Quantitative characterization method for lithogenous phase of tight sandstone reservoir

Abstract

The invention discloses a compact sandstone reservoir diagenetic phase quantitative characterization method which is characterized by comprising the following specific steps: step 1, determining diagenetic phase evaluation parameters and obtaining a parameter sequence; step 2, determining a correlation coefficient of the parameter sequence obtained in the step 1 by applying a grey correlation method, and calculating a correlation degree; step 3, constructing a multivariate comprehensive classification coefficient Feci; 4, dividing lithogenous phase types according to the multivariate comprehensive classification coefficient obtained in the step 3; and 5, performing logging characteristic analysis on the types of the diagenetic facies divided in the step 4, selecting a sensitive logging curve, and obtaining a discrimination function by using a Fisher discrimination method so as to realize quantitative identification of the diagenetic facies. According to the method, evaluation parameters are high in credibility and objective, so that lithogenous phase judgment can be more accurate.

Description

Technical Field

[0001] The invention belongs to the technical field of oilfield development, and in particular relates to a method for quantitatively characterizing diagenetic phases of a tight sandstone reservoir. Background Art

[0002] As the development objects become more complex, the heterogeneity of oil reservoirs gradually increases, and the difficulty of sweet spot optimization increases day by day. Among them, diagenesis has a significant impact on the porosity, permeability and connectivity of tight sandstone reservoirs, and is a key factor in controlling the development and distribution of high-quality reservoirs. Diagenetic phase refers to the diagenesis after the sediments form sedimentary rocks before metamorphism occurs, covering the research contents such as diagenetic minerals, diagenetic environment, diagenetic stage, and diagenetic evolution sequence. Diagenesis is generally divided into destructive diagenesis and constructive diagenesis. Destructive diagenesis mainly includes mechanical compaction and pressure solution, calcareous cementation, mud cementation, siliceous cementation, etc., which all make the reservoir performance worse. Therefore, diagenetic phase research has also become an effective way and means to predict favorable reservoirs.

[0003] The remaining reserves of tight oil are huge, but there is no universally adaptable quantitative identification method for diagenetic phases. The publication number is: CN 112580200 A, and the patent name is: A quantitative evaluation method for low-permeability sandstone diagenetic phases, in which the evaluation of diagenetic phases is to optimize the two parameters of apparent cementation rate and apparent dissolution rate, so that the characterization of the influence of dissolution and cementation on the reservoir is more accurate, but its characterization cannot comprehensively and objectively reflect the influence of diagenesis on the reservoir. Therefore, it is urgent to find a more comprehensive evaluation system to determine the judgment method of diagenetic phases, better guide the selection of favorable target production areas, and provide a strong basis for the efficient development of oil fields. Summary of the invention

[0004] The purpose of the present invention is to provide a method for quantitatively characterizing the diagenetic phase of a tight sandstone reservoir, the evaluation parameters of which are highly credible and objective, thereby enabling more accurate judgment of the diagenetic phase.

[0005] The technical solution adopted by the present invention is a method for quantitative characterization of diagenetic phases of tight sandstone reservoirs, and the specific steps are as follows:

[0006] Step 1, determine the diagenetic facies evaluation parameters and obtain the parameter sequence;

[0007] Step 2, applying the grey correlation method, determining the correlation coefficient of the parameter sequence obtained in step 1, and calculating the correlation degree;

[0008] Step 3, construct the multivariate comprehensive classification coefficient Feci;

[0009] Step 4, dividing the diagenetic facies types according to the multivariate comprehensive classification coefficient obtained in step 3;

[0010] Step 5: Perform logging characteristic analysis on the diagenetic facies types classified in step 4, select sensitive logging curves, and use Fisher discriminant method to obtain discriminant functions to achieve quantitative identification of diagenetic facies.

[0011] The present invention is also characterized in that:

[0012] In step 1, the evaluation parameters include displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0. K represents the permeability, Indicates porosity.

[0013] The specific process of step 2 is:

[0014] Step 2.1, the displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0 parameter sequence obtained in step 1 form a matrix, that is:

[0015]

[0016] Where X0=(X0(1),X0(2),…,X0(n)) T , n is the variable sequence length;

[0017] Step 2.2, using the linear normalization method, each parameter in the displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0 parameter sequence is dimensionless to obtain a dimensionless matrix;

[0018] The dimensionless expressions of each evaluation parameter are:

[0019]

[0020] In the formula, X i (k) represents the evaluation parameter; (X i (k)) min Indicates the minimum value of the same evaluation parameter; (X i (k)) max Indicates the maximum value of the same type of evaluation parameters; i = 0, 1, 2, 3, 4, 5; k = 1, 2, ..., n;

[0021] The dimensionless matrix is:

[0022]

[0023] Step 2.3, calculate the correlation coefficient

[0024] Solve the absolute difference between the first column and the remaining columns in the dimensionless matrix and form an absolute difference matrix;

[0025] The expression for the absolute difference is:

[0026] Δ 0i (k)=|x0(k)-x i (k)|, i=1,2,3,4,5 (4)

[0027] The absolute difference matrix is:

[0028]

[0029] The maximum number in the absolute difference matrix is ​​taken as the maximum difference, and the minimum number is taken as the minimum difference, specifically:

[0030] Δ max =max (Δ 0i (k)) (6)

[0031] Δ min =min(Δ 0i (k)) (7)

[0032] The data in the absolute difference matrix is ​​transformed according to formula (8) to obtain the correlation coefficient matrix;

[0033]

[0034] In the formula, ρ represents the resolution coefficient, which is 0.5;

[0035] Step 2.4, calculate the correlation

[0036] Correlation of displacement pressure Δ 01 is the average value of the first column in the correlation coefficient matrix;

[0037] Correlation of median radius Δ 02 is the average value of the second column in the correlation coefficient matrix;

[0038] Correlation of clay mineral content Δ 03 is the average value of the third column in the correlation coefficient matrix;

[0039] Correlation of carbonate content Δ 04 is the average value of the fourth column in the correlation coefficient matrix;

[0040] Correlation of face rate Δ 05 is the average value of the fifth column in the correlation coefficient matrix.

[0041] In step 3, the expression of the multivariate comprehensive classification coefficient is:

[0042]

[0043] In the formula, denotes porosity, obtained through core physical property tests, %; k i is permeability, ×10 -3 μm 2 ; C i is the pore face ratio, %; R i is the average pore throat radius, μm; CLM i is the clay mineral content, %; CAM i is the carbonate content, %; Pc i is the displacement pressure, MPa, where i denotes a single specific parameter; max is the maximum value of this parameter.

[0044] In step 4, the criteria for classifying diagenetic facies types are as follows:

[0045] Feci > 4, it is the chlorite film remaining intergranular pore facies;

[0046] 2 < Feci ≤ 4, it is the feldspar dissolution facies;

[0047] -1 < Feci ≤ 2, it is the clay mineral cemented micropore facies;

[0048] Feci ≤ -1, it is the carbonate cemented dense facies.

[0049] In step 5, the sensitive logging curves include density, compensated neutron, acoustic travel time, and resistivity curves.

[0050] In step 5, the discriminant functions are:

[0051] F1 = 10.144AC + 5182.119DEN - 18.476CNL + 0.35RT - 7441.267;

[0052] F2 = 9.848AC + 5260.691DEN - 18.412CNL + 0.35RT - 7570.852;

[0053] F3 = 9.598AC + 5294.353DEN - 18.401CNL + 0.35RT - 7600.513;

[0054] F4 = 9.528AC + 5366.449DEN - 18.616CNL + 0.34RT - 7765.432;

[0055] In the formula, F1 denotes the chlorite film remaining intergranular pore facies; F2 denotes the feldspar dissolution facies; F3 denotes the clay mineral cemented micropore facies; F4 denotes the carbonate cemented dense facies; AC denotes the resistivity curve; RT denotes the acoustic travel time curve; DEN denotes the density curve; CNL denotes the compensated neutron curve.

[0056] The beneficial effects of the present invention are as follows: the method for quantitative characterization of diagenetic phases of tight sandstone reservoirs of the present invention can quantitatively identify the diagenetic phases of all wells in a block through a discriminant function, and no longer relies on experimental tests such as physical properties. The method of the present invention takes into account the characteristics of tight reservoirs and rapid changes in physical properties, and the experimental data and logging data are relatively easy to obtain, universal, operational, and accurate, and also have a good corresponding effect with production characteristics, and can effectively guide the optimization of favorable target areas and main contribution sections. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the Fisher discriminant method in the embodiment of the method of the present invention;

[0058] Figure 2 A reservoir type logging identification diagram in an embodiment of the method of the present invention;

[0059] Figure 3 It is a plane distribution diagram of diagenetic phases in the embodiment of the method of the present invention;

[0060] Figure 4 Schematic diagram of pore evolution in an embodiment of the method of the present invention. DETAILED DESCRIPTION

[0061] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0062] Example 1

[0063] The present invention provides a method for quantitatively characterizing the diagenetic phase of a tight sandstone reservoir, and the specific steps are as follows:

[0064] Step 1, determine the diagenetic facies evaluation parameters and obtain the parameter sequence;

[0065] The evaluation parameters include displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0;

[0066] Reservoir Quality Factor

[0067] The above parameters are obtained through the casting thin section, scanning electron microscope, mercury injection and other test analyses of the samples;

[0068] Step 2, applying the grey correlation method, determining the correlation coefficient of the parameter sequence obtained in step 6, and calculating the correlation degree;

[0069] Step 3, construct multivariate comprehensive classification coefficient;

[0070] Establish a classification standard applicable to the diagenetic facies in the study area, and further upgrade the qualitative evaluation to a quantitative classification evaluation. Select seven parameters, namely porosity, permeability, clay mineral content, carbonate content, pore surface ratio, displacement pressure, and median pore throat radius, as the influencing parameters for constructing a multivariate comprehensive classification coefficient.

[0071] Through the study of the main controlling factors of physical properties of different diagenetic facies, it is found that for the characteristics of poor physical properties, strong heterogeneity, and complex microscopic pore structure of tight sandstone reservoirs, it is relatively difficult to evaluate with a single factor, and there may be intersections among multiple factors. The qualitative evaluation cannot well reflect the geological laws in the study area, and the evaluation results are not ideal, showing deviations from the actual situation. Often, due to intersections between single parameters, unclear boundaries, and different categories among multiple parameters, many data cannot be accurately distinguished, and the more parameters there are, the stronger the contradictions. Therefore, it is necessary to use a multi-factor comprehensive method to carry out quantitative evaluation of the diagenetic facies types in the study area, which can provide reliable guidance for the optimization of favorable areas in the next step.

[0072] Step 4: Divide the diagenetic facies types according to the multivariate comprehensive classification coefficient obtained in Step 3.

[0073] When Feci > 4, it is the chlorite film remaining intergranular pore facies.

[0074] When 2 < Feci ≤ 4, it is the feldspar dissolution facies.

[0075] When -1 < Feci ≤ 2, it is the clay mineral cemented micropore facies.

[0076] When Feci ≤ -1, it is the carbonate cemented dense facies.

[0077] By combining the grey correlation method with the multivariate comprehensive classification coefficient method, on the basis of determining the weight coefficients of each parameter, the division of diagenetic facies is quantified, greatly improving the accuracy of diagenetic facies division.

[0078] Step 5: Analyze the logging characteristics of the diagenetic facies types divided in Step 4 respectively, select sensitive logging curves, and use the Fisher discrimination method to establish a chart and obtain a discrimination function to achieve quantitative identification of diagenetic facies.

[0079] The sensitive logging curves include density, compensated neutron, acoustic time difference, and resistivity curves.

[0080] The discrimination function is:

[0081] F1 = 10.144AC + 5182.119DEN - 18.476CNL + 0.35RT - 7441.267;

[0082] F2 = 9.848AC + 5260.691DEN - 18.412CNL + 0.35RT - 7570.852;

[0083] F3=9.598AC+5294.353DEN-18.401CNL+0.35RT-7600.513;

[0084] F4=9.528AC+5366.449DEN-18.616CNL+0.34RT-7765.432;

[0085] Wherein, F1 represents the residual intergranular pore phase of chlorite film; F2 represents the feldspar dissolution phase; F3 represents the clay mineral cemented micropore phase; F4 represents the carbonate cemented dense phase; AC represents the resistivity curve; RT represents the acoustic time difference curve; DEN represents the density curve; CNL represents the compensated neutron curve.

[0086] The diagenetic phase obtained in step 4 needs to be realized based on the corresponding parameters obtained by experimental testing, but it is not applicable to some wells that have not been tested. Therefore, on the basis of the classification of diagenetic phase types, according to the analysis of its logging characteristics, the sensitive logging curve is selected, and then the chart is established through the Fisher discriminant method to obtain the discriminant function to discriminate the diagenetic phase type, so that all wells in the block are applicable, and the block no longer relies on experimental testing.

[0087] The Fisher discriminant analysis method is used to establish a map. By finding a projection matrix, the multidimensional data points are mapped to the low-dimensional space. Then the most suitable projection direction is optimized by analyzing the variance, which can achieve the purpose of dividing the different types of the total data body as much as possible. Then the discriminant function is constructed to achieve the quantitative identification of diagenetic facies. Through the establishment of the map, the distinction between different diagenetic facies has been greatly improved.

[0088] Example 2

[0089] Based on Example 1, the specific process of step 2 is as follows:

[0090] Step 2.1, the displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0 parameter sequence obtained in step 1 form a matrix, that is:

[0091]

[0092] Where X0=(X0(1),X0(2),…,X0(n)) T , n is the variable sequence length;

[0093] Step 2.2, using the linear normalization method, each parameter in the displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0 parameter sequence is dimensionless to obtain a dimensionless matrix;

[0094] The dimensionless expressions of each evaluation parameter are:

[0095]

[0096] Where, X i (k) represents the evaluation parameter; (X i (k)) min Indicates the minimum value of the same evaluation parameter; (X i (k)) max Indicates the maximum value of the same type of evaluation parameters; i = 0, 1, 2, 3, 4, 5; k = 1, 2, ..., n;

[0097] The dimensionless matrix is:

[0098]

[0099] Dimensionless processing can ensure the reliability of analysis results;

[0100] Step 2.3, calculate the correlation coefficient

[0101] Solve the absolute difference between the first column and the remaining columns in the dimensionless matrix and form an absolute difference matrix;

[0102] The expression for the absolute difference is:

[0103] Δ 0i (k)=|x0(k)-x i (k)|, i=1,2,3,4,5 (4)

[0104] The absolute difference matrix is:

[0105]

[0106] The first column in the dimensionless matrix is ​​the reference sequence, and the remaining sequences are comparison sequences;

[0107] The maximum number in the absolute difference matrix is ​​taken as the maximum difference, and the minimum number is taken as the minimum difference, specifically:

[0108] Δ max =max (Δ 0i (k)) (6)

[0109] Δ min =min (Δ 0i (k)) (7)

[0110] The data in the absolute difference matrix is ​​transformed according to formula (8) to obtain the correlation coefficient matrix;

[0111]

[0112] In the formula, ρ represents the resolution coefficient, which is 0.5;

[0113] Step 2.4, calculate the correlation

[0114] Correlation of displacement pressure Δ 01 is the average value of the first column in the correlation coefficient matrix;

[0115] Correlation of median radius Δ 02 is the average value of the second column in the correlation coefficient matrix;

[0116] Correlation of clay mineral content Δ 03 is the average value of the third column in the correlation coefficient matrix;

[0117] Correlation of carbonate content Δ 04 is the average value of the fourth column in the correlation coefficient matrix;

[0118] Correlation of face rate Δ 05 is the average value of the fifth column in the correlation coefficient matrix;

[0119] The greater the correlation, the greater the impact of this factor on the recovery factor improvement percentage.

[0120] Example 3

[0121] Based on Example 2, in step 3, the expression of the multivariate comprehensive classification coefficient is:

[0122]

[0123] In the formula, Indicates porosity, obtained through core physical property testing, %; k i is the permeability, ×10 -3 μm 2 ;c i is the face rate, %; R i is the average pore throat radius, μm; CLM i is the clay mineral content, %; CAM i is the carbonate content, %; Pc j is the displacement pressure, MPa, the subscript i is a single specific parameter; max is the maximum value of the parameter.

[0124] Example 4

[0125] The Chang 8 well in northern Shaanxi was used for verification.

[0126] The obtained displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0 parameter sequence constitutes a matrix, namely:

[0127]

[0128] Where X0=(X0(1),X0(2),…,X0(n)) T , n is the variable sequence length;

[0129] The parameter sequences of displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0 are shown in Table 1;

[0130] Table 1 Sequence data of evaluation parameters (partial)

[0131]

[0132] The linear normalization method is used to non-dimensionalize the parameters in the displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0 parameter sequence to obtain a dimensionless matrix.

[0133] The dimensionless expressions of each evaluation parameter are:

[0134]

[0135] Where, X i (k) represents the evaluation parameter; (X i (k)) min Indicates the minimum value of the same evaluation parameter; (X i (k)) max Indicates the maximum value of the same type of evaluation parameters; i = 0, 1, 2, 3, 4, 5; k = 1, 2, ..., n;

[0136] The dimensionless matrix is:

[0137]

[0138] The 6 evaluation parameter sequences without quantitative data are shown in Table 2;

[0139] Table 2 Dimensionless data table of each evaluation sequence (part)

[0140]

[0141] Calculate the correlation coefficient

[0142] Solve the absolute difference between the first column and the remaining columns in the dimensionless matrix and form an absolute difference matrix;

[0143] The expression for the absolute difference is:

[0144] Δ 0i (k)=|x0(k)-xi (k)|, i=1,2,3,4,5 (4)

[0145] The absolute difference matrix is:

[0146]

[0147] The first column in the dimensionless matrix is ​​the reference sequence, and the remaining sequences are comparison sequences;

[0148] The maximum number in the absolute difference matrix is ​​taken as the maximum difference, and the minimum number is taken as the minimum difference, specifically:

[0149] Δ max =max (Δ 0i (k)) (6)

[0150] Δ min =min (Δ 0i (k)) (7)

[0151] The data in the absolute difference matrix is ​​transformed according to formula (8) to obtain the correlation coefficient matrix;

[0152]

[0153] In the formula, ρ represents the resolution coefficient, which is 0.5;

[0154] Calculate the correlation:

[0155] Correlation of displacement pressure Δ 01 is the average value of the first column in the correlation coefficient matrix;

[0156] Correlation of median radius Δ 02 is the average value of the second column in the correlation coefficient matrix;

[0157] Correlation of clay mineral content Δ 03 is the average value of the third column in the correlation coefficient matrix;

[0158] Correlation of carbonate content Δ 04 is the average value of the fourth column in the correlation coefficient matrix;

[0159] Correlation of face rate Δ 05 is the average value of the fifth column in the correlation coefficient matrix;

[0160] The correlation coefficients are shown in Table 3;

[0161] Table 3 Correlation coefficient table (partial)

[0162]

[0163] Then the correlation relationship is: Δ02 >Δ 01 >Δ 05 >Δ 03 >Δ 04 ;

[0164] Based on the test analysis of casting thin sections, scanning electron microscopy, mercury injection, etc. of the Chang 8 samples in northern Shaanxi, the main influencing parameters of diagenetic facies were optimized. Then, the main controlling factors were determined according to the grey correlation method, and quantitative classification was carried out using the multivariate comprehensive classification coefficient. Chlorite film remaining intergranular pore facies: Feci > 4; feldspar dissolution facies: 2 < Feci ≤ 4; clay mineral cemented micropore facies: -1 < Feci ≤ 2; carbonate cemented dense facies: Feci ≤ -1;

[0165] The sensitive logging curves corresponding to different diagenetic facies of Well Chang 8 in northern Shaanxi are shown in Table 4;

[0166] Table 4 Logging response characteristics of different diagenetic facies

[0167]

[0168] As Figure 1 shown, using the Fisher discriminant method plate, the discriminant functions are obtained as follows:

[0169] F1 = 10.144AC + 5182.119DEN - 18.476CNL + 0.35RT - 7441.267;

[0170] F2 = 9.848AC + 5260.691DEN - 18.412CNL + 0.35RT - 7570.852;

[0171] F3 = 9.598AC + 5294.353DEN - 18.401CNL + 0.35RT - 7600.513;

[0172] F4 = 9.528AC + 5366.449DEN - 18.616CNL + 0.34RT - 7765.432;

[0173] Applying this method to accurately depict the planar distribution law of diagenetic facies of each sub-layer in the Chang 8 area of northern Shaanxi, guiding the optimization of planar geological "sweet spot" areas and the main contribution sections of vertical horizontal wells, it is expected to drive the effective utilization of 62 million tons of reserves.

[0174] As Figure 2 and Figure 3 shown, comparing the diagenetic facies types with experiments such as core, microscopic, and mercury injection, the correspondence is relatively strong, indicating that this method has good effects. Using this method to identify planar and vertical diagenetic facies provides a basis for the optimization of favorable areas and main contribution sections in the later stage.

[0175] The diagenetic facies types of Chang 8 in northern Shaanxi are divided according to the diagenetic characteristics. Diagenesis is the main factor leading to the dense reservoir and strong heterogeneity: the quantitative recovery of porosity evolution shows that diagenesis is the main reason for the dense reservoir (see Table 5 and Figure 4 As shown in Table 6, according to the characteristics of physical property changes in different diagenetic stages, combined with key reservoir parameters such as filling material characteristics, pore type, and surface ratio, the diagenetic phases are divided into four types, including chlorite film residual intergranular pore phase, feldspar dissolution phase, clay mineral cemented micropore phase, and carbonate cemented dense phase.

[0176] Table 5 Quantitative calculation method of sandstone porosity evolution

[0177]

[0178] Table 6 Statistics of diagenetic characteristics of different diagenetic phases

[0179]

[0180]

[0181] It can be seen from Table 6 that the calculation of porosity evolution is mainly intended to illustrate that diagenesis has a great influence on the reservoir. The change of porosity in different diagenetic stages is also accompanied by corresponding differences in permeability. Diagenesis is the main reason for reservoir compaction. Therefore, the diagenetic phase type has a good characterization effect on the reservoir quality. The diagenetic phase type divided by diagenetic characteristics is consistent with the one obtained by the discriminant function of the method of the present invention. Therefore, the method of the present invention has high credibility, objectivity and accuracy.

Claims

1. A method for quantitative characterization of diagenetic facies of tight sandstone reservoirs, characterized in that: The specific steps are as follows: Step 1: Determine the diagenetic facies evaluation parameters and obtain the parameter sequence; Step 2: Apply the grey relational analysis method to determine the correlation coefficients of the parameter sequence obtained in Step 1 and calculate the correlation degree; Step 3: Construct the multivariate comprehensive classification coefficient Feci; Step 4: Divide the diagenetic facies types according to the multivariate comprehensive classification coefficient obtained in Step 3; Step 5: Conduct logging characteristic analysis on the diagenetic facies types divided in Step 4 respectively, select the sensitive logging curves, and use the Fisher discrimination method to obtain the discrimination function to achieve quantitative identification of diagenetic facies.

2. The method for quantitative characterization of diagenetic facies of tight sandstone reservoir according to claim 1, characterized in that: In step 1, the evaluation parameters include displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, surface porosity X5 and reservoir quality factor X0. K represents the permeability, Indicates porosity.

3. The method for quantitative characterization of diagenetic facies of tight sandstone reservoir according to claim 1, characterized in that: The specific process of Step 2 is as follows: Step 2.1: Construct a matrix from the parameter sequences of the displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, pore porosity X5, and reservoir quality factor X0 obtained in Step 1, i.e.: Where X0=(X0(1),X0(2),…,X0(n)) T , n is the variable sequence length; Step 2.2: Use the linear normalization method to dimensionlessize each parameter in the parameter sequences of the displacement pressure X1, median radius X2, clay mineral content X3, carbonate content X4, pore porosity X5, and reservoir quality factor X0 to obtain a dimensionless matrix; The expression for dimensionlessizing each evaluation parameter is: In the formula, x i (k) represents the evaluation parameter; (X i (k)) min Indicates the minimum value of the same evaluation parameter; (X i (k)) max Indicates the maximum value of the same type of evaluation parameters; i = 0, 1, 2, 3, 4, 5; k = 1, 2, ..., n; The dimensionless matrix is: Step 2.3: Calculate the correlation coefficients Solve the absolute differences between the first column and the remaining columns in the dimensionless matrix to form an absolute difference matrix; The expression for the absolute difference is: Δ 0i (k)=|x0(k)-x i (k)|,i=1,2,3,4,5 (4) The absolute difference matrix is: Take the largest number in the absolute difference matrix as the maximum difference and the smallest number as the minimum difference, specifically: D max =max(Δ 0i (k)) (6) D min =min(Δ 0i (k)) (7) Transform the data in the absolute difference matrix according to formula (8) to obtain a correlation coefficient matrix; In the formula, ρ represents the resolution coefficient, taking 0.5; Step 2.4: Calculate the correlation degree Correlation of displacement pressure Δ 01 is the average value of the first column in the correlation coefficient matrix; Correlation of median radius Δ 02 is the average value of the second column in the correlation coefficient matrix; Correlation of clay mineral content Δ 03 is the average value of the third column in the correlation coefficient matrix; Correlation of carbonate content Δ 04 is the average value of the fourth column in the correlation coefficient matrix; Correlation of face rate Δ 05 is the average value of the fifth column in the correlation coefficient matrix.

4. The method for quantitative characterization of diagenetic facies of tight sandstone reservoir according to claim 1, characterized in that: In Step 3, the expression for the multivariate comprehensive classification coefficient is: In the formula, Indicates porosity, obtained through core physical property testing, %; k i is the permeability, ×10 -3 μm 2 ; c i is the face rate, %; R i is the average pore throat radius, μm; CLM i is the clay mineral content, %; CAM i is the carbonate content, %; Pc i is the displacement pressure, MPa, i is a single specific parameter; max is the maximum value of the parameter.

5. The method for quantitative characterization of diagenetic facies of tight sandstone reservoir according to claim 1, characterized in that: In Step 4, the diagenetic facies type division standard is: Feci > 4, for the chlorite film remaining intergranular pore facies; 2 < Feci ≤ 4, for the feldspar dissolution facies; -1 < Feci ≤ 2, for the clay mineral cemented micropore facies; Feci ≤ -1, for the carbonate cemented dense facies.

6. The method for quantitative characterization of diagenetic facies of tight sandstone reservoir according to claim 1, characterized in that: In Step 5, the sensitive logging curves include density, compensated neutron, acoustic time difference, and resistivity curves.

7. The method for quantitative characterization of diagenetic facies of tight sandstone reservoir according to claim 1, characterized in that: In Step 5, the discrimination functions are: F1 = 10.144AC + 5182.119DEN - 18.476CNL + 0.35RT - 7441.267; F2 = 9.848AC + 5260.691DEN - 18.412CNL + 0.35RT - 7570.852; F3 = 9.598AC + 5294.353DEN - 18.401CNL + 0.35RT - 7600.513; F4 = 9.528AC + 5366.449DEN - 18.616CNL + 0.34RT - 7765.432; In the formula, F1 represents the chlorite film remaining intergranular pore facies; F2 represents the feldspar dissolution facies; F3 represents the clay mineral cemented micropore facies; F4 represents the carbonate cemented dense facies; AC represents the resistivity curve; RT represents the acoustic time difference curve; DEN represents the density curve; CNL represents the compensated neutron curve.

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