062] It should be noted that wavelet decomposition is a well-known technique and will not be elaborated further.

[0063] Considering that real ion chromatography peaks are not as random as noise, they may produce one or more prominent detail coefficients with amplitudes much larger than the background noise at abrupt change points (such as the start and end of the peak); while noise is usually relatively discrete and random, and typically has a low detail coefficient; therefore, this embodiment of the invention will obtain the noise contamination coefficient of each detail component based on the amplitude distribution characteristics of each detail component; the noise contamination coefficient initially quantifies the interference effect of noise on the accurate analysis of ion chromatography peaks from the perspective of each detail component, preparing for subsequent noise reduction.

[0064] Preferably, in one embodiment of the present invention, the method for obtaining the noise pollution coefficient includes:

[0065] Please see Figure 2 The diagram illustrates a flowchart of a method for obtaining a noise pollution coefficient according to an embodiment of the present invention, specifically including:

[0066] Step S201: In each detail component, a preset medium amplitude range is determined based on the amplitude distribution characteristics of the detail component, and the amplitude at the preset percentile is used as the detail reference amplitude; the left endpoint of the preset medium amplitude range is used as the first reference amplitude, and the right endpoint of the preset medium amplitude range is used as the second reference amplitude.

[0067] It should be noted that the analysis method for the noise pollution coefficient of each detailed component is consistent. Here, we will use one of them as an example to illustrate the method, and will not go into detail about each one.

[0068] Since most noise has low detail coefficients (amplitude values ​​in detail components), a preset medium amplitude range can be initially defined based on the amplitude characteristics of the detail components. The left endpoint of the preset medium amplitude range is the first reference amplitude, and the right endpoint of the preset medium amplitude range is the second reference amplitude, which is equivalent to dividing the amplitude (detail coefficients) in the detail components into distributions.

[0069] Amplitudes below the first reference amplitude can be considered as low amplitude regions, where amplitudes typically correspond to the variation characteristics of real noise; amplitudes above the second reference amplitude can be considered as high amplitude regions, where amplitudes typically correspond to the variation characteristics of real ion chromatography peaks; and the preset medium amplitude range typically corresponds to the variation characteristics of low-intensity ion chromatography peaks or a small amount of high-intensity noise.

[0070] The more amplitude values ​​in the low amplitude range and the fewer amplitude values ​​in the preset medium amplitude range, the more concentrated the noise detail coefficients are, which may indicate that the ion chromatography signal is heavily contaminated by noise and is almost dominated by noise. On the other hand, the fewer amplitude values ​​in the low amplitude range and the more amplitude values ​​in the preset medium amplitude range, the more the detail coefficients in the detail components are mainly distributed in the higher value range, and the noise dominance trend is significantly weakened, indicating that the ion chromatography signal is relatively less affected by noise.

[0071] In a preferred embodiment of the present invention, considering that the maximum and minimum amplitude values ​​can often help analyze amplitude distribution characteristics, the mean of the maximum and minimum values ​​usually characterizes the distribution center of the amplitude, and half of the maximum value can often be used to characterize medium amplitude characteristics, thus a preset medium amplitude range can be determined; therefore, the method for obtaining the preset medium amplitude range includes:

[0072] In each detail component, the average of the maximum and minimum amplitudes is taken as the first amplitude, half of the maximum amplitude is taken as the second amplitude, and the first and second amplitudes are used as the interval endpoints to determine the preset medium amplitude interval.

[0073] It should be noted that the magnitudes of the first and second amplitudes need to be determined based on the actual situation. When there are negative values ​​in the detail components, the first amplitude will be smaller than the second amplitude. When the first and second amplitudes are equal, that is, when the minimum amplitude is equal to 0, the left endpoint of the preset medium amplitude range (the first reference amplitude) is set to 40% of the maximum amplitude, and the right endpoint of the preset medium amplitude range (the second reference amplitude) is set to 60% of the maximum amplitude. The implementer can also adjust it as needed.

[0074] At the same time, a detail coefficient benchmark is defined, that is, the amplitude benchmark of each detail component; specifically, the preset percentile is set to 10%, which can be adjusted by the implementer, but should not be higher than 30%; all amplitudes in the detail components are sorted in ascending order, and the amplitude at the 10th percentile of the sort is used as the detail benchmark reference amplitude to prepare for the subsequent analysis of the signal-to-noise gap coefficient.

[0075] Step S202: Based on the total number of amplitudes in each detail component that are in the preset medium amplitude range and the total number of amplitudes that are lower than the first reference amplitude, obtain the distribution characteristic coefficient of the corresponding detail component.

[0076] Since the total number of amplitudes below the first reference amplitude can be regarded as the total number of amplitudes in the low amplitude region, and the more amplitudes in the low amplitude region and the fewer amplitudes in the preset medium amplitude range, the more the detail component conforms to the characteristic of highly concentrated noise detail coefficients, which may indicate that the ion chromatography signal is severely contaminated by noise and is almost dominated by noise. Under this type of distribution characteristic in the detail component, the greater the influence of noise interference, the distribution characteristic coefficient of the detail component can be defined based on the amplitude distribution. The distribution characteristic coefficient reflects the distribution of detail coefficients (amplitudes) in the detail component. The smaller the distribution characteristic coefficient, the stronger the noise performance in the detail component.

[0077] In a preferred embodiment of the present invention, the method for obtaining the distribution characteristic coefficients includes:

[0078] In each detailed component, the sum of the total number of amplitudes within the preset medium amplitude range and the preset positive parameter is used as the numerator, the sum of the total number of amplitudes below the first reference amplitude and the preset non-zero positive parameter is used as the denominator, and the fractional ratio is used as the distribution characteristic parameter.

[0079] Specifically, the preset positive parameter ranges from 1 to N, where N is the total number of amplitudes in the detail components. In this embodiment, the preset positive parameter is set to N to prevent the numerator from being 0. The preset non-zero positive parameter ranges from 1 to 10. In this embodiment, the preset non-zero positive parameter is set to 1 to prevent the denominator from being 0. The numerator and denominator are then determined, and the ratio of the fractions is used as the distribution characteristic parameter. The smaller the numerator and the larger the denominator, the more concentrated the noise detail coefficients are in the low amplitude region, the greater the possibility of noise dominance, and the smaller the distribution characteristic.

[0080] In other embodiments of the present invention, the implementer may also directly perform negative correlation mapping on the total number of amplitudes below the first reference amplitude, for example, mapping to the exponential function exp(-x) with the natural constant e as the base, and then calculate the proportion of the total number of amplitudes in the preset medium amplitude region in the total number of amplitudes of the detail component, and multiply the mapping result by the proportion to obtain the distribution characteristic parameters.

[0081] It should be noted that both the preset positive parameter and the preset non-zero positive parameter are dimensionless parameters, so the distribution characteristic parameter is also a dimensionless parameter.

[0082] Step S203: Based on the total number of amplitudes lower than the second reference amplitude in each detail component, the average amplitude, and the distribution characteristic coefficient, obtain the noise pollution coefficient of the corresponding detail component.

[0083] Considering that the right end of the preset medium amplitude range (the second reference amplitude) can be regarded as a relatively strict noise threshold amplitude, the detail coefficients below the second reference amplitude are regarded as the detail coefficients corresponding to suspected noise to a certain extent. The more amplitudes below the second reference amplitude, the greater the suspected noise content may be. At the same time, the amplitude can reflect the intensity of the detail coefficients. The greater the average amplitude below the second reference amplitude, the greater the suspected noise intensity may be. Then, combined with the distribution characteristic coefficient, the noise pollution coefficient of the detail component is evaluated.

[0084] In a preferred embodiment of the present invention, in each detail component, the total number of amplitudes lower than the first reference amplitude is used as the suspected noise interference weight, and the average amplitude lower than the first reference amplitude is used as the suspected noise interference intensity. The suspected noise interference intensity is weighted using the suspected noise interference weight, and the weighted result is added with a preset adjustment factor to obtain the noise interference parameter. The noise interference parameter and the distribution characteristic coefficient are fused to obtain the noise pollution coefficient of the corresponding detail component.

[0085] Specifically, in the detail component, the total number of amplitudes below the first reference amplitude is counted and recorded as the suspected noise interference weight; the average amplitude below the first reference amplitude is calculated and recorded as the suspected noise interference intensity; then the suspected noise interference weight is multiplied by the suspected noise interference intensity, and the product is added to a preset adjustment factor to obtain the noise interference parameter;

[0086] The preset adjustment factor has a value range of [0.1,1]. In this embodiment, it is set to 0.1 to avoid the numerator being 0. The noise interference parameter is within a relatively strict suspected noise range (the range of amplitudes below the second reference amplitude). Most detail coefficients are regarded as the detail coefficients corresponding to suspected noise. The interference of noise is roughly quantified by combining the quantity and intensity.

[0087] Then, the distribution characteristic coefficients are negatively correlated and mapped to an exponential function exp(-x) with the natural constant e as the base. The logical relationship is adjusted, and the mapping result is multiplied and combined with the noise interference parameter to obtain the noise pollution coefficient of the corresponding detail component.

[0088] It should be noted that the unit of the suspected interference parameter is the same as the amplitude unit in the detail component, and the distribution characteristic coefficient is a dimensionless parameter, so the unit of the final noise pollution coefficient is also the same as the amplitude unit in the detail component.

[0089] Considering that noise may not exist in isolation in a single detail component, but may affect multiple adjacent frequency bands simultaneously; if a certain type of noise appears in multiple frequency bands at the same time, that is, the higher the degree of overlap of noise detail coefficients in different decomposition layers, then it may have broadband interference characteristics rather than isolated random noise; such broadband noise has a greater impact on the overall ion chromatography signal and needs to be given more attention.

[0090] Furthermore, considering that the detail reference amplitude can be regarded as the bottom line of the detail coefficient, the right end of the preset medium amplitude range (the second reference amplitude) can be regarded as a relatively strict noise threshold amplitude. When the bottom line of the detail coefficient in a certain detail component is not lower than the relatively strict noise threshold amplitude in other detail components, it indicates that the bottom line of the detail coefficient of that detail component is relatively high, the purity of the corresponding chromatographic signal is also relatively high, and the possibility of noise co-contamination with other detail components is lower. At the same time, the larger the noise contamination coefficient of the detail component, the more serious the noise on the detail component is.

[0091] Based on this, in this embodiment of the invention, after obtaining the noise pollution coefficient of each detail component based on the amplitude distribution characteristics of each detail component, the noise pollution co-contamination coefficient of each detail component is further obtained by combining the amplitude distribution differences between each detail component and the other detail components. The noise pollution co-contamination coefficient further evaluates the co-contamination of noise between different detail components by analyzing the amplitude distribution differences between different detail components, quantifies the degree of noise pollution, and prepares for subsequent noise reduction.

[0092] Preferably, in one embodiment of the present invention, for ease of analysis, any detail component is first taken as the target component, and the remaining detail components are taken as reference components; then, the analysis is described using the target component as an example, without going into detail; by changing the target component, the noise pollution cooperation coefficient of each detail component can be obtained; then, the noise cooperation effect between the target component and each reference component is evaluated to determine the signal-to-noise gap coefficient, and then the noise pollution cooperation coefficient of the target component can be evaluated by combining the noise pollution coefficient of the target component; based on this, the method for obtaining the noise pollution cooperation coefficient includes:

[0093] Take any one detail component as the target component and use the remaining detail components as reference components;

[0094] Based on the relationship between the detailed reference amplitude of the target component and the second reference amplitude of each reference component, the signal-to-noise gap coefficient between the target component and each reference component is determined.

[0095] Based on the distribution characteristics of the corresponding signal-to-noise gap coefficients between the target component and all reference components, and the noise pollution coefficient of the target component, the noise pollution coordination coefficient of the target component is obtained.

[0096] In a preferred embodiment of the present invention, considering that when the detail coefficient baseline (detail reference amplitude) in the target component is not lower than the relatively strict noise threshold amplitude (second reference amplitude) in the other reference detail components, it indicates that the detail coefficient baseline in the target component is relatively high, which indirectly reflects that it is relatively less affected by noise and has a large signal-to-noise gap coefficient with other reference components; the method for obtaining the signal-to-noise gap coefficient includes:

[0097] When the detail reference amplitude of the target component is less than the second reference amplitude of the reference component, the signal-to-noise gap coefficient is set to zero; when the detail reference amplitude of the target component is greater than or equal to the second reference amplitude of the reference component, the signal-to-noise gap coefficient is set to the difference between the detail reference amplitude of the target component and the second reference amplitude of the reference component.

[0098] Then, the distribution characteristics are represented by the mean. The signal-to-noise gap coefficients between the target component and all reference components are averaged. Then, the mean is negatively correlated and mapped by adding a division-by-zero adjustment factor to the mean and then taking the reciprocal to prevent the denominator from being 0. The value range of the division-by-zero adjustment factor is [0.001, 1]. In this embodiment, the value is 0.1. Then, the reciprocal is multiplied by the noise pollution coefficient of the target component to obtain the noise pollution coordination coefficient of the target component.

[0099] The larger the mean, the smaller the noise co-influence between the target component and the other reference components, and the smaller the reciprocal, which can appropriately weaken the noise pollution coefficient to a certain extent. Conversely, the smaller the mean, the larger the noise co-influence between the target component and the other reference components, which further increases the noise pollution coefficient to obtain a relatively larger noise pollution co-influence coefficient, so as to enhance noise suppression in the future.

[0100] It should be noted that the signal-to-noise gap coefficient is the difference between the amplitudes in the detail components, and its unit is the same as the unit of the amplitude in the detail components; since the unit of the noise pollution coefficient is also the same as the unit of the amplitude in the detail components, and the division-zero adjustment factor is a dimensionless parameter, the final calculated noise pollution coefficient is also a dimensionless parameter.

[0101] Step S3: Determine the wavelet threshold of the corresponding detail component based on the noise contamination cooperability coefficient and denoise the corresponding detail component; obtain the ion content of the preset metal ion in the low alcohol extract based on the spectral signal; reconstruct the ion chromatography signal based on the approximate component and all denoised detail components, and obtain the ion content of the remaining ions in the ultrapure water extract except for the preset metal ion.

[0102] Once the noise contamination coefficient of each detail component is determined, its corresponding wavelet threshold can be further determined for subsequent noise reduction and reconstruction of the ion chromatography signal.

[0103] Preferably, in one embodiment of the present invention, considering that the larger the noise contamination synergy coefficient, the more severe the noise contamination effect on the corresponding detail components, and the greater the degree of suppression should be; since soft thresholding has a better smoothing effect than hard thresholding, it can better preserve signal structure features and avoid excessive noise reduction filtering out some low-intensity ion chromatography signals, soft thresholding can be selected for wavelet denoising; then the method for obtaining wavelet thresholding and denoising the corresponding detail components includes:

[0104] For each detail component, the threshold adjustment weight is determined based on the noise contamination cooperability coefficient. The preset benchmark threshold is weighted using the threshold adjustment weight to obtain the wavelet threshold. The wavelet threshold is then used as a soft threshold to perform wavelet noise reduction on the corresponding detail component.

[0105] Specifically, the noise pollution synergy coefficient is linearly normalized, and the normalized value is added with a constant 1 as the threshold adjustment weight. The threshold adjustment weight is then multiplied by a preset benchmark threshold to obtain the wavelet threshold. The wavelet threshold is then used as a soft threshold to denoise the corresponding detail components to obtain the denoised detail components.

[0106] Among them, the preset benchmark threshold is ; i is the index of the detail component, and also its corresponding decomposition layer index; Let be the standard deviation of the amplitude in the i-th detail component; Let i be the length of the i-th detail component; The general threshold used in traditional wavelet thresholding denoising algorithms (existing technology); As a hierarchical attenuation factor, since the low-level detail components in wavelet decomposition mainly include high-frequency noise and subtle edges of the signal, requiring higher noise reduction, the general threshold of the corresponding detail components in the low decomposition layer is adjusted by the hierarchical attenuation factor to obtain the preset benchmark threshold.

[0107] It should be noted that linear normalization is performed across all detail components, and implementers may also use other normalization methods; wavelet threshold denoising based on soft thresholds is also a well-known technique and will not be elaborated further; in other embodiments, implementers may also use other methods such as SURE shrinkage or Bayesian shrinkage to determine the preset baseline threshold, which will not be elaborated further.

[0108] After noise reduction, the ion chromatography signal can be further reconstructed based on the approximate components and all the noise-reduced detail components.

[0109] It should be noted that the reconstruction of ion chromatography signals is a well-known technique and will not be elaborated further.

[0110] Similarly, the same operation can be performed on the anion chromatography signal to reduce noise and reconstruct it, thus obtaining the reconstructed anion chromatography signal. The specific process will not be repeated here. When obtaining the anion chromatography signal, a quaternary ammonium salt-functionalized polystyrene-divinylbenzene (PS-DVB) resin column is selected as the anion exchange column. The implementer can also adjust it according to the actual application.

[0111] Ultimately, the ion content of the preset metal ions in the low alcohol extract can be obtained based on the spectral signal, and the ion content of the remaining ions other than the preset metal ions in the ultrapure water extract can be obtained based on the reconstructed ion chromatography signal.

[0112] In a preferred embodiment of the present invention, based on the analysis of the extraction system selected in step S1, the preset metal ions include at least potassium ions and sodium ions; K is determined by spectral signal analysis of the lower alcohol extract. + and Na + To ensure the accuracy of ion content analysis, the content of various cations and anions was determined by analyzing the cation and anion chromatographic signals after noise reduction and reconstruction, respectively, to avoid ion exchange in ultrapure water.

[0113] K may also be detected in the cation chromatography signal based on noise reduction and reconstruction. + and Na + The content of [specific element] was discarded and not used as a reference; the K [value] was determined based on the spectral signal analysis of the lower alcohol extract. + and Na + The content of various cations and anions (excluding K) was determined by analyzing cation and anion chromatographic signals based on noise reduction and reconstruction. + and Na + The content of ions (excluding the content of other elements) is used as the final detection result for ion content.

[0114] It should be noted that the determination of the above-mentioned ion content is a well-known technical method and will not be elaborated further.

[0115] Based on the same inventive concept, the present invention also proposes an analytical device for the ion content in shale primary formation water. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the analytical method for the ion content in shale primary formation water described in steps S1-S3.

[0116] The device includes a shale sample collection device, an extraction device, an ion chromatography detector, and an ion chromatography analysis and processing module. The collection device is used to collect and pre-process shale cores. The extraction device includes a core crushing device, a weighing device, a beaker, an ultrasonic generator, qualitative filter paper, and a volumetric flask, used to obtain the extract from the shale sample. The ion chromatography detector is used to detect ion chromatography data in the shale sample solution. The ion chromatography analysis module performs steps S2-S3 above to denoise and reconstruct the ion chromatography signal, thereby obtaining the ion content.

[0117] In summary, this invention first obtains the spectral signal of the low-grade alcohol extract and the ion chromatography signal of the ultrapure water extract from shale samples; further, it performs wavelet decomposition on the ion chromatography signal of the ultrapure water extract, and obtains the noise contamination coefficient of each detail component based on the amplitude distribution characteristics of each detail component. Simultaneously, it obtains the noise contamination synergy coefficient of each detail component by combining the amplitude distribution differences between each detail component and the others, thereby determining the wavelet threshold for the corresponding detail component and reducing noise; finally, it obtains the ion content of a preset metal ion in the low-grade alcohol extract based on the spectral signal; and reconstructs the ion chromatography signal based on the approximate component and all denoised detail components, obtaining the ion content of other ions besides the preset metal ion in the ultrapure water extract. This invention uses a dual extraction system for complementary analysis, avoiding the mixing of ultrapure water with some cations such as K+. + and Na + Ion exchange occurs, affecting the accuracy of ion content analysis. At the same time, wavelet decomposition is performed on the ion chromatography data. Based on the difference between the noise and the detailed features corresponding to the true chromatographic peaks, the influence of noise is evaluated and noise is adaptively suppressed, thereby reconstructing a high-quality ion chromatography signal and improving the accuracy of ion content analysis in the original shale groundwater.

[0118] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] 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 analyzing the ion content in the original shale formation water, characterized in that, The method includes: Low-grade alcohol extract and ultrapure water extract of shale samples were obtained respectively, and the spectral signal of the low-grade alcohol extract and the ion chromatographic signal of the ultrapure water extract were obtained. Wavelet decomposition is performed on the ion chromatography signal of the ultrapure water extract to obtain all detail components and approximate components of the ion chromatography signal; based on the amplitude distribution characteristics of each detail component, the noise contamination coefficient of each detail component is obtained, and combined with the amplitude distribution difference between each detail component and the other detail components, the noise contamination co-coefficient of each detail component is obtained. Based on the noise contamination cooperability coefficient, the wavelet threshold corresponding to the detail component is determined and the corresponding detail component is denoised; based on the spectral signal, the ion content of the preset metal ion in the low alcohol extract is obtained; based on the approximate component and all denoised detail components, the ion chromatography signal is reconstructed, and the ion content of the remaining ions in the ultrapure water extract, excluding the preset metal ion, is obtained.

2. The method for analyzing ion content in shale primary formation water according to claim 1, characterized in that, The method for obtaining the noise pollution coefficient includes: In each of the detail components, a preset medium amplitude range is determined based on the amplitude distribution characteristics of the detail component, and the amplitude at the preset percentile is used as the detail reference amplitude; the left endpoint of the preset medium amplitude range is used as the first reference amplitude, and the right endpoint of the preset medium amplitude range is used as the second reference amplitude. Based on the total number of amplitudes in each detail component that are within a preset medium amplitude range, and the total number of amplitudes that are lower than the first reference amplitude, the distribution characteristic coefficients of the corresponding detail components are obtained. The noise pollution coefficient corresponding to each detail component is obtained based on the total number of amplitudes lower than the second reference amplitude in each detail component, the average amplitude, and the distribution characteristic coefficient.

3. The method for analyzing ion content in shale primary formation water according to claim 2, characterized in that, Methods for obtaining the preset medium amplitude range include: In each of the detailed components, the average of the maximum amplitude and the minimum amplitude is taken as the first amplitude, half of the maximum amplitude is taken as the second amplitude, and the first amplitude and the second amplitude are taken as the interval endpoints to determine a preset medium amplitude interval.

4. The method for analyzing ion content in shale primary formation water according to claim 2, characterized in that, The method for obtaining the distribution characteristic coefficients includes: In each of the detailed components, the sum of the total number of amplitudes within the preset medium amplitude range and the preset positive parameter is used as the numerator, the sum of the total number of amplitudes below the first reference amplitude and the preset non-zero positive parameter is used as the denominator, and the fractional ratio is used as the distribution characteristic parameter; wherein, the preset non-zero positive parameter takes values ​​in the range of [1, 10].

5. The method for analyzing ion content in shale primary formation water according to claim 2, characterized in that, Based on the total number and mean amplitudes of amplitudes lower than the second reference amplitude in each of the detail components, and the distribution characteristic coefficients, the noise pollution coefficients corresponding to the detail components are obtained, including: In each of the detailed components, the total number of amplitudes lower than the first reference amplitude is used as the suspected noise interference weight, and the average amplitude lower than the first reference amplitude is used as the suspected noise interference intensity. The suspected noise interference intensity is weighted using the suspected noise interference weight, and the noise interference parameter is obtained by adding a preset adjustment factor to the weighting result. By fusing the noise interference parameters and the distribution characteristic coefficients, the noise pollution coefficient corresponding to the detail component is obtained.

6. The method for analyzing ion content in shale primary formation water according to claim 2, characterized in that, The method for obtaining the noise pollution coordination coefficient includes: Take any one of the aforementioned detail components as the target component, and use the remaining detail components as reference components; Based on the relationship between the detailed reference amplitude of the target component and the second reference amplitude of each reference component, the signal-to-noise gap coefficient between the target component and each reference component is determined. Based on the distribution characteristics of the signal-to-noise gap coefficients between the target component and all reference components, and the noise pollution coefficient of the target component, the noise pollution coefficient of the target component is obtained.

7. The method for analyzing ion content in shale primary formation water according to claim 6, characterized in that, The method for obtaining the signal-to-noise gap coefficient includes: When the detailed reference amplitude of the target component is less than the second reference amplitude of the reference component, the signal-to-noise gap coefficient is set to zero. When the detail reference amplitude of the target component is greater than or equal to the second reference amplitude of the reference component, the signal-to-noise gap coefficient is set to the difference between the detail reference amplitude of the target component and the second reference amplitude of the reference component.

8. The method for analyzing ion content in shale primary formation water according to claim 1, characterized in that, The method for obtaining the wavelet threshold and denoising the corresponding detail components includes: For each detail component, a threshold adjustment weight is determined based on the noise contamination cooperability coefficient. The preset benchmark threshold is weighted using the threshold adjustment weight to obtain a wavelet threshold. The wavelet threshold is then used as a soft threshold to perform wavelet noise reduction on the corresponding detail component.

9. The method for analyzing ion content in shale primary formation water according to claim 1, characterized in that, The metal ions are assumed to include at least potassium and sodium ions.

10. An analytical device for the ion content in shale primary formation water, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for analyzing the ion content in the original shale formation water as described in any one of claims 1 to 9.

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