Evaluation method of buried hill reservoir permeability considering secondary porosity coefficient

Abstract

The present application relates to a kind of buried hill reservoir permeability evaluation method considering secondary porosity coefficient, comprising the following steps: pre-processing electroimaging logging data, obtain electroimaging dynamic image and electroimaging static image;Electroimaging logging data is scaled processing;The porosity value of buried hill reservoir and corresponding porosity spectrum and porosity spectrum cutoff value are obtained;According to the porosity spectrum and porosity spectrum cutoff value, secondary porosity is obtained, and according to the secondary porosity, secondary porosity coefficient is obtained;Determine secondary porosity coefficient interval;Based on the secondary porosity coefficient interval, the regression relationship between secondary porosity and nuclear magnetic calculation permeability is established;Permeability is determined based on the regression relationship.The permeability obtained by regression based on the present application is high in accuracy and strong in practicality;Secondly, the regression permeability curve obtained by the present application is highly consistent with the nuclear magnetic calculation permeability.Therefore, the present application can replace the method of nuclear magnetic logging calculation permeability to some extent.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of buried hill reservoir permeability calculation method, and particularly relates to a buried hill reservoir permeability evaluation method considering secondary pore coefficient. BACKGROUND

[0002] The borehole wall micro-resistivity imaging logging is widely applied in the quantitative analysis and fine evaluation of strata due to high resolution, large coverage area and intuitive processing results. The quantitative evaluation of the porosity distribution characteristics of the reservoir by using the electrical imaging logging data is one of the aspects. The porosity spectrum is the frequency histogram of the porosity value, and the electrical imaging logging instrument measures the apparent conductivity value of the borehole wall. The apparent resistivity value of the borehole wall can be obtained by conversion. The apparent resistivity matrix of the borehole wall is converted into the porosity matrix of the borehole wall by using the Archie formula. The porosity value in a certain depth range is quantitatively counted in different porosity value intervals. The statistical results are plotted into the frequency histogram in the coordinate system, and the so-called porosity spectrum is obtained.

[0003] According to the Archie formula, S n =aR w / (φ m R t ), for the calibrated FMI image, the basic reflection is the conductivity in the flushing zone near the borehole wall, so it should satisfy the Archie formula of the flushing zone:

[0004]

[0005] In the formula, S xo represents the water saturation of the flushing zone, a represents the lithology coefficient, Rmf represents the mud filtrate resistivity, φ represents the porosity, m represents the cementation index, n represents the saturation index, and R xo represents the resistivity of the flushing zone.

[0006] If the approximate assumption S xo =1, a=1, m=n=2, then the above formula becomes . Therefore, after R mf is known, the porosity can be obtained from the color scale reflecting the size of R xo in the FMI image.

[0007] The porosity spectrum analysis program of Schlumberger Company can calculate the porosity of different pore sizes and distinguish primary pores and secondary pores, and is highly recognized in the industry. Through the borehole electrical imaging data, an image window is selected, the porosity size of each imaging logging pixel point in the window is calculated by the Archie formula, the contribution share (frequency) of the porosity in different intervals in the unit is counted, and the statistical distribution graph of the porosity value, i.e. the porosity frequency distribution curve, is drawn, so that the porosity distribution in the formation corresponding to the window is understood.

[0008] The permeability is mainly divided into "total permeability" and "matrix permeability". At present, the "total permeability" is generally calculated by the Stoneley wave, and the "matrix porosity" is calculated by the core porosity permeability regression and the nuclear magnetic logging.

[0009] In the buried hill reservoir, the general core porosity permeability regression method is not applicable due to the special lithology (mainly granite); although the calculation result of the nuclear magnetic logging is accurate, the nuclear magnetic logging is currently only used in part of the exploration wells, and the coverage is low, and therefore, at present, a logging method with high coverage and strong applicability is urgently needed to evaluate the buried hill reservoir mechanism permeability. SUMMARY

[0010] In view of the above problems, the purpose of the present application is to provide a buried hill reservoir permeability evaluation method considering secondary pore coefficient, the permeability obtained by the present application is high in accuracy and strong in practicality; secondly, the permeability curve obtained by the present application is high in coincidence degree with the nuclear magnetic calculation permeability. Therefore, the present application can replace the method of calculating the permeability by the nuclear magnetic logging to a certain extent.

[0011] To achieve the above purpose, the present application adopts the following technical solutions:

[0012] In the first aspect, the present application provides a buried hill reservoir permeability evaluation method considering secondary pore coefficient, comprising the following steps:

[0013] Pretreatment is performed on the electrical imaging logging data to obtain an electrical imaging dynamic image and an electrical imaging static image;

[0014] The electrical imaging logging data is subjected to scaling processing to obtain scaled electrical imaging logging data, so that the electrical imaging logging data is close in color to the electrical imaging dynamic image and the electrical imaging static image;

[0015] The porosity value of the buried hill reservoir is obtained according to the electrical imaging logging data;

[0016] The porosity spectrum and the porosity spectrum cutoff value corresponding to the electrical imaging logging data are obtained according to the porosity value;

[0017] determining secondary porosity based on the porosity spectrum and the porosity spectrum cutoff value, and determining a secondary porosity coefficient based on the secondary porosity;

[0018] determining a secondary porosity coefficient interval;

[0019] establishing a regression relationship between the secondary porosity and the NMR calculated permeability based on the secondary porosity coefficient interval;

[0020] determining the permeability based on the regression relationship.

[0021] Further, the method further comprises determining a general classification of the lithology and the reservoir space type of the buried hill reservoir, and determining the NMR calculated permeability based on the general classification of the lithology and the reservoir space of the buried hill reservoir.

[0022] Further, the determining the lithology of the buried hill reservoir comprises determining the lithology of the buried hill reservoir based on conventional and electrical imaging logging data, geological data, core data and analytical chemistry data.

[0023] Further, the determining the general classification of the reservoir space type comprises classifying the reservoir space of the buried hill reservoir based on the lithology of the buried hill reservoir, the electrical imaging dynamic image and the electrical imaging static image, the core photograph and the core slice.

[0024] Further, the calibrating the electrical imaging logging data comprises:

[0025] determining the medium resistivity and the pad data of the buried hill reservoir, and determining the electrical imaging resistivity contour map and the calibration curve and the medium resistivity curve correction map based on the medium resistivity and the pad data;

[0026] passing as many broken lines in the electrical imaging resistivity contour map as possible through the area with high resistivity density in the resistivity contour map so that the average value of the resistivity of the calibration curve and the original curve is as coincident as possible, and obtaining the electrical imaging logging data.

[0027] Further, the determining the porosity value of the buried hill reservoir based on the electrical imaging logging data comprises determining the porosity value of the buried hill reservoir based on the calibrated electrical imaging logging data, the shallow lateral resistivity and the multi-mineral model interpretation.

[0028] Further, the porosity value is converted into the porosity spectrum corresponding to the electrical imaging logging data through the Archie formula calculation.

[0029] Further, the secondary porosity coefficient is calculated according to formula (1):

[0030]

[0031] Wherein, VISO, PHIT_AVE are respectively secondary porosity and average porosity calculated by electrical imaging, and V_P refers to secondary porosity coefficient.

[0032] Further, the determination of the secondary porosity coefficient interval comprises making the regression coefficient between the secondary porosity and the nuclear magnetic permeability in the interval reach above 0.8, wherein when the regression coefficient is equal to 0.8, the secondary porosity coefficient is the lower limit of the secondary porosity coefficient interval.

[0033] Further, within the porosity coefficient interval, a regression relationship between the secondary porosity and the nuclear magnetic permeability is established, as shown in formula (2):

[0034]

[0035] In formula (2), A and B are both constants, and PERM is the nuclear magnetic permeability;

[0036] According to the transformation of the formula (2), formula (3) is obtained:

[0037]

[0038] Further, within the remaining interval of the porosity coefficient interval, a regression relationship between the secondary porosity and the core porosity and permeability data is established, and the corresponding permeability is obtained.

[0039] In the second aspect, the present application provides a buried hill reservoir permeability evaluation device considering secondary porosity coefficient, comprising:

[0040] A first processing unit is configured to pre-process electrical imaging logging data to obtain an electrical imaging dynamic image and an electrical imaging static image;

[0041] A second processing unit is configured to scale process the electrical imaging logging data to obtain scaled electrical imaging logging data, so that the electrical imaging logging data is close to the electrical imaging dynamic image and the static image in color;

[0042] A third processing unit is configured to obtain a porosity value of a buried hill reservoir according to the electrical imaging logging data;

[0043] A fourth processing unit is configured to obtain a porosity spectrum and a porosity spectrum cutoff value corresponding to the electrical imaging logging data according to the porosity value;

[0044] A fifth processing unit is configured to obtain a secondary porosity according to the porosity spectrum and the porosity spectrum cutoff value, and obtain a secondary porosity coefficient according to the secondary porosity;

[0045] A sixth processing unit is configured to determine a secondary porosity coefficient interval;

[0046] a seventh processing unit configured to establish a regression relationship between the secondary porosity and the nuclear magnetic permeability based on the secondary porosity coefficient interval;

[0047] a ninth processing unit configured to determine the permeability based on the regression relationship.

[0048] In a third aspect, the present application provides a computer readable storage medium storing computer instructions for implementing the method for evaluating the buried hill reservoir permeability considering the secondary porosity coefficient when executed by a processor.

[0049] In a fourth aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for evaluating the buried hill reservoir permeability considering the secondary porosity coefficient when executing the computer program.

[0050] The present application has the following advantages due to the above technical solutions.

[0051] 1. The electrical imaging logging has been the main method for evaluating the buried hill reservoir, especially the fracture-related parameters, due to its intuitive and high resolution characteristics. Therefore, the electrical imaging logging of the present application is widely used in the buried hill reservoir, and can effectively make up for the lack of nuclear magnetic logging data.

[0052] 2. The main principle of the present application is to use the electrical imaging to calculate the secondary porosity and the nuclear magnetic permeability fitting, and to obtain the corresponding calculation formula, so as to be not affected by the lithology. At the same time, due to the high resolution of the electrical imaging logging, the accuracy of the final calculation result is high.

[0053] 3. Only one high-precision fitting (regression coefficient reaches 0.8) is needed, and the present application is applicable in the whole well area, has strong practicability, is convenient and fast, and through actual data verification, it is shown that the present application also has certain popularization in other blocks. BRIEF DESCRIPTION OF DRAWINGS

[0054] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the present application. Throughout the drawings, like reference numerals will be used to designate like components.

[0055] In the drawings:

[0056] Figure 1 is a 1:200 electrical imaging dynamic and static map of the buried hill reservoir of K well;

[0057] Figure 2 is a 1:200 logging map of the longitudinal lithology and reservoir space classification of the buried hill reservoir of K well;

[0058] Figure 3 is the K well electrical imaging resistivity contour map;

[0059] Figure 4 is the K well calibration curve and middle resistivity curve correction map;

[0060] Figure 5 is the K well electrical imaging dynamic and static image and calibration image comparison map;

[0061] Figure 6 is the K well porosity spectrum cutoff value comparison map calculated by different methods;

[0062] Figure 7 is the K well secondary porosity VISO and nuclear magnetic calculated permeability PERM crossplot;

[0063] Figure 8 is the K well buried hill reservoir average porosity PHIT_AVE histogram;

[0064] Figure 9 is the K well secondary porosity coefficient interval lower limit value calibration map when the regression coefficient is 0.8;

[0065] Figure 10 is the K well secondary porosity VISO and nuclear magnetic calculated permeability PERM regression relationship diagram within the determined secondary porosity coefficient interval;

[0066] Figure 11 is the L well electrical imaging calculated permeability and nuclear magnetic calculated permeability and core test permeability comparison map;

[0067] Figure 12 is the O well calculated permeability based on the present application and core permeability comparison map. DETAILED DESCRIPTION

[0068] Exemplary embodiments of the present application will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms without being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

[0069] The embodiment of the present application provides a buried hill reservoir permeability evaluation method considering secondary porosity coefficient, comprising the steps of: preprocessing electrical imaging logging data to obtain electrical imaging dynamic images and electrical imaging static images; performing calibration processing on the electrical imaging logging data to obtain calibrated electrical imaging logging data, so that the electrical imaging logging data is close in color to the dynamic and static images; obtaining a porosity value of the buried hill reservoir according to the electrical imaging logging data; obtaining a porosity spectrum corresponding to the electrical imaging logging data and a porosity spectrum cutoff value according to the porosity value; obtaining secondary porosity according to the porosity spectrum and the porosity spectrum cutoff value, and obtaining a secondary porosity coefficient according to the secondary porosity; determining a secondary porosity coefficient interval; establishing a regression relationship between the secondary porosity and the nuclear magnetic calculation permeability based on the secondary porosity coefficient interval; and determining the permeability based on the regression relationship. The permeability obtained by regression based on the present application has high accuracy and strong practicability; secondly, the permeability curve obtained by regression based on the present application has high consistency with the nuclear magnetic calculation permeability. Therefore, the present application can replace the method of calculating the permeability by nuclear magnetic logging to some extent.

[0070] Embodiment 1

[0071] Taking K well and L well drilled in the same block and O well in another block as examples.

[0072] The buried hill reservoir permeability evaluation method considering secondary porosity coefficient comprises the steps of:

[0073] S1, arranging the electrical imaging logging data of the buried hill reservoir of K well, preprocessing the electrical imaging logging data, and ensuring the quality of the processed dynamic and static images, the 1:200 electrical imaging dynamic and static images of the buried hill reservoir of K well are as shown in Figure 1 .

[0074] S2, based on the conventional and high-end logging data, geological data, core data and analysis test data of the buried hill reservoir of K well, the logging response characteristics of different lithologies in the buried hill reservoir of K well are analyzed. Based on the logging response characteristics, the electrical imaging dynamic and static images are combined with the core photos and core slices to determine the reservoir space types and approximate distribution of the buried hill reservoir of K well, as shown in Figure 2 .

[0075] Since the nuclear magnetic logging is greatly affected by the reservoir space form, the nuclear magnetic calculation permeability result is also affected by the reservoir space form. The reservoir space of the buried hill section in the block mainly presents the "double-pore medium characteristics", so the present method is mainly for the buried hill reservoir with "double-pore medium characteristics". For other types of reservoir spaces, the present application does not have targeted research, and it cannot be determined whether it has universality. Therefore, the step S2 is mainly to determine whether the reservoir is a double-pore medium characteristic reservoir space.

[0076] The technical principle of nuclear magnetic calculation permeability is:

[0077] NMR logging can directly characterize the pore structure, so the relationship between NMR logging results and permeability is the closest. The current mainstream method for calculating permeability based on NMR logging results is Coates formula:

[0078]

[0079] In the formula: Q is the distribution coefficient for evaluating the concentration of pore distribution of a specific size in the reservoir; S wirr is irreducible water saturation, K represents NMR calculated permeability, C represents an empirical coefficient, m represents cementation exponent, and φ represents NMR calculated porosity.

[0080] S3, calibrate the electrical imaging logging data to obtain electrical imaging logging data.

[0081] 1) Determine the middle resistivity and pad data of the buried hill reservoir, and obtain the electrical imaging resistivity contour map and the calibration curve and the middle resistivity curve correction map according to the middle resistivity and pad data;

[0082] 2) The broken line in the electrical imaging resistivity contour map passes through the area with high resistivity density in the resistivity contour map as much as possible, so that the average value of the calibration curve and the original curve resistivity is as much as possible. Coincide;

[0083] 3) When the coincidence is the best, save it, and you can get the scaled data of each pad. Then combine the scaled data of each pad, select the scaled pad data in the Pad concatenation and orientation workflow in the Processing module, and you can get the combined scaled image.

[0084] Determine the quality of the scaled image mainly by comparing it with the electrical imaging static image. The closer the color, the better the processing quality. Figure 5 The comparison chart of the electrical imaging dynamic and static images and the scaled image of K well.

[0085] S4, combine the scaled electrical imaging logging data, shallow lateral resistivity and multi-mineral model interpretation to obtain the porosity of the buried hill reservoir. Then convert the porosity to the porosity spectrum corresponding to the electrical imaging logging data through the Archie formula calculation.

[0086] The calculation of the electrical imaging porosity spectrum is essentially based on the Archie formula. The rock electrical coefficient is used to determine the "cementation index m," or "Archie Cementation Exp" parameter, in the Archie formula. Based on rock electrical experiments on the K Well buried-hill reservoir, the Archie Cementation Exp parameter for the K Well buried-hill reservoir is 1.75. Therefore, the cementation index is modified from 1.8 to 1.75, while the other parameters remain unchanged. Porosity is converted to a porosity spectrum using the Archie formula.

[0087] S5. While calculating the porosity spectrum, the porosity spectrum cutoff value can be calculated. By calculating the appropriate cutoff value, accurate secondary porosity can be obtained.

[0088] The methods for calculating the porosity spectrum cutoff value in the Techlog software platform mainly include the WN method, TSR method and SDR method. The Mannal method is a manual judgment method and is generally not selected. Figure 6 As shown, the porosity spectrum cutoff values ​​calculated by different methods vary somewhat. In Well K, the WN method exhibits significant errors and is therefore not considered. The TSR method exhibits significant volatility and exhibits significant errors in some intervals. Therefore, the SDR method is used in Well K to calculate the porosity spectrum cutoff value and secondary porosity VISO. Furthermore, the average porosity PHIT_AVE is also calculated when calculating the electrical imaging porosity spectrum.

[0089] The secondary porosity VISO can be combined with the secondary porosity coefficient V_P, and the specific formula (1) is as follows.

[0090] The calculated secondary porosity VISO is compared with the porosity PHIT_AVE calculated when calculating the electrical imaging porosity spectrum to obtain the secondary porosity coefficient V_P. The specific formula is:

[0091]

[0092] Where VISO and PHIT_AVE are the secondary porosity and average porosity calculated by electrical imaging, respectively.

[0093] S6. Since the secondary porosity VISO and the permeability calculated by nuclear magnetic resonance (NMR) PERM have a good regression relationship within a certain secondary porosity coefficient interval, determining the secondary porosity coefficient interval includes: making the regression relationship coefficient between the secondary porosity and the permeability calculated by nuclear magnetic resonance (NMR) within the interval reach above 0.8, wherein when the regression coefficient is equal to 0.8, the secondary porosity coefficient is the lower limit of the secondary porosity coefficient interval.

[0094] By plotting the secondary porosity VISO of Well K and the permeability PERM calculated by NMR, we can find that within a certain range, there is a good regression relationship between the two, such as Figure 7Therefore, first, histogram analysis is performed on the average porosity PHIT_AVE of the buried hill reservoir of the K well (see Figure 8 ), and it can be seen that the average porosity of the buried hill reservoir of the K well mainly distributes in the interval of 0-15%; second, the average porosity PHIT_AVE is plotted against the secondary porosity coefficient V_P, and the Interactive selection button in the Techlog software can be used to simultaneously display the corresponding points in the cross plot of the secondary porosity VISO and the nuclear magnetic calculation permeability PERM when the points in the cross plot of PHIT_AVE and V_P are selected. Based on the criterion that the average porosity is less than 15%, the fracture-cave coefficient V_P is filled downward from 100% until the correlation coefficient of the regression curve of the selected points in the cross plot of the secondary porosity VISO and the nuclear magnetic calculation permeability PERM reaches 0.8. According to this method, it can be determined that the range in which the secondary porosity VISO and the nuclear magnetic calculation permeability PERM have good correlation in the K well is [0.33, 1], and the specific relationship diagram is shown in Figure 9 、 Figure 10 .

[0095] S7, based on the determined V_P interval of the K well, a corresponding regression relationship is established between the secondary porosity VISO and the nuclear magnetic calculation permeability PERM. The regression formula is:

[0096]

[0097] The correlation coefficient R 2 of the formula is 0.8020.

[0098] The formula is applied to the L well which also drilled the buried hill reservoir in the same block, and in the remaining interval of the secondary porosity coefficient V_P, the porosity-permeability regression equation of the L well is:

[0099]

[0100] The correlation coefficient R 2 of the formula is 0.8284.

[0101] Finally, the calculated results are shown in Figure 11 . Figure 11 The last two from left to right are: comparison between the electrical imaging regression permeability and the core permeability; comparison between the nuclear magnetic calculation permeability and the core permeability, wherein the curve is the permeability obtained based on the regression of the application, the dotted line is the nuclear magnetic calculation permeability, and the dot is the core permeability. From the comparison results, it can be seen that: first, after comparison of the core permeability, it can be found that the permeability obtained based on the regression of the application has high accuracy and strong practicability; second, the permeability curve obtained based on the regression of the application has high consistency with the nuclear magnetic calculation permeability. Therefore, the application can replace the method of calculating the permeability by nuclear magnetic logging to some extent.

[0102] O well is another block which also drilled through buried hill block. O well mainly develops granite, and the lithology is inconsistent with K well and L well, and the average porosity PHIT_AVE mainly distributes in the range of 0-10%. But in the buried hill reservoir of O well, the pore type reservoir is also developed, and shows the characteristics of double pore media. Since there is no nuclear magnetic logging in the well, the fitting formula of K well and the secondary pore coefficient V_P interval are applied in O well, and the remaining interval is used to calculate the permeability by using the porosity-permeability regression formula of O well, Figure 12 is the final result. In Figure 12 , the last curve in the middle is the permeability calculated based on the present application, and the round dots are the core permeability. It can be found by comparison that the matching degree is high, proving that the present application has strong applicability, and can be used in different lithology conditions, and has certain popularization.

[0103] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating the permeability of a buried hill reservoir taking into account the secondary porosity coefficient, characterized in that: Including steps: Preprocess the electrical imaging logging data to obtain electrical imaging dynamic images and electrical imaging static images; Calibrate the electrical imaging logging data to obtain calibrated electrical imaging logging data, so that the electrical imaging logging data is close in color to the electrical imaging dynamic image and the electrical imaging static image; Obtaining a porosity value of the buried hill reservoir according to the electrical imaging logging data; Obtaining a porosity spectrum and a porosity spectrum cutoff value corresponding to the electrical imaging logging data according to the porosity value; Obtaining secondary porosity according to the porosity spectrum and the porosity spectrum cutoff value, and obtaining a secondary porosity coefficient according to the secondary porosity; Determine the secondary porosity coefficient interval; Based on the secondary porosity coefficient interval, a regression relationship between secondary porosity and permeability calculated by nuclear magnetic resonance is established; determining a permeability based on the regression relationship; The calculation of the secondary porosity coefficient is shown in formula (1): Among them, VISO and PHIT_AVE are the secondary porosity and average porosity calculated by electrical imaging, respectively, and V_P refers to the secondary porosity coefficient; Determining the secondary porosity coefficient interval includes making the regression relationship coefficient between the secondary porosity and the permeability calculated by nuclear magnetic resonance within the interval reach 0.8 or above, wherein when the regression coefficient is equal to 0.8, the secondary porosity coefficient is the lower limit of the secondary porosity coefficient interval; In the porosity coefficient range, a regression relationship between secondary porosity and permeability calculated by nuclear magnetic resonance is established, as shown in formula (2): In formula (2), A and B are constants, PERM is the permeability calculated by NMR; According to the formula (2), the formula (3) is obtained by transformation: ； In the remaining interval except the porosity coefficient interval, a regression relationship between the secondary porosity and the core porosity and permeability data is established to obtain the corresponding permeability.

2. The method for evaluating the permeability of buried hill reservoirs considering secondary porosity according to claim 1, characterized in that: The method also includes determining the approximate classification of the lithology and reservoir space type of the buried hill reservoir, and obtaining the NMR-calculated permeability based on the approximate classification of the lithology and reservoir space of the reservoir.

3. The method for evaluating the permeability of buried hill reservoirs considering secondary porosity according to claim 2, characterized in that: The determining of the lithology of the buried hill reservoir comprises: determining the lithology of the buried hill reservoir based on conventional and electrical imaging logging data, geological data, core data and analytical chemical data.

4. The method for evaluating the permeability of a buried hill reservoir considering secondary porosity according to claim 2, wherein: The determining of the rough classification of reservoir space types includes: roughly classifying the reservoir space of the buried hill reservoir in combination with the lithology of the buried hill reservoir, the electrical imaging dynamic image and the electrical imaging static image, core photos and core slices.

5. The method for evaluating the permeability of buried hill reservoirs considering secondary porosity according to claim 1, characterized in that: Calibration processing of electrical imaging logging data includes: Determine the medium resistivity and plate data of the buried hill reservoir, and obtain an electrical imaging resistivity contour map and a calibration curve and a medium resistivity curve correction map based on the medium resistivity and plate data; The broken lines in the electrical imaging resistivity contour map are passed through the area with high resistivity density in the resistivity contour map as much as possible so that the scale curve and the resistivity average value of the original curve are overlapped as much as possible, and the electrical imaging logging data are obtained.

6. The method for evaluating buried hill reservoir permeability considering secondary porosity according to claim 1, characterized in that: Obtaining the porosity value of the buried hill reservoir according to the electrical imaging logging data includes: obtaining the porosity value of the buried hill reservoir by combining the calibrated electrical imaging logging data, shallow lateral resistivity and multi-mineral model interpretation.

7. The method for evaluating buried hill reservoir permeability considering secondary porosity according to claim 6, characterized in that: The porosity value is converted into a porosity spectrum corresponding to the electrical imaging logging data by calculating the Archie formula.

8. A buried hill reservoir permeability evaluation device taking into account secondary porosity, comprising: The first processing unit is used to pre-process the electrical imaging logging data to obtain electrical imaging dynamic images and electrical imaging static images; a second processing unit configured to scale the electrical imaging logging data to obtain scaled electrical imaging logging data, so that the electrical imaging logging data has a color close to that of the electrical imaging dynamic image and the electrical imaging static image; a third processing unit, configured to obtain a porosity value of a buried hill reservoir according to the electrical imaging logging data; a fourth processing unit, configured to obtain a porosity spectrum and a porosity spectrum cutoff value corresponding to the electrical imaging logging data according to the porosity value; a fifth processing unit, configured to obtain a secondary porosity according to the porosity spectrum and the porosity spectrum cutoff value, and obtain a secondary porosity coefficient according to the secondary porosity; a sixth processing unit, for determining a secondary porosity coefficient interval; a seventh processing unit, configured to establish a regression relationship between secondary porosity and permeability calculated by nuclear magnetic resonance based on the secondary porosity coefficient interval; a ninth processing unit, configured to determine a permeability based on the regression relationship; The calculation of the secondary porosity coefficient is shown in formula (1): Among them, VISO and PHIT_AVE are the secondary porosity and average porosity calculated by electrical imaging, respectively, and V_P refers to the secondary porosity coefficient; Determining the secondary porosity coefficient interval includes making the regression relationship coefficient between the secondary porosity and the permeability calculated by nuclear magnetic resonance within the interval reach 0.8 or above, wherein when the regression coefficient is equal to 0.8, the secondary porosity coefficient is the lower limit of the secondary porosity coefficient interval; In the porosity coefficient range, a regression relationship between secondary porosity and permeability calculated by nuclear magnetic resonance is established, as shown in formula (2): In formula (2), A and B are constants, PERM is the permeability calculated by NMR; According to the formula (2), the formula (3) is obtained by transformation: ； In the remaining interval except the porosity coefficient interval, a regression relationship between the secondary porosity and the core porosity and permeability data is established to obtain the corresponding permeability.

9. A computer-readable storage medium, characterized in that Computer instructions are stored, and when the computer instructions are executed by a processor, the method for evaluating the permeability of a buried hill reservoir considering the secondary porosity coefficient according to any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method implements the buried hill reservoir permeability evaluation method considering the secondary porosity coefficient as claimed in any one of claims 1 to 7 when the processor executes the computer program.

Images