Reservoir clay porosity calculation method, device, electronic equipment and medium

Abstract

Disclosed are a method, device, electronic device, and medium for calculating reservoir clay porosity. The method may include: obtaining discrete clay porosity data from CMR nuclear magnetic resonance logging; determining the well logging data corresponding to the discrete CMR clay porosity data, and then determining a fitting equation between the discrete CMR clay porosity data and the well logging data; and calculating the clay porosity of a target well based on the fitting relationship and the well logging data. The present invention inverts reservoir clay porosity using conventional well logging information, enabling accurate and effective calculation of reservoir clay porosity. This method avoids the limitations of calculating reservoir clay porosity solely based on clay content, which is influenced by factors such as the rich organic matter content of shale and the complex types of clay minerals, thereby improving applicability. It also improves the accuracy of clay porosity logging inversion, enabling better service for oil and gas exploration and development.

Description

Technical Field

[0001] The present invention relates to the fields of oil and gas exploration and development and geophysics, and more specifically, to a method, device, electronic equipment and medium for calculating reservoir clay porosity. Background Art

[0002] In the field of oil and gas exploration and development, porosity reflects the fluid storage capacity and is one of the key parameters for reservoir parameter evaluation. Clay pores, as a type of pore, play an important role in evaluating the gas content of oil and gas reservoirs.

[0003] Currently, there are two methods to determine the clay porosity of oil and gas reservoirs: experimental methods and well logging technology. Specifically, they include:

[0004] Clay porosity is determined by comparing the porosity of a pure clay layer with the volume percentage of the adjacent clay. However, this method has some problems in calculating clay porosity: a pure clay layer is an idealized state and does not exist in actual formations. Therefore, the final calculated clay porosity is difficult to reflect the actual clay porosity of the formation;

[0005] The clay content is used to calculate the clay bound water porosity. This method only considers a single factor when calculating the clay bound water porosity, and its applicability to calculating the clay bound water porosity in oil and gas reservoirs is limited.

[0006] The porosity of oil and gas bound water is calculated using the clay volume content and the clay porosity at 100% clay content. In this method, the 100% clay content condition is an idealized state, and the value is greatly affected by human factors and has uncertainty.

[0007] The clay porosity is calculated by combining the clay volume content calculated by well logging with the total porosity at 100% clay content. In this method, 100% clay content is an idealized state, and the value is affected by many human factors and has uncertainty.

[0008] GRI measurements are performed on cores to obtain the irreducible water porosity, but the measurement time of this experiment is long and difficult to apply widely. In addition, the measurement results are closely related to the particle size of the core sample crushing and are greatly affected by human factors.

[0009] Therefore, it is necessary to develop a reservoir clay porosity calculation method, device, electronic equipment and medium.

[0010] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0011] The present invention proposes a reservoir clay porosity calculation method, device, electronic equipment and medium, which can invert reservoir clay porosity through conventional logging information, and can accurately and effectively calculate reservoir clay porosity; avoid the limitations of simply using clay content to calculate reservoir clay porosity due to factors such as rich organic matter and complex clay mineral types, thereby improving applicability; improve the accuracy of clay porosity logging inversion, and can better serve oil and gas exploration and development.

[0012] In a first aspect, an embodiment of the present disclosure provides a method for calculating reservoir clay porosity, comprising:

[0013] Obtain discrete clay porosity data from CMR nuclear magnetic logging;

[0014] Determining the well logging data corresponding to the CMR nuclear magnetic well logging clay porosity discrete data, and then determining a fitting equation between the CMR nuclear magnetic well logging clay porosity discrete data and the well logging data;

[0015] The clay porosity of the target well is calculated based on the fitting relationship and the logging data.

[0016] Preferably, obtaining discrete clay porosity data from CMR nuclear magnetic logging includes:

[0017] Obtain the total porosity curve and effective porosity curve of CMR nuclear magnetic logging;

[0018] Calculate the clay porosity of CMR nuclear magnetic logging and obtain the clay porosity curve of CMR nuclear magnetic logging;

[0019] The CMR nuclear magnetic logging clay porosity curve is sampled to obtain CMR nuclear magnetic logging clay porosity discrete data.

[0020] Preferably, the clay porosity of CMR nuclear magnetic logging is calculated by formula (1):

[0021] CLAY_CMR=MRP_CMR-FFV_3MS_CMR (1)

[0022] Among them, CLAY_CMR is the clay porosity obtained by CMR nuclear magnetic logging, MRP_CMR is the total porosity obtained by CMR nuclear magnetic logging, and FFV_3MS_CMR is the effective porosity obtained by CMR nuclear magnetic logging.

[0023] Preferably, determining the fitting equation between the clay porosity discrete data and the well logging data comprises:

[0024] Performing a sensitivity analysis on the discrete CMR clay porosity data and the well logging data to obtain a relationship expression between the discrete CMR clay porosity data and each well logging data;

[0025] The fitting equation is established based on the well logging data with a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data.

[0026] Preferably, establishing the fitting equation based on the well logging data having a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data includes:

[0027] Calculate the correlation coefficient between each relational expression and actual data;

[0028] The well logging data corresponding to the correlation coefficient being greater than a set threshold is regarded as sensitive data, and a plurality of sensitive data are determined;

[0029] An expression for the relationship between the clay porosity discrete data and the sensitive data is established and recorded as the fitting equation.

[0030] As a specific implementation of the embodiment of the present disclosure,

[0031] In a second aspect, the present disclosure also provides a reservoir clay porosity calculation device, comprising:

[0032] Discrete data acquisition module, which obtains discrete data of clay porosity from CMR nuclear magnetic logging;

[0033] A fitting module is configured to determine the well logging data corresponding to the discrete CMR nuclear magnetic well logging clay porosity data, and further determine a fitting equation between the discrete CMR nuclear magnetic well logging clay porosity data and the well logging data;

[0034] The calculation module calculates the clay porosity of the target well according to the fitting relationship and the well logging data.

[0035] Preferably, obtaining discrete clay porosity data from CMR nuclear magnetic logging includes:

[0036] Obtain the total porosity curve and effective porosity curve of CMR nuclear magnetic logging;

[0037] Calculate the clay porosity of CMR nuclear magnetic logging and obtain the clay porosity curve of CMR nuclear magnetic logging;

[0038] The CMR nuclear magnetic logging clay porosity curve is sampled to obtain CMR nuclear magnetic logging clay porosity discrete data.

[0039] Preferably, the clay porosity of CMR nuclear magnetic logging is calculated by formula (1):

[0040] CLAY_CMR=MRP_CMR-FFV_3MS_CMR (1)

[0041] Among them, CLAY_CMR is the clay porosity obtained by CMR nuclear magnetic logging, MRP_CMR is the total porosity obtained by CMR nuclear magnetic logging, and FFV_3MS_CMR is the effective porosity obtained by CMR nuclear magnetic logging.

[0042] Preferably, determining the fitting equation between the clay porosity discrete data and the well logging data comprises:

[0043] Performing a sensitivity analysis on the discrete CMR clay porosity data and the well logging data to obtain a relationship expression between the discrete CMR clay porosity data and each well logging data;

[0044] The fitting equation is established based on the well logging data with a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data.

[0045] Preferably, establishing the fitting equation based on the well logging data having a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data includes:

[0046] Calculate the correlation coefficient between each relational expression and actual data;

[0047] The well logging data corresponding to the correlation coefficient being greater than a set threshold is regarded as sensitive data, and a plurality of sensitive data are determined;

[0048] An expression for the relationship between the clay porosity discrete data and the sensitive data is established and recorded as the fitting equation.

[0049] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0050] a memory storing executable instructions;

[0051] A processor runs the executable instructions in the memory to implement the reservoir clay porosity calculation method.

[0052] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, implements the reservoir clay porosity calculation method.

[0053] Its beneficial effects are:

[0054] By inverting reservoir clay porosity using conventional logging information, reservoir clay porosity can be calculated accurately and effectively. This avoids the limitations of calculating reservoir clay porosity based solely on clay content due to factors such as shale being rich in organic matter and having complex clay mineral types, thereby improving applicability. It also improves the accuracy of clay porosity logging inversion, which can better serve oil and gas exploration and development.

[0055] Oil and natural gas resources are vast, and clay porosity calculations can contribute to assessing shale gas content and identifying proven reserves. As a clean energy source, promoting its widespread use can also contribute to environmental protection. The technology presented in this paper not only aids in clay porosity calculations in oil and gas reservoirs but can also be widely applied, providing a reference for clay porosity calculations in complex lithologic reservoirs.

[0056] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be described in detail in the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0058] Figure 1 A flow chart showing the steps of a method for calculating reservoir clay porosity according to an embodiment of the present invention.

[0059] Figure 2 A schematic diagram of clay porosity calculation results according to an embodiment of the present invention is shown.

[0060] Figure 3 A schematic diagram of clay porosity calculation accuracy analysis according to an embodiment of the present invention is shown.

[0061] Figure 4 A block diagram of a reservoir clay porosity calculation device according to an embodiment of the present invention is shown.

[0062] Description of reference numerals:

[0063] 201. Discrete data acquisition module; 202. Fitting module; 203. Calculation module. DETAILED DESCRIPTION

[0064] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0065] CMR logging has unique advantages in measuring different types of porosity, but because its logging cost is much higher than conventional logging, its application range is relatively small and it is usually used in key wells. The present invention provides a method for calculating reservoir clay porosity, including:

[0066] Obtaining discrete data of clay porosity from CMR nuclear magnetic logging. In one example, obtaining discrete data of clay porosity from CMR nuclear magnetic logging includes: obtaining a total porosity curve and an effective porosity curve from CMR nuclear magnetic logging; calculating clay porosity from CMR nuclear magnetic logging to obtain a clay porosity curve from CMR nuclear magnetic logging; and sampling the clay porosity curve from CMR nuclear magnetic logging to obtain discrete data of clay porosity from CMR nuclear magnetic logging.

[0067] In one example, the clay porosity from CMR logging is calculated using formula (1):

[0068] CLAY_CMR=MRP_CMR-FFV_3MS_CMR (1)

[0069] Among them, CLAY_CMR is the clay porosity obtained by CMR nuclear magnetic logging, MRP_CMR is the total porosity obtained by CMR nuclear magnetic logging, and FFV_3MS_CMR is the effective porosity obtained by CMR nuclear magnetic logging.

[0070] Specifically, CMR logging can independently measure high-precision, high-resolution porosity curves, unaffected by lithology, and extract the total porosity curve and effective porosity curve from CMR logging. The clay porosity of CMR logging is calculated by subtracting the effective porosity curve from the total porosity curve, which is formula (1), and the clay porosity curve of CMR logging is obtained. The clay porosity curve of CMR logging is sampled to obtain discrete clay porosity data. Sampling is performed on the target layer, and curves in different clay porosity intervals are sampled to ensure that the sampled clay porosity is representative.

[0071] Determine the well logging data corresponding to the CMR nuclear magnetic logging clay porosity discrete data, and then determine the fitting equation between the CMR nuclear magnetic logging clay porosity discrete data and the logging data. In one example, determining the fitting equation between the clay porosity discrete data and the logging data includes: performing a sensitivity analysis on the CMR nuclear magnetic logging clay porosity discrete data and the logging data to obtain a relationship expression between the CMR nuclear magnetic logging clay porosity discrete data and each logging data; and establish a fitting equation based on the logging data having a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data.

[0072] In one example, a fitting equation is established based on well logging data with correlations higher than a set threshold and discrete clay porosity data from CMR nuclear magnetic resonance logging. The equation includes: calculating the correlation coefficient between each relational expression and the actual data; defining the well logging data corresponding to correlation coefficients higher than the set threshold as sensitive data, and determining multiple sensitive data; and establishing a relational expression between the discrete clay porosity data and the sensitive data, which is recorded as a fitting equation.

[0073] Specifically, the CMR logging data at depths corresponding to clay porosity include: natural gamma, uranium-free gamma, photoelectric absorption cross-section index, acoustic time difference, compensated neutron, lithologic density, uranium, thorium, potassium, and deep lateral resistivity; and the shale reservoir parameters calculated using it include: total organic carbon content, total gas content, clay content, silica content, and water saturation.

[0074] A sensitivity analysis was performed on the discrete CMR logging clay porosity data and the logging data to obtain the relationship expression between the discrete CMR logging clay porosity data and each logging data. The correlation coefficient between each relationship expression and the actual data was calculated. The logging data corresponding to the correlation coefficient greater than the set threshold was regarded as sensitive data, and multiple sensitive data were determined. The relationship expression between the discrete clay porosity data and the sensitive data was established and recorded as the fitting equation.

[0075] The clay porosity of the target well is calculated based on the fitting relationship and logging data.

[0076] Specifically, the logging data of the target well is substituted into the fitting equation to calculate the clay porosity of the target well.

[0077] The present invention also provides a reservoir clay porosity calculation device, comprising:

[0078] The discrete data acquisition module acquires discrete data of clay porosity from CMR nuclear magnetic logging. In one example, acquiring discrete data of clay porosity from CMR nuclear magnetic logging includes: acquiring a total porosity curve and an effective porosity curve from CMR nuclear magnetic logging; calculating clay porosity from CMR nuclear magnetic logging to acquire a clay porosity curve from CMR nuclear magnetic logging; and sampling the clay porosity curve from CMR nuclear magnetic logging to acquire discrete data of clay porosity from CMR nuclear magnetic logging.

[0079] In one example, the clay porosity from CMR logging is calculated using formula (1):

[0080] CLAY_CMR=MRP_CMR-FFV_3MS_CMR (1)

[0081] Among them, CLAY_CMR is the clay porosity obtained by CMR nuclear magnetic logging, MRP_CMR is the total porosity obtained by CMR nuclear magnetic logging, and FFV_3MS_CMR is the effective porosity obtained by CMR nuclear magnetic logging.

[0082] Specifically, CMR logging can independently measure high-precision, high-resolution porosity curves, unaffected by lithology, and extract the total porosity curve and effective porosity curve from CMR logging. The clay porosity of CMR logging is calculated by subtracting the effective porosity curve from the total porosity curve, which is formula (1), and the clay porosity curve of CMR logging is obtained. The clay porosity curve of CMR logging is sampled to obtain discrete clay porosity data. Sampling is performed on the target layer, and curves in different clay porosity intervals are sampled to ensure that the sampled clay porosity is representative.

[0083] The fitting module determines the well logging data corresponding to the CMR nuclear magnetic logging clay porosity discrete data, and then determines the fitting equation between the CMR nuclear magnetic logging clay porosity discrete data and the logging data. In one example, determining the fitting equation between the clay porosity discrete data and the logging data includes: performing a sensitivity analysis on the CMR nuclear magnetic logging clay porosity discrete data and the logging data to obtain a relationship expression between the CMR nuclear magnetic logging clay porosity discrete data and each logging data; and establishing a fitting equation based on the logging data having a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data.

[0084] In one example, a fitting equation is established based on well logging data with correlations higher than a set threshold and discrete clay porosity data from CMR nuclear magnetic resonance logging. The equation includes: calculating the correlation coefficient between each relational expression and the actual data; defining the well logging data corresponding to correlation coefficients higher than the set threshold as sensitive data, and determining multiple sensitive data; and establishing a relational expression between the discrete clay porosity data and the sensitive data, which is recorded as a fitting equation.

[0085] Specifically, the CMR logging data at depths corresponding to clay porosity include: natural gamma, uranium-free gamma, photoelectric absorption cross-section index, acoustic time difference, compensated neutron, lithologic density, uranium, thorium, potassium, and deep lateral resistivity; and the shale reservoir parameters calculated using it include: total organic carbon content, total gas content, clay content, silica content, and water saturation.

[0086] A sensitivity analysis was performed on the discrete CMR logging clay porosity data and the logging data to obtain the relationship expression between the discrete CMR logging clay porosity data and each logging data. The correlation coefficient between each relationship expression and the actual data was calculated. The logging data corresponding to the correlation coefficient greater than the set threshold was regarded as sensitive data, and multiple sensitive data were determined. The relationship expression between the discrete clay porosity data and the sensitive data was established and recorded as the fitting equation.

[0087] The calculation module calculates the clay porosity of the target well based on the fitting relationship and logging data.

[0088] Specifically, the logging data of the target well is substituted into the fitting equation to calculate the clay porosity of the target well.

[0089] The present invention also provides an electronic device, which includes: a memory storing executable instructions; and a processor running the executable instructions in the memory to implement the above-mentioned reservoir clay porosity calculation method.

[0090] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned reservoir clay porosity calculation method is implemented.

[0091] To facilitate understanding of the solutions and effects of the embodiments of the present invention, four specific application examples are given below. Those skilled in the art should understand that these examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0092] Example 1

[0093] Figure 1 A flow chart showing the steps of a method for calculating reservoir clay porosity according to an embodiment of the present invention.

[0094] like Figure 1 As shown, the reservoir clay porosity calculation method includes: step 101, obtaining CMR nuclear magnetic logging clay porosity discrete data; step 102, determining the logging data corresponding to the CMR nuclear magnetic logging clay porosity discrete data, and then determining the fitting equation between the CMR nuclear magnetic logging clay porosity discrete data and the logging data; step 103, calculating the clay porosity of the target well based on the fitting relationship and the logging data.

[0095] Taking the shale gas well XXX in the Sichuan Basin and its surrounding areas as an example, the clay porosity calculation technology of shale gas reservoirs established based on conventional well logging information is applied to calculate the clay porosity in XXX well.

[0096] Wells with relatively complete and high-quality CMR logging and conventional logging information are selected as modeling wells. The shale gas reservoir section of the modeling wells has relatively complete CMR logging data and conventional logging information.

[0097] CMR logging information includes CMR logging total porosity, CMR logging effective porosity, conventional logging information includes natural gamma (GR), uranium-free gamma (KTH), photoelectric absorption cross section index (P e ), acoustic transit time (AC), compensated neutron (CNL), lithologic density (DEN), uranium (U), thorium (TH), potassium (K), deep lateral resistivity (RD), and shale reservoir parameters calculated using them, such as total organic carbon content (TOC), total gas content (TGAS), clay content (VCLAY), silica content (VSI), water saturation (SW), etc.

[0098] The CMR logging clay porosity is calculated using formula (1) to obtain a CMR logging clay porosity curve. The CMR logging clay porosity curve of the shale gas target layer is sampled to obtain a series of discrete CMR logging clay porosities of the shale gas reservoir section. The conventional logging information values ​​at the corresponding depth points are read using the discrete CMR logging clay porosity data sample points.

[0099] In order to obtain the clay porosity of shale gas reservoirs through conventional logging information, a mathematical expression between the logging data as the variable and the CMR nuclear magnetic resonance logging clay porosity information as the function was established.

[0100] The logging data were analyzed with the clay porosity of CMR nuclear magnetic logging. The conventional logging information response characteristics of the shale gas reservoir section are obvious, usually manifested as an increase in natural gamma (GR), photoelectric absorption cross section index (P e ), decreased uranium-free gamma (KTH), decreased lithologic density (DEN), increased acoustic transit time (AC), decreased compensated neutron (CNL), increased uranium (U), decreased thorium (TH), increased total organic carbon (TOC), decreased clay content (VCLAY), increased silica content (VSI), and decreased water saturation (SW). Therefore, some or all of the conventional logging information with obvious response characteristics to shale gas reservoirs can be selected as variables to establish an expression between conventional logging data and CMR nuclear magnetic logging clay porosity data. Data analysis shows that P e The correlations between KTH, CNL, DEN, U, TOC, SW and CMR nuclear magnetic logging clay porosity (CLAY_CMR) are high, and the correlation coefficients are all above 0.6, indicating that these conventional logging information can better reflect the clay porosity of shale gas reservoirs.

[0101] By using one or more of the above-determined parameters as variables, a mathematical formula is established with the clay porosity of CMR logging, and the mathematical formula with the highest correlation coefficient is extracted, as follows:

[0102] CLAY_POR=f(P e ,CNL,KTH,SW) (2)

[0103] Where CLAY_POR is the clay porosity of shale gas reservoir calculated using conventional logging information, a decimal; f(P e ,CNL,KTH,SW) is the photoelectric absorption cross section index (P e ), compensated neutron (CNL), uranium-free gamma (KTH), and water saturation (SW). The P of each shale mineral e Each parameter is different, indicating variations in mineral types and content. CNL characterizes porosity and hydrogen content, while clay pores primarily contain water. KTH better indicates shale intervals, and SW reflects the water volume of pores. Therefore, based on well logging theory, it is feasible to calculate clay porosity using these four parameters. The functional relationship and the variables in the relationship will vary for different study areas and should be determined based on the actual geological characteristics of the study area.

[0104] Figure 2 A schematic diagram of clay porosity calculation results according to an embodiment of the present invention is shown.

[0105] Figure 2 Tracks 1 through 7 from the left are conventional logging information. Track 9, CLAY_POR, is the clay porosity of the shale gas reservoir calculated using the aforementioned method. CLAY_POR_CMR is the clay porosity from CMR nuclear magnetic resonance logging. These discrete data are represented by bars in the figure. A comparison of the figures shows good consistency between the clay porosity of the shale gas reservoir calculated using this method and that from CMR nuclear magnetic resonance logging.

[0106] Figure 3 A schematic diagram illustrating the accuracy analysis of clay porosity calculations according to one embodiment of the present invention is shown. The conventional well logging clay porosity calculated using this method (the data represented on the abscissa) and the CMR clay porosity (the data represented on the ordinate) show a high correlation, further demonstrating the applicability and high accuracy of the clay porosity calculated using this method.

[0107] Example 2

[0108] Figure 4 A block diagram of a reservoir clay porosity calculation device according to an embodiment of the present invention is shown.

[0109] like Figure 4As shown, the reservoir clay porosity calculation device includes:

[0110] The discrete data acquisition module 201 acquires discrete clay porosity data from CMR nuclear magnetic logging;

[0111] The fitting module 202 determines the well logging data corresponding to the discrete data of the CMR well logging clay porosity, and then determines the fitting equation between the discrete data of the CMR well logging clay porosity and the well logging data;

[0112] The calculation module 203 calculates the clay porosity of the target well based on the fitting relationship and the well logging data.

[0113] As an optional solution, obtaining discrete clay porosity data from CMR logging includes:

[0114] Obtain the total porosity curve and effective porosity curve of CMR nuclear magnetic logging;

[0115] Calculate the clay porosity of CMR nuclear magnetic logging and obtain the clay porosity curve of CMR nuclear magnetic logging;

[0116] The clay porosity curve of CMR nuclear magnetic logging is sampled to obtain discrete data of CMR nuclear magnetic logging clay porosity.

[0117] As an alternative, the clay porosity of CMR nuclear magnetic logging can be calculated using formula (1):

[0118] CLAY_CMR=MRP_CMR-FFV_3MS_CMR (1)

[0119] Among them, CLAY_CMR is the clay porosity obtained by CMR nuclear magnetic logging, MRP_CMR is the total porosity obtained by CMR nuclear magnetic logging, and FFV_3MS_CMR is the effective porosity obtained by CMR nuclear magnetic logging.

[0120] As an optional solution, the fitting equations for determining the clay porosity discrete data and the well logging data include:

[0121] A sensitivity analysis was conducted on the discrete data of CMR nuclear magnetic logging clay porosity and the logging data, and the relationship expression between the discrete data of CMR nuclear magnetic logging clay porosity and each logging data was obtained;

[0122] A fitting equation is established based on the logging data with correlation higher than the set threshold and the discrete clay porosity data of CMR nuclear magnetic logging.

[0123] As an optional solution, based on the well logging data with correlation higher than the set threshold and the CMR nuclear magnetic logging clay porosity discrete data, a fitting equation is established, including:

[0124] Calculate the correlation coefficient between each relational expression and actual data;

[0125] The well logging data corresponding to the correlation coefficient being greater than the set threshold is regarded as sensitive data, and multiple sensitive data are determined;

[0126] An expression for the relationship between clay porosity discrete data and sensitive data is established and recorded as the fitting equation.

[0127] Example 3

[0128] The present disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor running the executable instructions in the memory to implement the above-mentioned reservoir clay porosity calculation method.

[0129] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0130] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0131] The processor may be a central processing unit (CPU) or other form of processing unit having data processing capability and / or instruction execution capability, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to execute the computer-readable instructions stored in the memory.

[0132] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of this disclosure.

[0133] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0134] Example 4

[0135] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for calculating the reservoir clay porosity is implemented.

[0136] According to an embodiment of the present disclosure, a computer-readable storage medium stores non-transitory computer-readable instructions, which, when executed by a processor, execute all or part of the steps of the aforementioned methods of the embodiments of the present disclosure.

[0137] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0138] Those skilled in the art should understand that the above description of the embodiments of the present invention is only for the purpose of illustrative purposes only to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0139] While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for calculating reservoir clay porosity, characterized in that: include: Obtain discrete clay porosity data from CMR nuclear magnetic logging; Determining the well logging data corresponding to the CMR nuclear magnetic well logging clay porosity discrete data, and then determining a fitting equation between the CMR nuclear magnetic well logging clay porosity discrete data and the well logging data; Calculating the clay porosity of the target well according to the fitting equation and the well logging data; Among them, obtaining discrete clay porosity data from CMR nuclear magnetic logging includes: Obtain the total porosity curve and effective porosity curve of CMR nuclear magnetic logging; Calculate the clay porosity of CMR nuclear magnetic logging and obtain the clay porosity curve of CMR nuclear magnetic logging; Sampling the CMR nuclear magnetic logging clay porosity curve to obtain CMR nuclear magnetic logging clay porosity discrete data; Wherein, determining the fitting equation of the clay porosity discrete data and the well logging data includes: Performing a sensitivity analysis on the discrete CMR clay porosity data and the well logging data to obtain a relationship expression between the discrete CMR clay porosity data and each well logging data; Establishing the fitting equation based on the well logging data with a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data; Wherein, establishing the fitting equation based on the logging data having a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data includes: Calculate the correlation coefficient between each relational expression and actual data; The well logging data corresponding to the correlation coefficient being greater than a set threshold is regarded as sensitive data, and a plurality of sensitive data are determined; Establishing a relationship expression between the clay porosity discrete data and the sensitive data, recorded as the fitting equation; The CMR logging data corresponding to the depth of clay porosity include natural gamma, uranium-free gamma, photoelectric absorption cross-section index, acoustic wave time difference, compensated neutron, lithologic density, uranium, thorium, potassium, deep lateral resistivity and shale reservoir parameters calculated using them. Shale reservoir parameters include: total organic carbon content, total gas content, clay content, silica content and water saturation.

2. The reservoir clay porosity calculation method according to claim 1, wherein: The clay porosity of CMR nuclear magnetic logging is calculated by formula (1): CLAY_CMR=MRP_CMR-FFV_3MS_CMR (1) Among them, CLAY_CMR is the clay porosity obtained by CMR nuclear magnetic logging, MRP_CMR is the total porosity obtained by CMR nuclear magnetic logging, and FFV_3MS_CMR is the effective porosity obtained by CMR nuclear magnetic logging.

3. A reservoir clay porosity calculation device, characterized in that: include: Discrete data acquisition module, which obtains discrete data of clay porosity from CMR nuclear magnetic logging; A fitting module is configured to determine the well logging data corresponding to the discrete CMR nuclear magnetic well logging clay porosity data, and further determine a fitting equation between the discrete CMR nuclear magnetic well logging clay porosity data and the well logging data; a calculation module, calculating the clay porosity of the target well according to the fitting equation and the well logging data; Among them, obtaining discrete clay porosity data from CMR nuclear magnetic logging includes: Obtain the total porosity curve and effective porosity curve of CMR nuclear magnetic logging; Calculate the clay porosity of CMR nuclear magnetic logging and obtain the clay porosity curve of CMR nuclear magnetic logging; Sampling the CMR nuclear magnetic logging clay porosity curve to obtain CMR nuclear magnetic logging clay porosity discrete data; Wherein, determining the fitting equation of the clay porosity discrete data and the well logging data includes: Performing a sensitivity analysis on the discrete CMR clay porosity data and the well logging data to obtain a relationship expression between the discrete CMR clay porosity data and each well logging data; Establishing the fitting equation based on the well logging data with a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data; Wherein, establishing the fitting equation based on the logging data having a correlation higher than a set threshold and the CMR nuclear magnetic logging clay porosity discrete data includes: Calculate the correlation coefficient between each relational expression and actual data; The well logging data corresponding to the correlation coefficient being greater than a set threshold is regarded as sensitive data, and a plurality of sensitive data are determined; Establishing a relationship expression between the clay porosity discrete data and the sensitive data, recorded as the fitting equation; The CMR logging data corresponding to the depth of clay porosity include natural gamma, uranium-free gamma, photoelectric absorption cross-section index, acoustic wave time difference, compensated neutron, lithologic density, uranium, thorium, potassium, deep lateral resistivity and shale reservoir parameters calculated using them. Shale reservoir parameters include: total organic carbon content, total gas content, clay content, silica content and water saturation.

4. The reservoir clay porosity calculation device according to claim 3, wherein: The clay porosity of CMR nuclear magnetic logging is calculated by formula (1): CLAY_CMR=MRP_CMR-FFV_3MS_CMR (1) Among them, CLAY_CMR is the clay porosity obtained by CMR nuclear magnetic logging, MRP_CMR is the total porosity obtained by CMR nuclear magnetic logging, and FFV_3MS_CMR is the effective porosity obtained by CMR nuclear magnetic logging.

5. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the reservoir clay porosity calculation method according to claim 1 or 2.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for calculating the reservoir clay porosity according to claim 1 or 2 is implemented.

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