Metal ion tracer quantitative evaluation method for low adsorption shale oil reservoirs

Abstract

The application relates to the technical field of oil and gas field development, in particular to a metal ion tracer quantitative evaluation method for low-adsorption shale oil reservoirs, which comprises the following steps: obtaining fracturing flowback fluid, diluting the fracturing flowback fluid by different multiples to obtain multiple samples and ion mass spectra thereof, selecting one sample with the highest dilution multiple as a control sample, and taking the remaining samples as detection samples; extracting target spectral peaks corresponding to each target metal ion; quantifying mass spectrum distortion values, ionization response imbalance degrees, matrix interference degrees and contribution weights of each detection sample; determining the original content of each target metal ion in each detection sample; and weighting the original content by using the contribution weight to obtain the final content of each target metal ion in the fracturing flowback fluid, so that quantitative evaluation of the metal ion tracer in the low-adsorption shale oil reservoir is realized. The application improves the precision and reliability of quantitative evaluation of the metal ion tracer.

Description

Technical Field

[0001] This application relates to the field of oil and gas field development technology, specifically to a quantitative evaluation method for metal ion tracers for low-adsorption shale oil reservoirs. Background Technology

[0002] In the process of fracturing low-adsorption shale oil reservoirs, rare earth elements are usually added to the fracturing fluid as metal ion tracers to evaluate the fracturing effect and fracture connectivity. The distribution characteristics of the fracturing fracture network are inverted by detecting the content of metal ions in the flowback fluid.

[0003] Currently, inductively coupled plasma mass spectrometry (ICP-MS) is commonly used to quantitatively assess the content of metal ion tracers. However, shale oil reservoirs typically have extremely high formation water salinity, and the flowback fluids contain a large number of easily ionizable matrix elements. These matrix elements preferentially ionize in plasma, generating a massive amount of matrix ions, which in turn causes severe matrix interference. This complex matrix interference not only promotes the formation of polyatomic ions by matrix elements, causing mass overlap with metal ions and resulting in abnormal distortion of mass spectrometry peaks, but also triggers strong ionization competition and space charge effects, thereby inhibiting the transmission efficiency of metal ions. This leads to a severe imbalance and response deviation in the response signals between different metal ions, severely limiting the accuracy of ICP-MS in the quantitative assessment of metal ion tracers. Summary of the Invention

[0004] To address the aforementioned technical challenges, a quantitative evaluation method for metal ion tracers in low-adsorption shale oil reservoirs is provided.

[0005] The solution to the technical problem presented in this application is to provide a quantitative evaluation method for metal ion tracers in low-adsorption shale oil reservoirs, comprising the following steps:

[0006] Fracturing flowback fluid from a low-adsorption shale oil reservoir injected with metal ion tracers was obtained, and multiple samples were obtained after dilution at different ratios. ICP-MS was performed on each sample to generate ion mass spectra. The sample with the highest dilution ratio was selected as the control sample, and the remaining samples were used as test samples.

[0007] The target peaks corresponding to each target metal ion in the metal ion tracer are identified and extracted from the ion mass spectrum; the morphological differences of the target peaks corresponding to the same target metal ion in the ion mass spectra of each test sample and the control sample are compared, and the mass spectrometry distortion value of each test sample is quantified.

[0008] The fluctuation of the response change of the target metal ion between the ion mass spectra of the detection sample and the control sample is evaluated, and the ionization response imbalance of each detection sample is calculated. The mass spectrometry distortion value and the ionization response imbalance are fused to determine the matrix interference of each detection sample, and the contribution weight of each detection sample is determined accordingly.

[0009] Based on the ion mass spectrometry, the original content of each target metal ion in each test sample was determined; the original content was weighted using contribution weights to obtain the final content of each target metal ion in the fracturing flowback fluid, so as to achieve quantitative evaluation of metal ion tracers in low-adsorption shale oil reservoirs.

[0010] Preferably, the process of obtaining the target spectral peak is as follows: extract all spectral peaks in the ion mass spectrum; based on the theoretical atomic mass number of each target metal ion in the metal ion tracer, traverse all spectral peaks in the ion mass spectrum, calculate the difference between the atomic mass number at each spectral peak and the theoretical atomic mass number, and select the spectral peak with a difference less than or equal to a preset threshold as the target spectral peak of the target metal ion.

[0011] Preferably, the calculation process for the mass spectrometry distortion value is as follows:

[0012] Based on the morphological distribution characteristics of the target spectral peaks in the ion mass spectrum, the peak morphology coefficient of the target spectral peaks is calculated.

[0013] The difference in peak shape coefficients between the ion mass spectra of the same target metal ion and the control sample is taken as the shape deviation.

[0014] The mass spectrometry distortion value is positively correlated with the morphological deviation.

[0015] Preferably, the calculation of the peak shape coefficient of the target spectral peak includes: extracting the peak interval corresponding to the target spectral peak from the ion mass spectrum, and integrating the response intensity within the peak interval to obtain the peak area of ​​the target spectral peak; simultaneously, extracting the response intensity corresponding to the peak point of the target spectral peak to obtain the peak height; and using the ratio of the peak area to the peak height as the peak shape coefficient of the target spectral peak.

[0016] Preferably, the process of obtaining the peak interval is as follows: in the ion mass spectrum, find the two atomic mass numbers corresponding to the response intensity dropping to the baseline level along both sides of the target peak, and define the interval contained in these two atomic mass numbers as the peak interval.

[0017] Preferably, the calculation of the ionization response imbalance of each detected sample includes: multiplying the peak area of ​​each target peak in the ion mass spectrum by the dilution factor of the sample to which the ion mass spectrum belongs, as the equivalent response of each target peak; using the ratio of the equivalent response of each target peak in the ion mass spectrum of each detected sample to the equivalent response of the target peak corresponding to the same target metal ion in the ion mass spectrum of the control sample as the response ratio; and calculating the dispersion of the response ratios of all target peaks in the ion mass spectrum of each detected sample as the ionization response imbalance of each detected sample.

[0018] Preferably, the matrix interference degree is positively correlated with the mass spectrometry distortion value and the ionization response imbalance degree.

[0019] Preferably, the contribution weight is the result of negative mapping of the matrix interference degree.

[0020] Preferably, the determination of the original content of each target metal ion in each test sample includes: for each diluted test sample, using the internal standard method to quantitatively analyze the target metal ions in its ion mass spectrum, calculating the measured concentration of each target metal ion in each test sample, multiplying it by the dilution factor of the test sample, and converting it into the original content of each target metal ion in each test sample.

[0021] Preferably, obtaining the final content of each target metal ion in the fracturing flowback fluid includes: weighting and summing the original contents of each target metal ion in all test samples based on contribution weights, and using this sum as the final content of each target metal ion in the fracturing flowback fluid.

[0022] This application has at least the following beneficial effects:

[0023] This application uses the theoretical atomic mass number of the target metal ion in the metal ion tracer for peak identification and matching. This allows for precise location of the target peak corresponding to rare earth elements in complex backflow liquid mass spectrometry backgrounds, effectively eliminating interference from irrelevant peaks and background noise generated by matrix elements and their polyatomic ions. This ensures that subsequent quantitative analysis targets the correct target ion signal, improving the accuracy of qualitative analysis. By quantifying the peak morphology distortion caused by polyatomic ion superposition interference and calculating the mass spectrometry distortion value, it can accurately capture the abnormal broadening or tailing characteristics of the target peak caused by matrix effects. This effectively reflects the degree of waveform distortion of the target metal ion under polyatomic ion superposition interference, providing a quantitative basis for matrix interference assessment in the morphological dimension. Furthermore, by assessing the ionization response imbalance of each detected sample, it accurately evaluates the overall ionization competition and space charge effects. The influence of mass spectrometry signal transmission fluctuations provides a quantitative basis for the response distribution dimension of matrix interference assessment. The matrix interference degree of each test sample is determined, and the contribution weight of each sample is accordingly determined. Its beneficial effect is that it enables cross-validation and comprehensive evaluation of two types of interference: mass spectrometry waveform distortion and ion transport imbalance. Furthermore, through a negative mapping mechanism, precise penalty and weight isolation are implemented for severely distorted mass spectrometry data. The original contents are weighted using contribution weights to obtain the final contents of each target metal ion in the fracturing flowback fluid, thereby achieving quantitative assessment of metal ion tracers in low-adsorption shale reservoirs. Its beneficial effect is that test samples with low matrix interference dominate the final quantitative results, while the influence of high-interference samples is automatically suppressed, greatly improving the accuracy and reliability of quantitative assessment of metal ion tracers in high-salinity backgrounds. Attached Figure Description

[0024] The quantitative evaluation method for metal ion tracers for low-adsorption shale oil reservoirs of this application will be further described in detail below with reference to the accompanying drawings.

[0025] Figure 1 A flowchart illustrating the steps of a method for quantitative evaluation of metal ion tracers for low-adsorption shale oil reservoirs provided in this application embodiment;

[0026] Figure 2 A flowchart illustrating the steps of the method for obtaining mass spectrometry distortion values ​​provided in this application embodiment. Detailed Implementation

[0027] The following, in conjunction with the accompanying drawings and embodiments, provides a more detailed description of the quantitative evaluation method for metal ion tracers for low-adsorption shale oil reservoirs proposed in this application.

[0028] Please see Figure 1 The diagram illustrates a flowchart of a method for quantitative evaluation of metal ion tracers for low-adsorption shale oil reservoirs according to an embodiment of this application. The method includes the following steps:

[0029] Step 1: Obtain the fracturing flowback fluid of a low-adsorption shale oil reservoir injected with metal ion tracers, dilute it by different factors to obtain multiple samples, perform ICP-MS detection on each sample to generate ion mass spectra, select the sample with the highest dilution factor as the control sample, and use the remaining samples as test samples.

[0030] Step 2: Identify and extract the target peaks corresponding to each target metal ion in the metal ion tracer from the ion mass spectrum; compare the morphological differences of the target peaks corresponding to the same target metal ion in the ion mass spectra of each detection sample and the control sample, and quantify the mass spectrometry distortion value of each detection sample.

[0031] Step 3: Evaluate the fluctuation of the response changes of the target metal ion between the ion mass spectra of the detection sample and the control sample, and calculate the ionization response imbalance of each detection sample; combine the mass spectrometry distortion value and the ionization response imbalance to determine the matrix interference of each detection sample, and determine the contribution weight of each detection sample accordingly.

[0032] Step 4: Based on the ion mass spectrum, determine the original content of each target metal ion in each test sample; use contribution weights to weight the original content to obtain the final content of each target metal ion in the fracturing flowback fluid, so as to achieve quantitative evaluation of metal ion tracers in low-adsorption shale oil reservoirs.

[0033] In step 1, the specific process is as follows:

[0034] During fracturing operations in low-adsorption shale oil reservoirs, tracers containing target metal ions of rare earth elements are injected into the target formation along with the fracturing fluid. After fracturing, the fracturing flowback fluid returned to the surface is collected at the wellhead. The target metal ions containing rare earth elements include lanthanum (La), cerium (Ce), praseodymium (Pr), samarium (Sm), neodymium (Nd), etc., which are used as markers to indicate the flowback path of the fracturing fluid and the connectivity of the formation after fracturing.

[0035] Since the collected fracturing flowback fluid contains both artificially injected tracers and inherent high-mineralization matrix elements from the formation, such as sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), and barium (Ba), the collected fracturing flowback fluid needs to undergo the following pretreatment to avoid interference from matrix elements on the tracer signal during subsequent mass spectrometry analysis:

[0036] Microwave digestion technology is used to digest the fracturing flowback fluid to destroy and eliminate organic matter, particulate matter or suspended impurities in the fracturing flowback fluid. Solid residues are then removed by filtration. This process aims to prevent residues from clogging the atomizer or affecting the atomization effect during subsequent inductively coupled plasma mass spectrometry (ICP-MS) detection.

[0037] The clarified fracturing flowback fluid after digestion and filtration was divided into multiple portions, and each portion was serially diluted with ultrapure water at different ratios to obtain multiple diluted samples.

[0038] Specifically, the clarified fracturing flowback fluid after digestion and filtration is divided into 5 equal parts. The first sample is diluted by a factor of 1, the second sample by a factor of 2, the third sample by a factor of 3, and the fourth and fifth samples by a factor of 4 and 5, respectively. As other implementation methods, the implementer can set the appropriate values ​​according to the actual situation.

[0039] Inductively coupled plasma mass spectrometry (ICP-MS) was used to inject and detect each sample, and ion mass spectra of each sample were generated.

[0040] It should be noted that for quantitative analysis using ICP-MS, the instrument is highly susceptible to signal drift and fluctuations caused by matrix effects during ionization and transmission. Therefore, industry standards and practical operations require the use of an internal standard method for calibration. This internal standard method involves introducing an equal amount of an internal standard element of known concentration into the sample. By monitoring fluctuations in the signal intensity of the internal standard element, the response deviation caused by changes in instrument status (such as atomization efficiency, plasma temperature, and ion lens voltage) is calibrated in real time, effectively compensating for signal gain or suppression caused by matrix effects. The internal standard element is selected from at least one of scandium (Sc), germanium (Ge), rhodium (Rh), indium (In), rhenium (Re), and bismuth (Bi). In this embodiment, scandium is selected as the internal standard element, with an atomic mass number of 45.

[0041] Therefore, an equal volume of scandium (Sc) internal standard solution was added to each diluted sample to obtain a mixed solution. This mixed solution was then thoroughly mixed on a vortex mixer to ensure that the internal standard element was evenly distributed and reached complete equilibrium in the solution. The prepared mixed solution was then transferred to a sample vial and detected online using an inductively coupled plasma mass spectrometer to generate an ion mass spectrum. In the ion mass spectrum, the horizontal axis represents the atomic mass number in amu (amu), and the vertical axis represents the response intensity in cps (cps).

[0042] Since the concentration of matrix elements in the reflux solution decreases with increasing dilution factor, the sample with a higher dilution factor is less affected by the matrix effect. Therefore, the sample with the highest dilution factor is defined as the control sample; that is, the fifth sample is defined as the control sample; and the remaining samples are defined as test samples.

[0043] Furthermore, baseline correction and median filtering are performed on the ion mass spectrum to eliminate interference from noise peaks with small response intensities. Baseline correction and median filtering are well-known techniques and will not be elaborated upon here.

[0044] Furthermore, under ideal ICP-MS detection conditions, after ionization and transport, the energy distribution of the ion beam of the same element in the mass analyzer exhibits a relatively fixed statistical law, reflected in the mass spectrum as a bell-shaped peak with an approximately Gaussian distribution. That is, there is a stable proportional relationship between the peak area and peak height, which does not change with the sample dilution factor but is determined solely by the element's own ionization and transport characteristics. However, in the flowback fluid environment of high-salinity shale oil reservoirs, the sample contains a large number of matrix elements that readily undergo polyatomic reactions (such as oxides forming Ba). These polyatomic ions overlap with the atomic mass number of the target metal ions in the mass analyzer, generating strong mass spectrometry background interference. Since the interference response of polyatomic ions typically presents as a scattered and continuous additional response near the atomic mass number of the target metal ion, this superposition effect has a relatively limited impact on the peak height center of the target spectral peak, but it significantly increases the integrated area within the entire peak range, leading to abnormal broadening or tailing of the target spectral peak and other distortions. Therefore, in step 2, the mass spectrometry distortion value is calculated. The flowchart of the method for obtaining the mass spectrometry distortion value provided in this embodiment is as follows: Figure 2 As shown, it specifically includes:

[0045] Step 201: Extract all peaks in the ion mass spectrum; based on the theoretical atomic mass number of the target metal ion in the tracer, traverse all peaks in the ion mass spectrum, calculate the difference between the atomic mass number at each peak and the theoretical atomic mass number, and select the peaks with differences less than or equal to a preset threshold as the target peaks of the target metal ion.

[0046] Specifically, the AMPD (Automatic multiscale-based peak detection) algorithm is used to extract the peaks as spectral peaks. The AMPD algorithm is a well-known technology and will not be described in detail here. Secondly, the absolute value of the difference between the atomic mass number and the theoretical atomic mass number at each spectral peak is calculated. The preset threshold is set to 0.5. As other implementation methods, implementers can set it according to the actual situation.

[0047] It should be noted that when the difference between the atomic mass number of multiple spectral peaks and the theoretical atomic mass number is less than or equal to a preset threshold, the spectral peak with the smallest difference is selected as the target spectral peak of the target metal ion; when there is no spectral peak with a difference less than or equal to the preset threshold, it is determined that the target metal ion is not detected in the test sample or the signal intensity is lower than the instrument detection limit, and the content of the target metal ion is recorded as zero or not detected, and no further calculation is performed on it.

[0048] Furthermore, since the control sample is the sample with the highest dilution factor, if there are no spectral peaks in the control sample with a difference less than or equal to a preset threshold, it is determined that the target metal ion is not detected in the control sample. At this time, the calculation process of the mass spectrometry distortion value and ionization response imbalance for the target metal ion in all test samples is directly stopped, and the matrix interference of the target metal ion in all samples is marked as unmeasurable.

[0049] Step 202: In the ion mass spectrum, find the two atomic mass numbers corresponding to the response intensity dropping to the baseline level along both sides of the target peak. Define the interval contained in these two atomic mass numbers as the peak interval.

[0050] Step 203: For the target peak in the ion mass spectrum, integrate the response intensity within its peak range to obtain the peak area of ​​the target peak; at the same time, extract the response intensity corresponding to the peak point of the target peak to obtain the peak height; and use the ratio of peak area to peak height as the peak shape coefficient of the target peak.

[0051] Step 204: The difference in peak shape coefficients of the target peak corresponding to the same target metal ion between the ion mass spectra of each test sample and the control sample is taken as the shape deviation; the mass spectrometry distortion value of each test sample is positively correlated with the shape deviation.

[0052] It should be noted that a positive correlation means that the dependent variable increases as the independent variable increases and decreases as the independent variable decreases.

[0053] Specifically, the absolute value of the difference between the peak shape coefficients of the target peaks corresponding to the same target metal ion in the ion mass spectra of each test sample and the control sample is taken as the shape deviation; the mean value of the shape deviations of all target peaks in the ion mass spectra of each test sample is taken as the mass spectrometry distortion value of each test sample.

[0054] It should be noted that the greater the morphological deviation, the more severe the peak shape deviation of the single target spectral peak of the detected sample is compared with the uninterrupted control sample, indicating that the target metal ion is severely interfered with; the greater the mass spectrometry distortion value, the more severe the matrix ion interference is on the entire detected sample, resulting in severe distortion of the mass spectrometry data and extremely low reliability of the subsequently calculated concentration.

[0055] Secondly, during ICP-MS detection, the sample is ionized to form an ion beam that is transmitted to the mass analyzer. However, due to the extremely high mineralization of shale oil reservoir fracturing flowback fluids, which contain a large number of easily ionizable matrix elements, these matrix elements generate a massive amount of matrix ions during ionization. This leads to strong ionization competition and space charge effects in the ion source and transmission lens. This matrix suppression effect not only reduces the overall transmission efficiency of target metal ions but also exhibits significant differences in the degree of suppression for target metal ions with different atomic mass numbers. Typically, the space charge effect causes ions with lower atomic mass numbers to undergo more severe off-axis scattering, resulting in greater transmission suppression compared to heavier ions.

[0056] Under ideal conditions without matrix interference, the response intensity of the target metal ions should maintain a relatively fixed ratio with the control sample after different gradient dilutions of the same fracturing flowback fluid. However, when the test sample is subjected to strong matrix ion ionization competition, the attenuation of the response intensity of the target metal ions from lower to higher atomic mass numbers is no longer uniform, resulting in a severe imbalance in the response intensity ratio between the target metal ions and the control sample. Therefore, the specific process in step 3 is as follows:

[0057] Step 301: The product of the peak area of ​​each target peak in the ion mass spectrum and the dilution factor of the sample to which the ion mass spectrum belongs is used as the equivalent response of each target peak; the ratio of the equivalent response of each target peak in the ion mass spectrum of each detected sample to the equivalent response of the target peak corresponding to the same target metal ion in the ion mass spectrum of the control sample is used as the response ratio; the dispersion of the response ratio of all target peaks in the ion mass spectrum of each detected sample is calculated as the ionization response imbalance of each detected sample.

[0058] The degree of dispersion is measured by calculating the variance of the response proportions of all target peaks in the ion mass spectrum of each detected sample.

[0059] It should be noted that, considering the different dilution factors of different samples, resulting in different ion contents after dilution, the equivalent response quantity is calculated by multiplying the corresponding dilution factors, which can eliminate the influence of different dilution factors of different detection samples. The greater the ionization response imbalance, the greater the difference in the response ratio of target metal ions with different atomic mass numbers. This reflects that the detection sample is subjected to extremely severe matrix ionization competition and space charge effect during ionization and transport, resulting in a serious imbalance in the overall ratio.

[0060] Furthermore, based on the mass spectrometry distortion value and the ionization response imbalance, the matrix interference degree is determined, specifically as follows:

[0061] Step 302: The matrix interference degree of each test sample is positively correlated with the mass spectrometry distortion value and the ionization response imbalance degree.

[0062] Specifically, the maximum value normalization method is used to normalize the mass spectrometry distortion value and ionization response imbalance of all detected samples. Based on a preset first weight and a preset second weight, the normalized mass spectrometry distortion value and the normalized ionization response imbalance are weighted and summed to obtain the matrix interference. The sum of the preset first weight and the preset second weight is 1. In this embodiment, the preset first weight is set to equal the preset second weight, both set to 0.5. In other implementation methods, the implementer can set them according to the actual situation. The maximum value normalization method is a well-known technology and will not be described in detail here. When performing the maximum value normalization process, if the maximum value of the mass spectrometry distortion value or the maximum value of the ionization response imbalance of all detected samples is 0, then to avoid dividing by zero, the normalization result of the corresponding array is directly assigned to 0.

[0063] It should be noted that the greater the matrix interference, the more severe the abnormal broadening or tailing of the target metal ion peaks during the detection process. In addition, there is a drastic imbalance in the transmission attenuation between different target metal ions. This reflects that the sample is greatly negatively affected by the matrix elements in the high-mineralization fracturing flowback fluid. The more severe the matrix effect interference, the more distorted the mass spectrometry data will be, and the lower the contribution weight should be assigned in the subsequent ion content calculation.

[0064] Step 303: Negatively map the matrix interference degree as the contribution weight of each detected sample;

[0065] The calculation process for the contribution weight is as follows:

[0066] In the formula, For the first The contribution weight of each test sample, The preset sensitivity coefficient, For the first The matrix interference of the test samples. The number of all tested samples, This represents an exponential function with the natural constant as the base; wherein, the preset sensitivity coefficient is used to adjust the influence intensity of the matrix interference on the contribution weight. In this embodiment, the value is set to 3. As for other implementation methods, the implementer can set it according to the actual situation.

[0067] Therefore, in step 4, the specific process is as follows:

[0068] Step 401: For each diluted test sample, the internal standard method is used to quantitatively analyze the target metal ions in the ion mass spectrum, calculate the measured concentration of each target metal ion in each test sample, multiply it by the dilution factor of the test sample, and convert it to obtain the original content of each target metal ion in each test sample.

[0069] Step 402: Based on the contribution weight, the original contents of each target metal ion in all the tested samples are weighted and summed to obtain the final contents of each target metal ion in the fracturing flowback fluid.

[0070] It should be noted that the internal standard method is a well-known technique and will not be elaborated upon here.

[0071] The fracturing effect of shale oil reservoirs is evaluated using the final content, specifically as follows:

[0072] After obtaining the final content of each target metal ion in the fracturing flowback fluid, the flowback rate of each target metal ion is calculated by comparing it with the initial injection amount of each target metal ion before fracturing. This is used to evaluate the fracturing effect and fluid production contribution of low-adsorption shale reservoirs. The flowback rate is a well-known technique and will not be elaborated here.

[0073] The flowback rate directly reflects the fracture connectivity and fluid production capacity of the corresponding fracturing segment. A high flowback rate for a target metal ion indicates a well-developed fracture network, strong fluid conductivity, and significant fluid production contribution in the segment where the tracer was injected. Conversely, a low flowback rate suggests potential fracture closure, poor connectivity, or significant fluid retention in the segment. This allows for a quantitative assessment of the fracturing effect in low-adsorption shale reservoirs.

Claims

1. A quantitative evaluation method for metal ion tracers in low-adsorption shale oil reservoirs, characterized in that, The method includes the following steps: Fracturing flowback fluid from a low-adsorption shale oil reservoir injected with metal ion tracers was obtained, and multiple samples were obtained after dilution at different ratios. ICP-MS was performed on each sample to generate ion mass spectra. The sample with the highest dilution ratio was selected as the control sample, and the remaining samples were used as test samples. The target peaks corresponding to each target metal ion in the metal ion tracer are identified and extracted from the ion mass spectrum; the morphological differences of the target peaks corresponding to the same target metal ion in the ion mass spectra of each test sample and the control sample are compared, and the mass spectrometry distortion value of each test sample is quantified. The fluctuation of the response change of the target metal ion between the ion mass spectra of the detection sample and the control sample is evaluated, and the ionization response imbalance of each detection sample is calculated. The mass spectrometry distortion value and the ionization response imbalance are fused to determine the matrix interference of each detection sample, and the contribution weight of each detection sample is determined accordingly. Based on the ion mass spectrometry, the original content of each target metal ion in each test sample was determined; the original content was weighted using contribution weights to obtain the final content of each target metal ion in the fracturing flowback fluid, so as to achieve quantitative evaluation of metal ion tracers in low-adsorption shale oil reservoirs. The calculation process for the mass spectrometry distortion value is as follows: Based on the morphological distribution characteristics of the target spectral peaks in the ion mass spectrum, the peak morphology coefficient of the target spectral peaks is calculated. The difference in peak shape coefficients between the ion mass spectra of the same target metal ion and the control sample is taken as the shape deviation. The mass spectrometry distortion value is positively correlated with the morphological deviation. Extract the peak range corresponding to the target peak from the ion mass spectrum, and integrate the response intensity within the peak range to obtain the peak area of ​​the target peak. The calculation of the ionization response imbalance of each detection sample includes: multiplying the peak area of ​​each target peak in the ion mass spectrum by the dilution factor of the sample to which the ion mass spectrum belongs, as the equivalent response of each target peak; the ratio of the equivalent response of each target peak in the ion mass spectrum of each detection sample to the equivalent response of the corresponding target peak of the same target metal ion in the ion mass spectrum of the control sample, as the response ratio; and calculating the dispersion of the response ratios of all target peaks in the ion mass spectrum of each detection sample, as the ionization response imbalance of each detection sample.

2. The method for quantitative evaluation of metal ion tracers for low-adsorption shale oil reservoirs as described in claim 1, characterized in that, The process of obtaining the target spectral peak is as follows: extract all spectral peaks in the ion mass spectrum; based on the theoretical atomic mass number of each target metal ion in the metal ion tracer, traverse all spectral peaks in the ion mass spectrum, calculate the difference between the atomic mass number at each spectral peak and the theoretical atomic mass number, and select the spectral peak with a difference less than or equal to a preset threshold as the target spectral peak of the target metal ion.

3. The method for quantitative evaluation of metal ion tracers for low-adsorption shale oil reservoirs as described in claim 1, characterized in that, The calculation of the peak shape coefficient of the target spectral peak includes: simultaneously extracting the response intensity corresponding to the peak point of the target spectral peak as the peak height; and using the ratio of the peak area to the peak height as the peak shape coefficient of the target spectral peak.

4. The method for quantitative evaluation of metal ion tracers for low-adsorption shale oil reservoirs as described in claim 3, characterized in that, The process of obtaining the peak interval is as follows: in the ion mass spectrum, find the two atomic mass numbers corresponding to the response intensity dropping to the baseline level along both sides of the target peak, and define the interval contained in these two atomic mass numbers as the peak interval.

5. The method for quantitative evaluation of metal ion tracers for low-adsorption shale oil reservoirs as described in claim 1, characterized in that, The matrix interference degree is positively correlated with the mass spectrometry distortion value and the ionization response imbalance degree.

6. The method for quantitative evaluation of metal ion tracers for low-adsorption shale oil reservoirs as described in claim 1, characterized in that, The contribution weight is calculated as follows: In the formula, For the first The contribution weight of each test sample, The preset sensitivity coefficient, For the first The matrix interference of the test samples. The number of all tested samples, This represents an exponential function with the natural constant as its base.

7. The method for quantitative evaluation of metal ion tracers for low-adsorption shale oil reservoirs as described in claim 1, characterized in that, The determination of the original content of each target metal ion in each test sample includes: for each diluted test sample, using the internal standard method to quantitatively analyze the target metal ions in its ion mass spectrum, calculating the measured concentration of each target metal ion in each test sample, multiplying it by the dilution factor of the test sample, and converting it into the original content of each target metal ion in each test sample.

8. The method for quantitative evaluation of metal ion tracers for low-adsorption shale oil reservoirs as described in claim 1, characterized in that, The method for obtaining the final content of each target metal ion in the fracturing flowback fluid includes: weighting and summing the original contents of each target metal ion in all test samples based on contribution weights, which is taken as the final content of each target metal ion in the fracturing flowback fluid.

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