Elemental logging-based rock chemical compressibility evaluation method

Abstract

The application provides a rock chemistry compressibility evaluation method based on element logging, S1, element content logging curves of each element are measured by using a lithology scanning element logging instrument, and oxide content corresponding to each element is obtained by backstepping from the element content curves; S2, based on the rock chemistry index concept, the oxide content corresponding to each element obtained in S1 is substituted into a formula for numerical calculation, each rock chemistry index curve obtained by calculation is normalized respectively, and correlation analysis is performed on the field fracturing test data; S3, the rock chemistry index and the fracturing test data which have relatively strong correlation in S2 are used to determine the weight of a series of rock chemistry indexes which have relatively strong correlation by using a multiple regression method, the rock chemistry index and the weight coefficient obtained by the multiple regression are multiplied and superimposed, and a compressibility index model of the rock chemistry index based on the element logging is constructed. The compressibility index is obtained by using the element logging data, and a theoretical basis is provided for a reservoir fracturing scheme.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas exploration technology and is used for the exploration and development of shale oil and gas reservoirs in petroleum geophysics. In particular, it is a method for evaluating the compressibility of rocks based on elemental logging. Background Technology

[0002] Compressibility assessment is an important basis for measuring the degree of reservoir response to fracturing stimulation, especially in the development of shale gas reservoirs. It is of great significance for selecting the best fracturing intervals, optimizing fracturing parameters, and predicting economic benefits.

[0003] In evaluating the compressibility of shale reservoirs, commonly used evaluation indicators include geological evaluation indicators, shale volumetric fracturing evaluation indicators, and engineering technology evaluation indicators. These indicators can help determine the total geological reserves, geological "sweet spots," maturity, etc., of the reservoir, and obtain data such as the brittleness parameters of the reservoir rock, natural fractures, formation dip angle, and geostress, thereby evaluating the feasibility of forming volumetric fractures in the reservoir. In practical applications, in addition to the brittleness index, the influence of fracture toughness on fracture extension is also considered, and the concept of a compressibility index is introduced. Furthermore, some studies have comprehensively evaluated the compressibility of shale reservoirs by calculating parameters such as the effective thickness, effective porosity, total organic carbon content, and brittleness index of the shale reservoir, and combining this with fracturing construction process parameters and microseismic monitoring results. Furthermore, in the compressibility evaluation method for deep tight sandstone reservoirs, a fitting formula for fracture energy density-elastic modulus based on triaxial rock experiments was proposed. The rock brittleness index was determined using the mineral composition method and the elastic modulus-Poisson's ratio method, and the fracture development index was determined using the rock fracture criterion, comprehensively considering the influence of rock brittleness, fracture toughness, geostress environment, and the degree of natural fracture development. In the shale gas reservoir compressibility evaluation method considering the influence of fracture toughness, a critical mechanical energy release rate was introduced, and an average critical mechanical energy release rate was defined. Combined with the brittleness index, a shale gas reservoir compressibility evaluation model was established using the fracture development index as a quantitative indicator.

[0004] The existing compressibility assessment methods mainly include the following:

[0005] (1) Evaluation method for compressibility of shale oil reservoirs: This method was submitted by the Energy Industry Shale Oil Standardization Technical Committee and specifies the evaluation indicators and methods for compressibility of shale oil reservoirs. It is applicable to the evaluation of compressibility of shale oil reservoirs.

[0006] (2) A new method for evaluating the compressibility of deep tight sandstone reservoirs: Based on triaxial rock experiments, a fitting formula was established. The rock brittleness index and fracture development index were determined by the mineral composition method and the elastic modulus-Poisson ratio method. Taking into account the influence of rock brittleness, fracture toughness, geostress environment and the degree of natural fracture development, the weight of each factor was calculated by the hierarchical analysis method, and a compressibility evaluation method for deep tight sandstone was established.

[0007] (3) Shale gas reservoir compressibility evaluation method considering the influence of fracture toughness: Based on the consideration of fracture toughness, the critical mechanical energy release rate is introduced, the influence of type I and type II fracture toughness on rock fracture development is integrated, and the average critical mechanical energy release rate is defined. Combining the average critical mechanical energy release rate and the brittleness index, a shale gas reservoir compressibility evaluation model is established.

[0008] (4) A new method for evaluating the compressibility of shale oil reservoirs based on physical information constraints: By establishing a neural network model with physical information constraints, accurate prediction of rock mechanical parameters is achieved. Based on the influence of rock mechanical parameters on reservoir compressibility, a compressibility evaluation method based on brittleness index and mechanical parameters is established.

[0009] (5) Evaluation method of compressibility of mudstone and shale and its application in Jiaoshiba area: The compressibility of Jiaoshiba area is evaluated from three aspects: rock brittleness, geostress characteristics and fracture development degree. The Rickman formula combined with pre-stack seismic inversion prediction confirms that the shale brittleness index is high, reflecting that the shale is easy to fracture.

[0010] (6) Study on the compressibility evaluation method of shale reservoir in Weiyuan area of ​​Sichuan: By calculating parameters such as effective thickness, effective porosity, total organic carbon content, and brittleness index of shale reservoir, and combining data such as fracturing construction process parameters and microseismic monitoring results, the compressibility of Longmaxi Formation shale reservoir in Weiyuan area of ​​Sichuan is comprehensively evaluated.

[0011] The above methods have low adaptability to special geological conditions, fail to reflect the stratigraphic sedimentary cycle sequence of the block, and cannot clearly reveal the boundary between stratigraphic sections. Summary of the Invention

[0012] The purpose of this invention is to address the aforementioned problems in existing technologies by proposing a rock chemical compressibility assessment method based on elemental logging data to determine reservoir compressibility.

[0013] The objective of this invention can be achieved through the following technical solutions:

[0014] A method for assessing rock chemical compressibility based on elemental logging includes the following steps:

[0015] S1. Preparation of elemental logging data: The elemental logging curves of each element are obtained by using lithological scanning elemental logging instruments, and the oxide content of each element is deduced from the elemental content curves.

[0016] S2. Using the oxide contents of each element obtained in S1, numerical calculations are performed based on the concept of rock chemical indices, including at least one of the following: calcium-alkali index, combination index, alkalinity ratio, differentiation index, consolidation index, felsic index, magnesium-iron index, Larssen index, degree of oxidation and oxidation rate, magnesium-iron ratio, and weathering index. The calculated rock chemical index curves are normalized and correlated with field fracturing test data. A correlation greater than 0.8 (R²) is required. 2 >0.8) is a rock chemical index that is strongly correlated with the compressibility index;

[0017] S3. Using the petrochemical indices and fracturing test data from S2 that are strongly correlated with the compressibility index, the weights of a series of strongly correlated petrochemical indices are determined using multiple regression. Multiple regression analysis is a statistical method used to study the relationship between multiple independent variables (explanatory variables) and a dependent variable (explained variable). In multiple regression, weights typically refer to regression coefficients, which represent the degree of influence of the independent variables on the dependent variable. The following is the calculation process for weights (regression coefficients) in multiple linear regression:

[0018] (1) Establishing the model:

[0019] Determine the form of the model, i.e., the dependent variable Y and multiple independent variables X1, X2, ..., Xn. k The relationship between them. A standard multiple linear regression model can be expressed as:

[0020] Y = β0 + β1X1 + β2X2 + ... + β k X k +ε

[0021] Where β0 is the intercept term, β1,β2,...,β k ε is the regression coefficient, and ε is the error term.

[0022] (2) Data collection:

[0023] Organize the dataset, including data for the dependent variable and all independent variables. Here, the dependent variable is the fracturing test data, and the independent variable is the rock chemical index.

[0024] (3) Estimate the regression coefficients:

[0025] Ordinary Least Squares (OLS) is used to estimate the regression coefficients. The goal of OLS is to minimize the sum of squares of the error terms, i.e.:

[0026]

[0027] Where n is the number of samples, Y i and X ij These are the values ​​of the dependent and independent variables for the i-th observation, respectively.

[0028] (4) Solve the normal equation:

[0029] By differentiating the objective function described above and setting the derivative to zero, we can obtain a set of normal equations. These equations can be expressed as:

[0030] X T Xβ=X T Y

[0031] Where X is the design matrix, with each row corresponding to an observation and each column corresponding to an independent variable (the first column is 1 to represent the intercept term), Y is the vector of dependent variables, and β is the vector of regression coefficients.

[0032] (5) Calculate the regression coefficients:

[0033] If X T X is invertible, and the regression coefficients can be obtained by solving a system of linear equations:

[0034] β=(X T X) -1 X T Y

[0035] This step can be achieved through numerical methods, such as Gaussian elimination or by using statistical software packages.

[0036] (6) Model validation:

[0037] After obtaining the regression coefficients, statistical tests of the model are required, including significance tests of the coefficients and goodness-of-fit tests of the model, in order to evaluate the effectiveness of the model and the reliability of the coefficients.

[0038] (7) Model Application:

[0039] Once the model is validated as effective, it can be used for prediction or further analysis.

[0040] This process can be completed directly using the built-in modules of statistical software (such as R, Python's statsmodels or scikit-learn library, SPSS, EXCEL, etc.).

[0041] By multiplying and summing the rock chemical index with the weight coefficients obtained from the above multiple regression, a compressibility index model based on elemental logging of rock chemical indexes is constructed:

[0042]

[0043] In the above formula, FI represents the compressibility index based on elemental logging petrochemical indices, which is dimensionless; S i (i = 1, 2, 3, ..., n) are highly correlated petrochemical indices, dimensionless; W i (i = 1, 2, 3, ..., n) are the weight coefficients of each rock chemical index obtained by the analytic hierarchy process, which are dimensionless; n is the number of rock chemical indices.

[0044] Preferably, the specific back-calculation process for obtaining the oxide content from the elemental content curve in step S1 includes the following steps:

[0045] (1) Determine the chemical formula of the oxide: Based on the element types obtained from element logging, assume that the chemical formula of the oxide formed by this element is EO. x Where E is the element, O is oxygen, and x is the number of oxygen atoms;

[0046] (2) Calculate the relative molecular mass of the oxide: EO x relative molecular mass Add the relative atomic masses of x oxygen atoms to the relative atomic mass of E;

[0047] (3) Calculate the molar ratio of elements in oxides: Since each EO x The molecule contains 1 E atom and x oxygen atoms, so the E atom and EO x The molar ratio is 1 / (1+x);

[0048] (4) Given the mass percentage of an element: Let the mass percentage of element E be w. E ;

[0049] (5) Calculate the total mass of the oxides: Let the total mass of the sample be M. total Then the mass m of element E E For w E ×M total ;

[0050] (6) Calculate the mass of the oxide based on the mass of the element: The mass of the oxide is calculated from the mass of element E in the oxide EO. x The molar ratio in the middle is used to calculate EO. x quality

[0051] (7) Calculate the mass percentage of the oxide:

[0052] Based on the above steps, the mass percentage of an oxide can be calculated by back-calculating the known mass percentage of an element.

[0053] Preferably, in step S2, the rock chemical index calculation steps are as follows:

[0054] (1) Calcium-Base Index (CA)

[0055] The curves of (Na2O+K2O) versus SiO2 and CaO versus SiO2 should intersect or be extended to intersect. The SiO2 value corresponding to the foot of the perpendicular on the horizontal axis of the intersection point is called the calcium-alkali index, or the Peacock index, denoted by the symbol CA. There is only one CA for a rock assemblage.

[0056] (2) Combination index (σ)

[0057] σ=(Na2O+K2O) 2 / (SiO2-43) (where the oxide is a mass percentage)

[0058] Among them, Na2O, K2O, and SiO2 represent the contents of sodium oxide, potassium oxide, and silicon dioxide, respectively;

[0059] (3) Alkalinity Ratio (AR)

[0060]

[0061] If the SiO2 content of the rock is greater than 50% and the K2O / Na2O ratio is greater than 1 but less than 2.5, the (Na2O+K2O) in the formula is replaced by 2Na2O, without considering the K2O content; under the same SiO2 conditions, the larger the AR value, the greater the alkalinity of the rock or rock series.

[0062] Among them, Al2O3, CaO, Na2O, and K2O represent the contents of aluminum oxide, calcium oxide, sodium oxide, and potassium oxide, respectively.

[0063] (4) Differentiation Index (DI)

[0064] According to the theory of magma differentiation, silicate magma differentiation evolved toward enriched SiO2-NaAlSi3O8-KAlSi3O8 residual magma; the residual SiO2-NaAlSi3O8-KAlSi3O8 system includes six standard minerals: quartz (q), orthoclase (or), albite (ab), nepheline (ne), leucite (lc), and hexagonal potassium nepheline (kp);

[0065] DI = q + or + ab + ne + lc + kp

[0066] Where q, or, ab, ne, lc, and kp represent the contents of quartz, orthoclase, albite, nepheline, leucite, and hexagonal potassium nepheline, respectively.

[0067] (5) Consolidation Index (SI)

[0068] When crystallization differentiation occurs, the SI value of the residual magma decreases rapidly; from basic to acidic rocks, the consolidation index decreases; a high degree of magma differentiation results in a low SI value and a high degree of acidity in the rock; conversely, a poor degree of differentiation results in a high SI value and a high degree of basicity in the rock. Therefore, SI is an important petrochemical parameter reflecting the degree of differentiation and the basicity of the rock; the specific calculation formula is as follows:

[0069]

[0070] Among them, MgO, FeO, Fe2O3, Na2O, and K2O represent the contents of magnesium oxide, ferrous oxide, iron oxide, sodium oxide, and potassium oxide, respectively.

[0071] (6) Long Britain Index (FL)

[0072] The higher the degree of magma fractionation and crystallization, the larger the "Fragrance Index"; FL is a petrochemical parameter reflecting the degree of magma fractionation and crystallization.

[0073]

[0074] Among them, Na2O, K2O, and CaO represent the contents of sodium oxide, potassium oxide, and calcium oxide, respectively;

[0075] (7) Magnesium Iron Index (MF)

[0076] The higher the degree of magma separation and crystallization, the larger the "magnesium-iron index". MF is a petrochemical parameter that reflects the degree of magma separation and crystallization.

[0077]

[0078] Among them, FeO, Fe2O3, and MgO represent the contents of ferrous oxide, ferric oxide, and magnesium oxide, respectively;

[0079] (8) Larssen Index (LI)

[0080] The higher the LI value, the greater the rock acidity, indicating higher SiO2 and K2O content, and lower FeO content. * The content is low; the specific calculation formula for LI is as follows:

[0081] LI=[(SiO2 / 3)+K2O]-(CaO+MgO+FeO * (w) B %)

[0082] Among them, FeO * =FeO + 0.9Fe2O3 + MnO, where SiO2, K2O, CaO, MgO, FeO, and Fe2O3 represent the contents of silicon dioxide, potassium oxide, calcium oxide, magnesium oxide, ferrous oxide, and iron oxide, respectively.

[0083] (9) Oxidation degree (OX°) and oxidation rate (OX)

[0084] OX°=Fe 3+ / (Fe 3+ +Fe 2+ +Mn) (number of atoms)

[0085] OX = FeO / (FeO + Fe2O3)

[0086] Among them, Fe 3+ Fe 2+ Mn, FeO, and Fe2O3 represent the contents of ferric ions, ferrous ions, manganese, ferrous oxide, and ferric oxide, respectively.

[0087] (10) Magnesium-to-iron ratio (M / F)

[0088]

[0089] Among them, MgO, FeO, Fe2O3, MnO, and NiO represent the contents of magnesium oxide, ferrous oxide, iron oxide, manganese oxide, and nickel oxide, respectively.

[0090] (11) Weathering Index (WI)

[0091] The weathering process is essentially a desiliconization and aluminum-iron enrichment process. The more silicon is reduced and the more Fe2O3 and Al2O3 are enriched, the more thorough the weathering process and the higher the degree of weathering. Conversely, the less silicon is reduced and the lower the degree of weathering.

[0092]

[0093] Among them, (X) a The percentage of atoms of element X divided by its atomic weight; the denominator in the function is the strength of the bond between each element and oxygen.

[0094] The oxide content obtained by back-deriving from elemental logging data is substituted into the above 11 rock chemical index definition formulas to calculate the rock chemical index curve.

[0095] Preferably, in step S2, the normalized values ​​of each rock chemical index curve are as follows:

[0096] The range transformation method is adopted; the parameters in the range transformation are divided into two types: positive index and negative index. The larger the index value of the positive index, the better, and the smaller the index value of the negative index, the better.

[0097] For positive indices, the normalized value S of the rock chemistry index i The calculation formula is:

[0098]

[0099] For inverse indices, the normalized value S of the rock chemistry index i The calculation formula is:

[0100]

[0101] In the formula, S i X is the normalized value of the parameter. i The original value of the parameter before normalization, max X i The maximum value of the rock chemical index curve is min X. i This represents the minimum value of the rock chemical index curve.

[0102] Preferably, in step S2, after normalizing the rock chemical index curves, a correlation analysis is performed with the field fracturing test data. The correlation analysis mainly evaluates the linear relationship between variables, that is, whether they are correlated in a linear form. The correlation coefficient is used to quantify the strength of the linear relationship between variables. The specific steps are as follows:

[0103] (1) Collect normalized rock chemical index data and field fracturing test data to ensure the accuracy and completeness of the data;

[0104] (2) The two sets of data for correlation analysis were preprocessed, including handling missing values, outliers and erroneous data. The above-mentioned handling of missing values, outliers and erroneous data, namely handling the rock chemical index data that does not conform to common sense and the field fracturing test data and meaningless values, are existing technical means, so they will not be analyzed in detail here.

[0105] (3) Use the scatter plot visualization tool in EXCEL to display the relationship between the two sets of data, select the linear correlation analysis method, intuitively evaluate the correlation between them and calculate the correlation coefficient;

[0106] Preferably, in step S3, the weights of a series of strongly correlated rock chemical indices are determined using a multiple regression method, and the specific steps are as follows:

[0107] This can be achieved directly through the multiple regression option in the data analysis module of Excel. The specific steps for performing multiple regression analysis in Excel using the data analysis toolkit are as follows:

[0108] Step 1: Prepare data

[0109] Ensure that the data is correctly organized in an Excel worksheet, including a column of dependent variables, namely field fracturing test data, and multiple columns of independent variables, namely rock chemical indices;

[0110] Step 2: Enable the data analysis toolkit;

[0111] Step 3: Open the data analysis dialog box;

[0112] Step 4: Select multiple regression;

[0113] Step 5: Set the regression parameters;

[0114] 1. Enter Y region: Select the column where the field fracturing test is located;

[0115] 2. Enter X range: Select the column containing the rock chemical index;

[0116] 3. Output Range: Select a cell to begin outputting the regression analysis results; Excel will display at least the regression statistics, regression coefficients, and R-squared values ​​here. 2 value;

[0117] Step 6: View and analyze the results;

[0118] After clicking "OK", Excel will generate the regression analysis results in the specified output area, including:

[0119] Regression statistics: including at least the number of observations, R0 2 Value, adjusted R 2 value;

[0120] Regression coefficients: coefficient, standard error, t-statistic, and p-value for each independent variable;

[0121] Coefficient: Represents the expected change in the dependent variable for every unit change in the independent variable;

[0122] Through steps 1 to 6, multivariate regression analysis was performed using Excel to determine the weight coefficients of the rock chemical index.

[0123] Compared with the prior art, the present invention has the following beneficial effects:

[0124] 1. This invention proposes to use the rock chemical index of elemental logging as evaluation data, and finally obtain the compressibility index through a series of calculations. This not only overcomes the limitations of existing similar technologies that rely on relevant laboratory mechanical experimental data, but also makes full use of elemental logging data to evaluate compressibility from a new perspective, providing a theoretical basis for reservoir fracturing schemes.

[0125] 2. Improved Assessment Accuracy: By directly calculating the chemical index of rocks using elemental logging data, this method can more accurately reflect the chemical composition and corresponding physical properties of rocks. Compared with traditional physical logging methods, detailed analysis of chemical composition provides a deeper understanding of rock characteristics, helping to more accurately assess rock compressibility.

[0126] 3. Optimize fracturing design: By analyzing the correlation between rock chemical indices and fracturing test data, it is possible to identify which chemical indices are closely related to rock compressibility. Such analysis allows for more targeted fracturing design, thereby optimizing resource extraction and increasing oil and gas production.

[0127] 4. Data-Driven Decision Support: A compressibility index model is constructed using a multiple regression method combined with rock chemical indices and actual fracturing data, providing a powerful data-driven tool. This method adjusts and optimizes the model based on actual data, ensuring the practicality and effectiveness of the decision support system.

[0128] 5. Improved operational efficiency and cost-effectiveness: Accurate compressibility assessments guide more effective fracturing operations, reducing ineffective or inefficient fracturing attempts, thereby saving costs and reducing environmental impact. Through scientific forecasting and assessment, operational plans can be more rational, avoiding the overuse of resources and time.

[0129] 6. Wide applicability: This method is not limited to specific types of rocks or geological structures. Its rock chemical composition-based analysis method is applicable to a variety of lithologies and geological environments, enhancing its applicability to different oil fields around the world.

[0130] In summary, this invention, by combining modern elemental logging technology with advanced data analysis techniques, provides a more scientific, accurate, and cost-effective assessment method for rock fracturing in oil and gas development. This has significant practical and economic implications for improving the efficiency of oil and gas development and reducing development risks and costs. Attached Figure Description

[0131] Figure 1 This is a graph showing the relationship between the long-term index and fracturing test data in this invention.

[0132] Figure 2 This is a graph showing the relationship between the magnesium-iron index and fracturing test data in this invention.

[0133] Figure 3 This is a graph showing the combination index and fracturing test data in this invention.

[0134] Figure 4 This is a graph showing the consolidation index versus fracturing test data in this invention.

[0135] Figure 5This is a graph showing the relationship between the weathering index and fracturing test data in this invention.

[0136] Figure 6 This is a graph showing the relationship between the Larssen index and fracturing test data in this invention.

[0137] Figure 7 This is a curve showing the relationship between alkalinity rate and fracturing test data in this invention.

[0138] Figure 8 This is a graph showing the relationship between the magnesium-iron ratio and fracturing test data in this invention.

[0139] Figure 9 This is a graph showing the relationship between oxidation degree and fracturing test data in this invention.

[0140] Figure 10 This is a graph showing the relationship between oxidation rate and fracturing test data in this invention.

[0141] Figure 11 This is a graph showing the relationship between the calcium-alkali index and fracturing test data in this invention.

[0142] Figure 12 This is a graph showing the difference index and fracturing test data in this invention.

[0143] Figure 13 This is a diagram showing the actual application and processing results of the present invention.

[0144] Figure 14 This is a graph showing the curves of (Na2O+K2O) versus SiO2 and the curves of CaO versus SiO2 in this invention. Detailed Implementation

[0145] The following are specific embodiments of the present invention, which are described in conjunction with the accompanying drawings. However, the present invention is not limited to these embodiments.

[0146] Example 1

[0147] A method for assessing rock chemical compressibility based on elemental logging, characterized in that the compressibility assessment method includes the following steps:

[0148] S1. Preparation of elemental logging data: The elemental logging curves of each element are obtained by using lithological scanning elemental logging instruments, and the oxide content of each element is deduced from the elemental content curves.

[0149] The specific lithological elemental logging instruments used here can be LithoScanner, ECS, etc.

[0150] The specific back-calculation process of obtaining the oxide content from the elemental content curve in step S1 includes the following steps:

[0151] (1) Determine the chemical formula of the oxide: Based on the element types obtained from element logging, assume that the chemical formula of the oxide formed by this element is EO. x Where E is the element, O is oxygen, and x is the number of oxygen atoms;

[0152] (2) Calculate the relative molecular mass of the oxide: EO x relative molecular mass Add x relative atomic masses of oxygen atoms to the relative atomic mass of E; for example, the relative atomic mass of oxygen is approximately 16 g / mol.

[0153] (3) Calculate the molar ratio of elements in oxides: Since each EO x The molecule contains 1 E atom and x oxygen atoms, so the E atom and EO x The molar ratio is 1 / (1+x);

[0154] (4) Given the mass percentage of an element: Let the mass percentage of element E be w. E ;

[0155] (5) Calculate the total mass of the oxides: Let the total mass of the sample be M. total Then the mass m of element E E For w E ×M total ;

[0156] (6) Calculate the mass of the oxide based on the mass of the element: The mass of the oxide is calculated from the mass of element E in the oxide EO. x The molar ratio in the middle is used to calculate EO. x quality

[0157] (7) Calculate the mass percentage of the oxide:

[0158] Based on the above steps, the mass percentage of an oxide can be calculated by back-calculating the known mass percentage of an element.

[0159] The method of calculating the mass percentage of an oxide by inversely deducing the mass percentage of an element is a current method and will not be described in detail here.

[0160] S2. Using the oxide contents of each element obtained in S1, numerical calculations are performed based on the concept of rock chemical indices, including at least one of the following: calcium-alkali index, combination index, alkalinity ratio, differentiation index, consolidation index, felsic index, magnesium-iron index, Larssen index, degree of oxidation and oxidation rate, magnesium-iron ratio, and weathering index. The calculated rock chemical index curves are normalized and correlated with field fracturing test data. A correlation greater than 0.8 (R²) is required. 2>0.8) is a rock chemical index that is strongly correlated with the compressibility index;

[0161] In step S2, the rock chemical index calculation steps are as follows:

[0162] (1) Calcium-Base Index (CA)

[0163] The curves of (Na₂O + K₂O) versus SiO₂ and the curves of CaO versus SiO₂ are shown in... Figure 14 The intersection of the upper and lower strata or the intersection of the extended curves, and the SiO2 value corresponding to the foot of the perpendicular on the horizontal axis of the intersection point is called the calcium-alkali index, or the Peacock index, denoted by the symbol CA. There is only one CA for a rock assemblage.

[0164] (2) Combination index (σ)

[0165] σ=(Na2O+K2O) 2 / (SiO2-43) (where the oxide is a mass percentage)

[0166] Among them, Na2O, K2O, and SiO2 represent the contents of sodium oxide, potassium oxide, and silicon dioxide, respectively;

[0167] (3) Alkalinity Ratio (AR)

[0168]

[0169] If the SiO2 content of the rock is greater than 50% and the K2O / Na2O ratio is greater than 1 but less than 2.5, the (Na2O+K2O) in the formula is replaced by 2Na2O, without considering the K2O content; under the same SiO2 conditions, the larger the AR value, the greater the alkalinity of the rock or rock series.

[0170] Among them, Al2O3, CaO, Na2O, and K2O represent the contents of aluminum oxide, calcium oxide, sodium oxide, and potassium oxide, respectively.

[0171] (4) Differentiation Index (DI)

[0172] According to the theory of magmatic differentiation, silicate magma differentiation evolves towards enrichment in SiO2-NaAlSi3O8-KAlSi3O8 residual magma. Bowen pointed out that the residual SiO2-NaAlSi3O8-KAlSi3O8 system includes six standard minerals: quartz (q), orthoclase (or), albite (ab), nepheline (ne), leucite (lc), and hexagonal potassium nepheline (kp); Thornton and Tuttle (CP Thornton and OF Tuttle, 1960) called the sum of the percentages of these six standard minerals the differentiation index (DI).

[0173] DI = q + or + ab + ne + lc + kp

[0174] In this equation, q, or, ab, ne, lc, and kp represent the contents of quartz, orthoclase, albite, nepheline, leucite, and hexagonal potassium nepheline, respectively. Because these six standard minerals cannot appear simultaneously, the presence of q will not result in the presence of ne, lc, or kp; and the presence of kp will not result in or, ab, etc. Therefore, DI is actually only the sum of two or three of these six minerals.

[0175] (5) Consolidation Index (SI)

[0176] When crystallization differentiation occurs, the SI value of the residual magma decreases rapidly; from basic to acidic rocks, the consolidation index decreases; a high degree of magma differentiation results in a low SI value and a high degree of acidity in the rock; conversely, a poor degree of differentiation results in a high SI value and a high degree of basicity in the rock. Therefore, SI is an important petrochemical parameter reflecting the degree of differentiation and the basicity of the rock; the specific calculation formula is as follows:

[0177]

[0178] Among them, MgO, FeO, Fe2O3, Na2O, and K2O represent the contents of magnesium oxide, ferrous oxide, iron oxide, sodium oxide, and potassium oxide, respectively.

[0179] (6) Long Britain Index (FL)

[0180] The higher the degree of magma fractionation and crystallization, the larger the "Fragrance Index"; FL is a petrochemical parameter reflecting the degree of magma fractionation and crystallization.

[0181]

[0182] Among them, Na2O, K2O, and CaO represent the contents of sodium oxide, potassium oxide, and calcium oxide, respectively;

[0183] (7) Magnesium Iron Index (MF)

[0184] The higher the degree of magma separation and crystallization, the larger the "magnesium-iron index". MF is a petrochemical parameter that reflects the degree of magma separation and crystallization.

[0185]

[0186] Among them, FeO, Fe2O3, and MgO represent the contents of ferrous oxide, ferric oxide, and magnesium oxide, respectively;

[0187] (8) Larssen Index (LI)

[0188] Larsson's index (LI) is a method of expressing rock acidity. It can be positive or negative. A higher LI indicates greater rock acidity, suggesting higher SiO2 and K2O content, and lower FeO content. * The content is low; the specific calculation formula for LI is as follows:

[0189] LI=[(SiO2 / 3)+K2O]-(CaO+MgO+FeO * (w) B %)

[0190] Among them, FeO * =FeO + 0.9Fe2O3 + MnO, where SiO2, K2O, CaO, MgO, FeO, and Fe2O3 represent the contents of silicon dioxide, potassium oxide, calcium oxide, magnesium oxide, ferrous oxide, and iron oxide, respectively.

[0191] (9) Oxidation degree (OX°) and oxidation rate (OX)

[0192] OX°=Fe 3+ / (Fe 3+ +Fe 2+ +Mn) (number of atoms)

[0193] OX = FeO / (FeO + Fe2O3)

[0194] Among them, Fe 3+ Fe 2+ Mn, FeO, and Fe2O3 represent the contents of ferric ions, ferrous ions, manganese, ferrous oxide, and ferric oxide, respectively.

[0195] (10) Magnesium-to-iron ratio (M / F)

[0196]

[0197] Among them, MgO, FeO, Fe2O3, MnO, and NiO represent the contents of magnesium oxide, ferrous oxide, iron oxide, manganese oxide, and nickel oxide, respectively.

[0198] (11) Weathering Index (WI)

[0199] The weathering process is essentially a desiliconization and aluminum-iron enrichment process. The more silicon is reduced and the more Fe2O3 and Al2O3 are enriched, the more thorough the weathering process and the higher the degree of weathering. Conversely, the less silicon is reduced and the lower the degree of weathering.

[0200]

[0201] Among them, (X) a The percentage of atoms of element X divided by its atomic weight; the denominator in the function is the strength of the bond between each element and oxygen.

[0202] The oxide content obtained by back-deriving from elemental logging data is substituted into the above 11 rock chemical index definition formulas to calculate the rock chemical index curve.

[0203] In step S2, the normalized values ​​of each rock chemical index curve are as follows:

[0204] The range transformation method is adopted; the parameters in the range transformation are divided into two types: positive index and negative index. The larger the index value of the positive index, the better, and the smaller the index value of the negative index, the better.

[0205] For positive indices, the normalized value S of the rock chemistry index i The calculation formula is:

[0206]

[0207] For inverse indices, the normalized value S of the rock chemistry index i The calculation formula is:

[0208]

[0209] In the formula, S i X is the normalized value of the parameter. i The original value of the parameter before normalization, max X i The maximum value of the rock chemical index curve is min X. i This represents the minimum value of the rock chemical index curve.

[0210] In step S2, after normalizing the rock chemical index curves, correlation analysis is performed with the field fracturing test data. The correlation analysis mainly evaluates the linear relationship between variables, that is, whether they are correlated in a linear form. The correlation coefficient is used to quantify the strength of the linear relationship between variables. The specific steps are as follows:

[0211] The various rock chemical indices include: calcium-alkali index, assemblage index, alkalinity ratio, differentiation index, consolidation index, felsic index, magnesium-iron index, Larssen index, degree of oxidation and oxidation rate, magnesium-iron ratio, and weathering index.

[0212] (1) Collect normalized rock chemical index data and field fracturing test data to ensure the accuracy and completeness of the data;

[0213] (2) Preprocess the two sets of data for correlation analysis, including handling missing values, outliers and erroneous data; the above three preprocessing methods are existing data processing methods and belong to existing technology.

[0214] (3) Use the scatter plot visualization tool in EXCEL to display the relationship between the two sets of data, select the linear correlation analysis method, intuitively evaluate the correlation between them and calculate the correlation coefficient;

[0215] In Excel, the correlation coefficient R 2 The coefficient of determination (R²) is commonly used to measure the goodness of fit of a regression model. Calculate R². 2 The steps are as follows:

[0216] ① Calculate the total sum of squares (SST):

[0217] This measures the sum of the deviations of each data point from the data mean. The calculation formula is:

[0218]

[0219] Among them, Y i It is the i-th observation. The average of all observations.

[0220] ② Calculate the Regression Sum of Squares (SSR):

[0221] This measures the sum of deviations between the values ​​predicted by the regression line and the data mean. The calculation formula is:

[0222]

[0223] in, It is the i-th value predicted by the regression equation.

[0224] ③ Calculate the residual sum of squares (SSE):

[0225] This measures the sum of the deviations between the actual observed values ​​and the predicted values ​​of the regression line. The calculation formula is:

[0226]

[0227] in, It is the i-th value predicted by the regression equation.

[0228] ④ Calculate R 2 value:

[0229] R 2 The value can be calculated using the following formula:

[0230] Or, equivalently:

[0231] This formula indicates that R 2 It is the proportion of the variance explained by the regression model to the total variance.

[0232] Calculate R in Excel 2 This includes the following two methods:

[0233] 1. Use data analysis toolkits:

[0234] ① Ensure the data analysis toolkit is enabled. Add-ins can be managed in "File" → "Options" → "Add-ins".

[0235] ② Go to the “Data” tab and click the “Data Analysis” button.

[0236] ③ Select "Regression" analysis, and then click "OK".

[0237] ④ Set the input Y region (dependent variable) and input X region (independent variable).

[0238] ⑤ Select an output range or a new worksheet to display the results.

[0239] ⑥ Click "OK", and Excel will output including R 2 The regression analysis results include the values.

[0240] 2. Use the formula:

[0241] If you don't use data analysis tools, you can also calculate R manually. 2 First, calculate SST, SSR, and SSE, then use the formula above to calculate R. 2 (4) Calculate the correlation coefficient using EXCEL, assess the significance of the correlation coefficient, and interpret the magnitude of the correlation between the rock chemical index and the fracturing test data based on the value and significance of the correlation coefficient. R 2 The value ranges from 0 to 1. The closer the value is to 1, the stronger the explanatory power of the model, that is, the better the model can predict changes in the dependent variable, i.e., the pressure index.

[0242] (4) Calculate the correlation coefficient using EXCEL, assess the significance of the correlation coefficient, and interpret the magnitude of the correlation between the rock chemical index and the fracturing test data based on the value of the correlation coefficient and the significance results. R 2 The value ranges from 0 to 1. The closer the value is to 1, the stronger the explanatory power of the model, that is, the better the model can predict changes in the dependent variable, i.e., the pressure index.

[0243] S3. Using multiple regression to determine the weights of a series of strongly correlated petrochemical indices and fracturing test data from S2 that are highly correlated with the compressibility index, the petrochemical indices are multiplied and superimposed with the weight coefficients obtained from the multiple regression to construct a compressibility index model based on elemental logging of petrochemical indices:

[0244]

[0245] In the above formula, FI represents the compressibility index based on elemental logging petrochemical indices, which is dimensionless; S i (i = 1, 2, 3, ..., n) are highly correlated petrochemical indices, dimensionless; W i (i = 1, 2, 3, ..., n) are the weight coefficients of each rock chemical index obtained by the analytic hierarchy process, which are dimensionless; n is the number of rock chemical indices.

[0246] In step S3, the weights of a series of strongly correlated rock chemical indices are determined using multiple regression. The specific steps are as follows:

[0247] This step can be achieved directly through the multiple regression option in the data analysis module of Excel. The specific steps for performing multiple regression analysis using the data analysis toolkit in Excel are as follows:

[0248] Step 1: Prepare data

[0249] Ensure that the data is correctly organized in an Excel worksheet, including one column of dependent variables (in this study, in-situ fracturing test data) and multiple columns of independent variables (in this study, indices of rock chemistry).

[0250] Step 2: Enable the data analysis toolkit

[0251] 1. Click the "File" menu.

[0252] 2. Select "Options".

[0253] 3. In the "Excel Options" window, select "Add-ins".

[0254] 4. In the bottom management box, select "Excel Add-ins" and then click "Go".

[0255] 5. Check "Analysis ToolPak" and "Analysis ToolPak - VBA", then click "OK".

[0256] Step 3: Open the data analysis dialog box

[0257] 1. Click the "Data" tab.

[0258] 2. In the "Analysis" group, click the "Data Analysis" button.

[0259] Step 4: Select Multiple Regression

[0260] 1. In the "Data Analysis" dialog box, select "Regression" from the list, and then click "OK".

[0261] Step 5: Set regression parameters

[0262] 1. Enter Y region: Select the column where the field fracturing test is located.

[0263] 2. Enter X range: Select the column containing the rock chemical index.

[0264] 3. Output Range: Select a cell to begin outputting the regression analysis results. Excel will display the regression statistics, regression coefficients, and R-squared values ​​here. 2 Values, etc.

[0265] Step 6: View and analyze the results

[0266] After clicking "OK", Excel will generate the regression analysis results in the specified output area. This includes:

[0267] - Regression statistics: including the number of observations, R0 2 Value, adjusted R 2 Values, etc.

[0268] - Regression coefficients: coefficient, standard error, t-statistic, and p-value for each independent variable.

[0269] - Coefficient: Represents the expected change in the dependent variable for every unit change in the independent variable.

[0270] Through the above steps, a multivariate regression analysis was performed using Excel to determine the weight coefficients of the rock chemical index.

[0271] Example 2

[0272] The above-mentioned rock chemical index-based compressibility evaluation method based on elemental logging is applied to guide the field well compressibility evaluation. The correlation between the rock chemical index and fracturing test data is analyzed, specifically the R-value in the two sets of discrete point fitting equations. 2 Size, processing and analysis chart as follows Figures 1 to 12 As shown.

[0273] Based on the above well correlation analysis results, the following eight petrochemical indices with correlation coefficients greater than 0.8 were extracted, and the following compressibility coefficient model based on elemental logging petrochemical indices was constructed:

[0274]

[0275] In the above formula,

[0276] FI stands for Rock Chemical Index, Compressibility Index.

[0277] LI represents the normalized Larsson index, corresponding to weight α.

[0278] MF represents the normalized magnesium-iron index, corresponding to weight b.

[0279] This represents the normalized magnesium-to-iron ratio, corresponding to the weight c.

[0280] AR represents the normalized alkalinity rate, corresponding to the weight d.

[0281] ZHZS represents the normalized portfolio index, corresponding to the weight e.

[0282] FL represents the normalized Long British Index, corresponding to the weight f.

[0283] SI represents the normalized consolidation index, corresponding to the weight g.

[0284] WI represents the normalized weathering index, corresponding to the weight h.

[0285]

[0286] The final compressibility evaluation results are as follows: Figure 13 As shown, the upper layer of the block is composed of sand and mud interlayers, while the lower layer is composed of sandstone. The compressibility index gradually increases with depth, and there is a relatively obvious stratigraphic boundary in the middle. The petrochemical index evaluation results based on elemental logging are more accurate than the conventional rock mechanics evaluation results and are closer to the field fracturing test data. Moreover, the petrochemical index compressibility curve contains rich stratigraphic information, sensitively and continuously reflecting the stratification and cyclic characteristics of the measured strata, and clearly revealing the sedimentary cyclicity of the three sections of the block.

[0287] Elemental logging-based rock chemical compressibility assessment methods can effectively guide oilfield compressibility evaluation and fracturing scheme design. In practical applications, it is necessary to comprehensively consider factors such as the rock's chemical composition, structural characteristics, and external environment to select an appropriate compressibility assessment method. This is of great significance for accurately evaluating the rock's mechanical behavior and failure characteristics.

[0288] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

[0289] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

Claims

1. A method for evaluating the compressibility of rocks based on elemental logging, characterized in that, The compressibility assessment method includes the following steps: S1. Preparation of elemental logging data: The elemental logging curves of each element are obtained by using lithological scanning elemental logging instruments, and the oxide content of each element is deduced from the elemental content curves. S2. Using the oxide contents of each element obtained in S1, numerical calculations are performed based on the concept of rock chemical indices, including at least one of the following: calcium-alkali index, combination index, alkalinity ratio, differentiation index, consolidation index, felsic index, magnesium-iron index, Larssen index, degree of oxidation and oxidation rate, magnesium-iron ratio, and weathering index. The calculated rock chemical index curves are normalized and correlated with field fracturing test data. A correlation greater than 0.8 (R²) is required. 2 >0.8) is a rock chemical index that is strongly correlated with the compressibility index; S3. Using multiple regression to determine the weights of a series of strongly correlated petrochemical indices and fracturing test data from S2 that are highly correlated with the compressibility index, the petrochemical indices are multiplied and superimposed with the weight coefficients obtained from the multiple regression to construct a compressibility index model based on elemental logging of petrochemical indices: In the above formula, FI represents the compressibility index based on elemental logging petrochemical indices, which is dimensionless; S i (i = 1, 2, 3, ..., n) are highly correlated petrochemical indices, dimensionless; W i (i = 1, 2, 3, ..., n) are the weight coefficients of each rock chemical index obtained by the analytic hierarchy process, which are dimensionless; n is the number of rock chemical indices. The specific back-calculation process of obtaining the oxide content from the elemental content curve in step S1 includes the following steps: (1) Determine the chemical formula of the oxide: Based on the element types obtained from element logging, assume that the chemical formula of the oxide formed by this element is EO. x Where E is the element, O is oxygen, and x is the number of oxygen atoms; (2) Calculate the relative molecular mass of the oxide: EO x relative molecular mass Add the relative atomic masses of x oxygen atoms to the relative atomic mass of E; (3) Calculate the molar ratio of elements in oxides: Since each EO x The molecule contains 1 E atom and x oxygen atoms, so the E atom and EO x The molar ratio is 1 / (1+x); (4) Given the mass percentage of an element: Let the mass percentage of element E be w. E ; (5) Calculate the total mass of the oxides: Let the total mass of the sample be M. total Then the mass m of element E E For w E ×M total ; (6) Calculate the mass of the oxide based on the mass of the element: The mass of the oxide is calculated from the mass of element E in the oxide EO. x The molar ratio in the middle is used to calculate EO. x quality (7) Calculate the mass percentage of the oxide: Based on the above steps, the mass percentage of an oxide can be calculated by back-calculating the known mass percentage of an element.

2. The method for evaluating rock chemical compressibility based on elemental logging as described in claim 1, characterized in that, In step S2, the rock chemical index calculation steps are as follows: (1) Calcium-Base Index (CA) The curves of (Na2O+K2O) versus SiO2 and CaO versus SiO2 should intersect or be extended to intersect. The SiO2 value corresponding to the foot of the perpendicular on the horizontal axis of the intersection point is called the calcium-alkali index, or the Peacock index, denoted by the symbol CA. There is only one CA for a rock assemblage. (2) Combination index (σ) σ=(Na2O+K2O) 2 / (SiO2-43) (where the oxide is a mass percentage) Among them, Na2O, K2O, and SiO2 represent the contents of sodium oxide, potassium oxide, and silicon dioxide, respectively; (3) Alkalinity Ratio (AR) If the SiO2 content of the rock is greater than 50% and the K2O / Na2O ratio is greater than 1 but less than 2.5, the (Na2O+K2O) in the formula is replaced by 2Na2O, without considering the K2O content; under the same SiO2 conditions, the larger the AR value, the greater the alkalinity of the rock or rock series. Among them, Al2O3, CaO, Na2O, and K2O represent the contents of aluminum oxide, calcium oxide, sodium oxide, and potassium oxide, respectively. (4) Differentiation Index (DI) According to the theory of magma differentiation, silicate magma differentiation evolved toward enriched SiO2-NaAlSi3O8-KAlSi3O8 residual magma; the residual SiO2-NaAlSi3O8-KAlSi3O8 system includes six standard minerals: quartz (q), orthoclase (or), albite (ab), nepheline (ne), leucite (lc), and hexagonal potassium nepheline (kp); DI = q + or + ab + ne + lc + kp Where q, or, ab, ne, lc, and kp represent the contents of quartz, orthoclase, albite, nepheline, leucite, and hexagonal potassium nepheline, respectively. (5) Consolidation Index (SI) When crystallization differentiation occurs, the SI value of the residual magma decreases rapidly; from basic to acidic rocks, the consolidation index decreases; a high degree of magma differentiation results in a low SI value and a high degree of acidity in the rock; conversely, a poor degree of differentiation results in a high SI value and a high degree of basicity in the rock. Therefore, SI is an important petrochemical parameter reflecting the degree of differentiation and the basicity of the rock; the specific calculation formula is as follows: Among them, MgO, FeO, Fe2O3, Na2O, and K2O represent the contents of magnesium oxide, ferrous oxide, iron oxide, sodium oxide, and potassium oxide, respectively. (6) Long Britain Index (FL) The higher the degree of magma fractionation, the greater the "Fel content"; FL is a petrochemical parameter that reflects the degree of magma fractionation. Among them, Na2O, K2O, and CaO represent the contents of sodium oxide, potassium oxide, and calcium oxide, respectively; (7) Magnesium Iron Index (MF) The higher the degree of magma fractionation, the larger the "magnesium-iron index". MF is a petrochemical parameter that reflects the degree of magma fractionation. Among them, FeO, Fe2O3, and MgO represent the contents of ferrous oxide, ferric oxide, and magnesium oxide, respectively; (8) Larssen Index (LI) The higher the LI value, the greater the rock acidity, indicating higher SiO2 and K2O content, and lower FeO content. * The content is low; the specific calculation formula for LI is as follows: LI=[(SiO2 / 3)+K2O]-(CaO+MgO+FeO * )(w B %) Among them, FeO * =FeO + 0.9Fe2O3 + MnO, where SiO2, K2O, CaO, MgO, FeO, and Fe2O3 represent the contents of silicon dioxide, potassium oxide, calcium oxide, magnesium oxide, ferrous oxide, and iron oxide, respectively. (9) Oxidation degree (OX°) and oxidation rate (OX) OX°=Fe 3+ / (Fe 3+ +Fe 2+ +Mn) (number of atoms) OX = FeO / (FeO + Fe2O3) Among them, Fe 3+ Fe 2+ Mn, FeO, and Fe2O3 represent the contents of ferric ions, ferrous ions, manganese, ferrous oxide, and ferric oxide, respectively. (10) Magnesium-to-iron ratio (M / F) Among them, MgO, FeO, Fe2O3, MnO, and NiO represent the contents of magnesium oxide, ferrous oxide, iron oxide, manganese oxide, and nickel oxide, respectively. (11) Weathering Index (WI) The weathering process is essentially a desiliconization and aluminum-iron enrichment process. The more silicon is reduced and the more Fe2O3 and Al2O3 are enriched, the more thorough the weathering process and the higher the degree of weathering. Conversely, the less silicon is reduced and the lower the degree of weathering. Among them, (X) a The percentage of atoms of element X divided by its atomic weight; the denominator in the function is the strength of the bond between each element and oxygen. The oxide content obtained by back-deriving from elemental logging data is substituted into the above 11 rock chemical index definition formulas to calculate the rock chemical index curve.

3. The method for evaluating rock chemical compressibility based on elemental logging as described in claim 2, characterized in that, In step S2, the normalized values ​​of each rock chemical index curve are as follows: The range transformation method is adopted; the parameters in the range transformation are divided into two types: positive index and negative index. The larger the index value of the positive index, the better, and the smaller the index value of the negative index, the better. For positive indices, the normalized value S of the rock chemistry index i The calculation formula is: For inverse indices, the normalized value S of the rock chemistry index i The calculation formula is: In the formula, S i X is the normalized value of the parameter. i The original value of the parameter before normalization, max X i The maximum value of the rock chemical index curve is min X. i This represents the minimum value of the rock chemical index curve.

4. The method for evaluating rock chemical compressibility based on elemental logging as described in claim 3, characterized in that, In step S2, after normalizing the rock chemical index curves, correlation analysis is performed with the field fracturing test data. The correlation analysis assesses the linear relationship between variables, whether they are linearly correlated, and uses the correlation coefficient to quantify the strength of the linear relationship between variables. The specific steps are as follows: (1) Collect normalized rock chemical index data and field fracturing test data to ensure the accuracy and completeness of the data; (2) Preprocess the two sets of data for correlation analysis, including handling missing values, outliers and erroneous data; (3) Use the scatter plot visualization tool in EXCEL to display the relationship between the two sets of data, select the linear correlation analysis method, intuitively evaluate the correlation between them and calculate the correlation coefficient; (4) Use EXCEL to calculate the correlation coefficient, evaluate the significance of the correlation coefficient, and interpret the correlation between the rock chemical index and the fracturing test data based on the value of the correlation coefficient and the significance results.

5. The method for evaluating rock chemical compressibility based on elemental logging as described in claim 4, characterized in that, In step S3, the weights of a series of strongly correlated rock chemical indices are determined using multiple regression. The specific steps are as follows: This can be achieved directly through the multiple regression option in the data analysis module of Excel. The specific steps for performing multiple regression analysis in Excel using the data analysis toolkit are as follows: Step 1: Prepare data Ensure that the data is correctly organized in an Excel worksheet, including a column of dependent variables, namely field fracturing test data, and multiple columns of independent variables, namely rock chemical indices; Step 2: Enable the data analysis toolkit; Step 3: Open the data analysis dialog box; Step 4: Select multiple regression; Step 5: Set the regression parameters; 1. Enter Y region: Select the column where the field fracturing test is located; 2. Enter X range: Select the column containing the rock chemical index; 3. Output Range: Select a cell to begin outputting the regression analysis results; Excel will display at least the regression statistics, regression coefficients, and R-squared values ​​here. 2 value; Step 6: View and analyze the results; After clicking "OK", Excel will generate the regression analysis results in the specified output area, including: Regression statistics: including at least the number of observations, R0 2 Value, adjusted R 2 value; Regression coefficients: coefficient, standard error, t-statistic, and p-value for each independent variable; Coefficient: Represents the expected change in the dependent variable for every unit change in the independent variable; through steps 1 to 6, multiple regression analysis is performed using Excel to determine the weight coefficients of the rock chemical index.

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