Quantitative analytical calculation method for mud-phase shale components

Abstract

The invention provides a mud-phase shale component quantitative analysis and calculation method, which relates to the technical field of geological mineral exploration and mainly comprises the steps of sample collection and pretreatment, X-ray diffraction pattern acquisition, pattern analysis and mineral component determination, quantitative calculation based on an innovative model, result correction and evaluation and the like. Through an innovative quantitative analysis model and a strict sample processing flow, the content of various mineral components in the mud-phase shale can be more accurately determined, analysis errors are reduced, more reliable data support is provided for geological research and resource exploration, and compared with a traditional method, the method has the advantages that operation steps are simplified, analysis efficiency is improved, and the method is suitable for popularization and application. According to the method, the quantitative analysis of the mud-phase shale components can be completed in a shorter time, the progress of research and exploration is accelerated, the dependence on complex and expensive chemical analysis equipment is reduced, the experiment cost and the human input are reduced, and meanwhile the utilization efficiency of resources is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological mineral exploration, and more specifically, particularly relates to a quantitative analytical calculation method for mud phase shale components. Background Art

[0002] The quantitative analysis and calculation method of mud-phase shale composition needs to involve sample collection and pretreatment technology, X-ray diffraction technology, spectrum analysis technology, quantitative calculation model construction technology, result correction and evaluation technology, and data visualization technology;

[0003] In terms of sample collection and pretreatment, geological sampling specifications are usually used for sample collection. Crusher and grinder are used to process the samples in the laboratory and dried in an oven. For X-ray diffraction technology, manufacturers often use a specific model of X-ray diffractometer, set a certain tube voltage, tube current, radiation type, scanning range, step length and scanning time to obtain diffraction patterns. When analyzing the spectrum, professional software is used for background subtraction and smoothing, and advanced spectrum recognition algorithms are used in combination with the mineral standard diffraction spectrum database to determine the mineral composition. The construction of quantitative calculation models is often based on statistical analysis and regression analysis to determine the correction coefficient. The law of conservation of elements and multivariate statistical analysis methods will be used for result correction and evaluation. Data visualization is achieved with the help of common drawing software or libraries;

[0004] However, there are some significant problems with the existing methods for analyzing the composition of muddy shale. First, the sample collection and pretreatment process may result in insufficient sample representativeness or contamination due to improper operation, affecting the accuracy of subsequent analysis results. In the application of X-ray diffraction technology, the fixed mode of instrument parameter setting may not be able to adapt to different types of muddy shale samples, resulting in uneven quality of the spectrum. The accuracy and versatility of the spectrum analysis algorithm need to be improved, and it is sometimes difficult to accurately identify complex mineral components. The correction coefficient determination method of the quantitative calculation model may not be accurate enough, affecting the accuracy of the calculation of the relative content of mineral components. The result correction and evaluation methods may have limitations and cannot fully consider various influencing factors. In addition, the data visualization method may not be intuitive and comprehensive enough, and it is difficult to clearly display complex composition information. These problems limit the accuracy, efficiency and reliability of muddy shale composition analysis, and a more optimized analysis method is urgently needed to solve them. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a quantitative analytical calculation method for mud-phase shale components to solve the above problems.

[0006] A quantitative analytical calculation method for mud-phase shale components comprises the following steps:

[0007] S1: Collect mud shale samples and pre-treat them;

[0008] S2: using X-ray diffraction technology to obtain a diffraction pattern of the sample obtained in S1;

[0009] S3: Analyze the diffraction pattern obtained in S2 to determine the main mineral components and their characteristic peaks;

[0010] S4: Calculate the relative content of each mineral component determined in S3 based on an innovative quantitative analysis model;

[0011] Among them, the quantitative analysis model is:

[0012]

[0013] C i represents the relative content of the i-th mineral component, A i represents the characteristic peak intensity of the i-th mineral component, k i is the correction coefficient of the i-th mineral component, and n is the number of mineral components;

[0014] S5: Based on the relative content of each mineral component and the known mineral chemical composition, calculate the chemical component content of mud phase shale.

[0015] Preferably, the pretreatment step comprises grinding the sample to a specific particle size and performing a drying process.

[0016] Preferably, when determining the main mineral components and their characteristic peaks, a spectrum recognition algorithm is used in combination with a known mineral standard diffraction spectrum database for comparison.

[0017] Preferably, the correction factor k i Determined based on regression analysis method by testing and analyzing standard samples of known composition and content.

[0018] Preferably, when calculating the chemical composition content of mud phase shale, the law of conservation of elements is introduced for correction, and the correction formula is:

[0019] E corrected =E calculated ×(1+α);

[0020] Among them, E corrected is the corrected chemical composition content, E calculated is the preliminary calculated chemical composition content, and α is the correction factor determined based on experimental data.

[0021] Preferably, a multivariate statistical analysis method is used to comprehensively evaluate the multiple measurement results to improve the accuracy and reliability of the quantitative analysis.

[0022] Preferably, the quantitative analysis model is optimized using a machine learning algorithm, and the machine learning algorithm is a random forest algorithm or a support vector machine algorithm.

[0023] Preferably, an error analysis step is introduced to calculate the relative standard deviation and uncertainty of the quantitative analysis results.

[0024] Preferably, a quality control system is established to regularly calibrate and verify measuring instruments.

[0025] Preferably, the calculation results are presented in a visual manner, including drawing a component content bar graph and a mineral composition pie chart.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. Improve analysis accuracy: Through innovative quantitative analysis models and strict sample processing procedures, the content of various mineral components in mud phase shale can be determined more accurately, reducing analysis errors and providing more reliable data support for geological research and resource exploration.

[0028] 2. Shorten the analysis time: Compared with traditional methods, this method simplifies the operation steps, improves the analysis efficiency, and can complete the quantitative analysis of mud-phase shale components in a shorter time, which helps to speed up the progress of research and exploration.

[0029] 3. Reduce costs: Reduce dependence on complex and expensive chemical analysis equipment, reduce experimental costs and manpower investment, and improve resource utilization efficiency.

[0030] 4. Enhanced applicability: It can be applied to mud-phase shale samples from different regions and different layers, has wide applicability and versatility, and provides convenience for large-scale geological surveys and resource assessments.

[0031] 5. Promote scientific research: Accurate and rapid quantitative analysis of components helps to gain a deeper understanding of the formation process, sedimentary environment and oil and gas resource potential of mud shale, and promotes the research and development of geological science.

[0032] 6. Optimize resource exploration decisions: Provide more accurate geological information for the exploration of oil, gas and other resources, help decision makers more accurately assess resource reserves and distribution, and reduce exploration risks.

[0033] 7. Improve data visualization: By drawing intuitive visualization methods such as component content bar charts and mineral composition pie charts, researchers can understand and interpret analysis results more clearly and quickly, making it easier to discover potential patterns and trends.

[0034] 8. Ensure the reliability of results: Strict quality control system and verification experiments ensure the accuracy, stability and reliability of analysis results, and enhance the credibility in practical applications.

[0035] 9. Promote technological innovation: Introduce new ideas and methods into the field of mud-phase shale composition analysis, and promote continuous innovation and improvement of related technologies.

[0036] 10. Promote environmental protection: In fields such as oil and gas exploration, accurate composition analysis helps to formulate more reasonable mining and environmental protection strategies and reduce potential impacts on the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0038] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0039] See also Figure 1 The present invention provides a method for quantitative analysis and calculation of mud-phase shale components, which mainly includes the steps of sample collection and pretreatment, X-ray diffraction (XRD) spectrum acquisition, spectrum analysis and mineral composition determination, quantitative calculation based on an innovative model, and result correction and evaluation.

[0040] S1 includes sample collection and pretreatment. Sample collection is to collect representative samples from mud shale outcrops or drilling cores in different geological layers and geographical locations, ensuring that the sample collection process follows strict geological sampling specifications to ensure the integrity and representativeness of the samples. Pretreatment is to crush and grind the collected samples in the laboratory to a particle size of less than -200 mesh (about 74 microns), and use an oven to dry the ground samples at 105°C for 24 hours to remove the influence of moisture on the test results.

[0041] The instruments and parameters required for obtaining the X-ray diffraction pattern in S2 are as follows: a Bruker D8ADVANCE X-ray diffractometer is used, the tube voltage is set to 40 kV, the tube current is 40 mA, CuKα radiation (λ = 0.15406 nm), the scanning range is 5°-70°, the step length is 0.02°, and the scanning time for each step is 1 second;

[0042] The test process is: spread the pretreated sample evenly on the sample stage of the diffractometer to ensure that the surface is flat, start the diffractometer to scan, and obtain the X-ray diffraction pattern of the sample.

[0043] The spectral analysis and mineral composition determination in S3 include the following steps:

[0044] Step 1: Preliminary processing of the spectrum: background subtraction and smoothing of the acquired diffraction spectrum to improve data quality, using professional software such as Origin or Jade for processing;

[0045] Step 2: Determination of mineral composition: Using advanced pattern recognition algorithms, combined with known mineral standard diffraction pattern databases (such as PDF-4+) for comparison, the main mineral components are determined by the position, intensity and shape of characteristic peaks. Common minerals include quartz, feldspar, clay minerals (such as illite, montmorillonite, kaolinite), carbonate minerals (such as calcite, dolomite), etc.

[0046] The quantitative analysis model based on the innovation model in S4 is:

[0047]

[0048] Among them, C i represents the relative content of the i-th mineral component, A i represents the characteristic peak intensity of the i-th mineral component, k i is the correction coefficient of the i-th mineral component, and n is the number of mineral components;

[0049] The correction coefficient is determined by preparing a series of standard samples with known composition and content, covering common mud-phase shale mineral combinations, performing XRD tests on these standard samples, obtaining their diffraction patterns and characteristic peak intensities, and establishing the relationship between characteristic peak intensity and mineral content through linear regression analysis and other methods to determine the correction coefficient k. i For example, for quartz minerals, by testing and analyzing multiple standard samples, the regression equation A is obtained: 石英 =0.05C 石英 +10, then the correction factor k 石英 =0.05.

[0050] Results correction and evaluation:

[0051] (1) Correction of the law of conservation of elements: When calculating the chemical composition content of mud phase shale, the law of conservation of elements is introduced for correction. The correction formula is:

[0052] E corrected =E calculated ×(1+α);

[0053] Among them, E corrected is the corrected chemical composition content, E calculated is the preliminary calculated chemical composition content, α is the correction factor determined based on experimental data, and the correction factor α under different mineral combinations is determined by testing and calculating a large number of samples with known accurate compositions.

[0054] (2) Multivariate statistical analysis and evaluation: Multivariate statistical analysis methods (such as principal component analysis and cluster analysis) are used to comprehensively evaluate the results of multiple measurements. The measurement data of multiple samples are collected to construct a data set. The main influencing factors are extracted through principal component analysis. The samples are divided into different categories through cluster analysis to evaluate the consistency and accuracy of the measurement results.

[0055] Verification experiment:

[0056] 50 mud-phase shale samples from different regions and different layers were selected, and the composition of these samples was analyzed by the method of the present invention and traditional chemical analysis methods (such as X-ray fluorescence spectrometry analysis and inductively coupled plasma mass spectrometry analysis). The experimental data of the samples were compared as follows:

[0057]

[0058] The data obtained by the two methods were subjected to correlation analysis, and the correlation coefficients were calculated. The results showed that the method of the present invention had a significant correlation with the traditional method in the determination of quartz and feldspar contents (the correlation coefficients were 0.95 and 0.92, respectively), indicating that the method of the present invention had high accuracy. The relative standard deviation (RSD) and uncertainty of the method of the present invention were calculated. For the determination of quartz content, the RSD was 3.5%, and the uncertainty was ±2.0%; for the determination of feldspar content, the RSD was 4.2%, and the uncertainty was ±1.8%. The results showed that the method of the present invention had good precision and reliability.

[0059] By establishing a strict quality control system, regularly calibrating and maintaining the X-ray diffractometer, inserting standard samples for monitoring in each batch of sample tests, ensuring the accuracy and stability of the test results, and presenting the calculation results in a visual way, including drawing component content bar charts and mineral composition pie charts, using Python's matplotlib library or other drawing software to draw, and intuitively displaying the composition distribution of mud-phase shale;

[0060] The quantitative analysis and calculation method of mud-phase shale components of the present invention realizes accurate and rapid quantitative analysis of mud-phase shale components through innovative quantitative analysis models, strict sample processing and advanced data analysis methods. Verification experiments show that this method has good consistency with traditional methods, and has higher efficiency and accuracy, and can provide strong technical support for geological research and resource exploration.

[0061] In actual operation, we also pay special attention to the potential impact of the experimental environment on the test results. For example, temperature and humidity fluctuations in the laboratory may cause subtle changes in the performance of the X-ray diffractometer, thereby affecting the accuracy of the diffraction pattern. In order to minimize this impact, we installed a constant temperature and humidity system in the laboratory to control the ambient temperature at 25±1℃ and the relative humidity at 50±5%. In addition, before each sample test, the environmental parameters are recorded and monitored so that possible environmental interference can be corrected during data analysis.

[0062] In the sample collection process, in addition to obtaining samples from common mud shale outcrops and drilling cores, we also carried out targeted collection of mud shale in some special geological environments. For example, in areas with frequent tectonic activities, we collected samples of mud shale that was squeezed and deformed; in areas with complex sedimentary environments, we collected samples of mud shale with different sedimentary rhythms. This helps to more comprehensively verify the applicability and accuracy of the method of the present invention under various complex geological conditions.

[0063] For the steps of spectrum analysis and mineral composition determination, we further introduced deep learning algorithms in machine learning. By using a large amount of annotated diffraction spectrum data to train the deep learning model, it can automatically extract complex features in the spectrum and accurately identify the mineral composition. At the same time, in order to verify the reliability of the model, we cross-compared and verified the results of the deep learning algorithm with the traditional spectrum recognition algorithm. In practical applications, the deep learning algorithm has shown higher accuracy and efficiency in distinguishing complex and similar minerals.

[0064] In the quantitative calculation process, in addition to considering common mineral components, we also paid attention to and studied some trace minerals with important geological significance. For example, for mud-phase shale samples containing trace pyrite, apatite and other minerals, we improved the accuracy of the determination of these trace mineral contents by optimizing experimental conditions and improving quantitative analysis models. This is of great significance for a deeper understanding of the formation environment and evolution process of mud-phase shale.

[0065] In terms of result correction and evaluation, in addition to element conservation law correction and multivariate statistical analysis and evaluation, we also introduced uncertainty propagation analysis. By quantifying and propagating the errors that may be introduced in various links such as sample collection, preprocessing, instrument measurement, and data analysis, we can more comprehensively evaluate the uncertainty range of the final result. In addition, we have established an error database to count and analyze the errors generated in the analysis process of mud-phase shale samples of different types and regions, providing a reference and basis for error control for subsequent analysis of similar samples.

[0066] In terms of data visualization, in addition to using Python's matplotlib library to draw basic component content bar graphs and mineral composition pie charts, we also used professional geological drawing software such as Surfer and Grapher to draw more complex and expressive three-dimensional surface graphs and profile graphs. These graphs can more intuitively show the spatial distribution and change trend of mud-phase shale components, providing more abundant information for geological researchers.

[0067] In order to further verify the reliability and stability of the method of the present invention, we conducted a long-term follow-up experiment. The same batch of mud shale samples were tested repeatedly at different time intervals (such as one month, three months, six months, etc.), and the test results were compared and analyzed. The results show that the method of the present invention has good repeatability and stability, and the deviation of the test results is within an acceptable range.

[0068] In addition, we have also actively carried out cooperation and exchanges with relevant research institutions at home and abroad. We compared and verified the method of the present invention with other advanced mud-phase shale composition analysis methods, absorbed and learned from the advantages of other methods, and continuously improved and optimized the method of the present invention. At the same time, through collaborative research, we jointly established a large mud-phase shale composition database, covering sample data from different geological backgrounds around the world. This not only provides a broader verification basis for our method, but also provides valuable data resources for mud-phase shale research worldwide.

[0069] In actual application cases, the method of the present invention has played an important role in a large-scale shale gas exploration project. Through the rapid and accurate analysis of a large number of mud-phase shale samples, it provides a key geological basis for the stratigraphic division, reservoir evaluation and formulation of mining plans in the exploration area. Compared with traditional methods, the method of the present invention greatly shortens the analysis cycle, reduces costs, improves exploration efficiency, and provides strong technical support for the smooth advancement of the project.

[0070] In summary, the quantitative analysis and calculation method for mud-phase shale composition of the present invention has been continuously improved and optimized in practice. By comprehensively considering various influencing factors, introducing advanced technologies and algorithms, strengthening quality control and data visualization, and carrying out extensive cooperation and verification, it provides an efficient, accurate and reliable mud-phase shale composition analysis method for the field of geological research and resource exploration, which has broad application prospects and important scientific value.

[0071] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A quantitative analytical calculation method for mud-phase shale components, characterized in that: The following steps are involved: S1: Collect mud shale samples and pre-treat them; S2: using X-ray diffraction technology to obtain a diffraction pattern of the sample obtained in S1; S3: Analyze the diffraction pattern obtained in S2 to determine the main mineral components and their characteristic peaks; S4: Calculate the relative content of each mineral component determined in S3 based on an innovative quantitative analysis model; Among them, the quantitative analysis model is: C i represents the relative content of the i-th mineral component, A i represents the characteristic peak intensity of the i-th mineral component, k i is the correction coefficient of the i-th mineral component, and n is the number of mineral components; S5: Based on the relative content of each mineral component and the known mineral chemical composition, calculate the chemical component content of mud phase shale.

2. A method for quantitative analysis and calculation of mud-phase shale components as claimed in claim 1, characterized in that: The pre-treatment steps include grinding the sample to a specific particle size and drying it.

3. A method for quantitative analysis and calculation of mud-phase shale components as claimed in claim 1, characterized in that: When determining the main mineral components and their characteristic peaks, a spectrum recognition algorithm is used and compared with a known mineral standard diffraction spectrum database.

4. A method for quantitative analysis and calculation of mud-phase shale components as claimed in claim 1, characterized in that: Correction factor k i Determined based on regression analysis method by testing and analyzing standard samples of known composition and content.

5. A method for quantitative analysis and calculation of mud-phase shale components as claimed in claim 1, characterized in that: When calculating the chemical composition content of mud phase shale, the element conservation law is introduced for correction. The correction formula is: AND corrected =And calculated ×(1+α); Among them, E corrected is the corrected chemical composition content, E calculated is the preliminary calculated chemical composition content, and α is the correction factor determined based on experimental data.

6. A method for quantitative analysis and calculation of mud-phase shale components as claimed in claim 1, characterized in that: Multivariate statistical analysis methods are used to comprehensively evaluate multiple measurement results to improve the accuracy and reliability of quantitative analysis.

7. A method for quantitative analysis and calculation of mud-phase shale components as claimed in claim 1, characterized in that: The quantitative analysis model is optimized using a machine learning algorithm, wherein the machine learning algorithm is a random forest algorithm or a support vector machine algorithm.

8. A method for quantitative analysis and calculation of mud-phase shale components as claimed in claim 1, characterized in that: The error analysis step was introduced to calculate the relative standard deviation and uncertainty of the quantitative analysis results.

9. A method for quantitative analysis and calculation of mud-phase shale components as claimed in claim 1, characterized in that: Establish a quality control system to calibrate and verify measuring instruments regularly.

10. A method for quantitative analysis and calculation of mud-phase shale components as claimed in claim 1, characterized in that: The calculation results are presented in a visual way, including drawing component content bar graphs and mineral composition pie charts.

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