Optimal porosity calculation method, system and equipment with M-N value of mudstone skeleton as constraint and medium
By using a porosity calculation method constrained by the MN value of the mudstone skeleton, and employing kerogen correction and cross-plot method, the problem of low porosity calculation accuracy in shale formations has been solved, achieving higher accuracy porosity evaluation and promoting efficiency and cost reduction in oilfield exploration and development.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-10-11
- Publication Date
- 2026-04-14
AI Technical Summary
Existing porosity calculation methods in shale formations suffer from low accuracy due to strong heterogeneity and large variations in framework parameters, which affects the accuracy of oil reserve assessment.
By constraining the MN value of the mudstone skeleton, and using conventional well logging information for kerogen correction and cross-plot method, a set of response equations is constructed to obtain the optimal porosity, overcoming the difficulty of selecting rock skeleton parameters and improving calculation accuracy.
It improves the accuracy of porosity calculation, reduces data acquisition costs, enhances oilfield exploration and development efficiency, and reduces reliance on nuclear magnetic resonance logging.
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of well logging technology, and specifically relates to an optimal porosity calculation method, system, equipment and medium constrained by the MN value of the mudstone skeleton. Background Technology
[0002] Reservoir porosity calculation is crucial for evaluating oil and gas reserves. Commonly used methods include fitting analysis, volumetric modeling, and cross-plotting. With increasingly complex exploration targets, the accuracy of porosity calculations for complex lithologies such as carbonates, volcanic rocks, and shale is generally low. Recognizing the significant impact of lithological heterogeneity on parameter calculations, experts have attempted to obtain continuous and more accurate lithological profiles through quantitative calculation of mineral components to achieve porosity calculations using variable framework parameters. Optimization methods are one such approach, with their application in well logging dating back to the 1980s. Schlumberger's introduction of the GLOBAL optimization interpretation method improved the utilization of well logging data, and commercial software modules such as ELAN and ELANPLUS, developed based on this method, have achieved good application results. During the same period, Atlas Copco launched the OPTIMA optimization logging interpretation program. In 1992, Zhong Xingshui and Gao Chuqiao developed a formation component analysis program, which was a relatively mature optimization logging interpretation method in China at the time. Zhang Zhaohui et al. (2012) combined conventional logging and elemental capture logging data, using the theoretical value method and cross-plot method to determine the theoretical logging response values of formation components, and obtained good application results in volcanic rock formations by calculating the optimal porosity point by point. However, due to the coexistence of source and reservoir in shale formations, the clay components such as montmorillonite, illite, and kaolinite vary rapidly and significantly. Inaccurate selection of framework parameters leads to low porosity accuracy calculated by conventional methods, which seriously restricts the submission of oil reserves and the evaluation of geological sweet spots. Summary of the Invention
[0003] The purpose of this invention is to provide an optimal porosity calculation method, system, equipment, and medium constrained by the MN value of the mudstone skeleton, to address the shortcomings of commonly used porosity calculation methods due to the strong heterogeneity of shale formations, large variations in skeleton parameters, and low accuracy. This application leverages conventional well logging information to improve porosity calculation accuracy, which is beneficial for the refined evaluation of geological reserves, reduces data acquisition costs, and improves the efficiency of oilfield exploration and development.
[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This invention provides a method for calculating optimal porosity constrained by the MN value of a mudstone skeleton, comprising the following steps: Step 1: Perform kerogen correction on the logging curve corresponding to any sampling point in the study area to obtain the corrected three-porosity curve; Step 2: Obtain the mudstone skeleton MN value based on the corrected three-porosity curve, and calculate the mud content of the strata in the study area based on the mudstone skeleton MN value. Step 3: Construct a set of response equations containing the mud content parameters of the strata in the study area, calculate the optimal porosity of the strata in the study area using the obtained set of response equations, and use the optimal porosity of the strata to accurately evaluate the geological reserves.
[0005] Preferably, in step 1, the well logging curve corresponding to any sampling point in the study area is corrected using kerogen to obtain the corrected three-porosity curve. The specific method is as follows: Establish an organic carbon content calculation model corresponding to the study area, and calculate the organic carbon content of any sampling point in the study area based on the obtained organic carbon content calculation model. Based on empirical formulas and the obtained organic carbon content, the volume of kerogen corresponding to any sampling point is calculated. Based on the rock volume model corresponding to the study area, the well logging curve is corrected by kerogen correction using the kerogen volume of any sampling point, resulting in the corrected three-porosity curve corresponding to any sampling point.
[0006] Preferably, the expression for the obtained organic carbon content calculation model is as follows:
[0007] In the formula: GR is natural gamma; RT is formation resistivity; AC is acoustic transit time.
[0008] Preferably, in step 2, the mudstone skeleton MN value is calculated based on the obtained corrected three-porosity curve, and the mudstone content of the formation in the study area is obtained based on the mudstone skeleton MN value. The specific method is as follows: Construct a cross plot based on the obtained corrected three-porosity curves; Draw a straight line between the sandstone point in the intersection diagram and any sampling point in the intersection diagram. The intersection of the straight line and the mudstone line in the intersection diagram is the mudstone skeleton point. Calculate the MN value of the mudstone skeleton point; The mudstone content of any sampling point is calculated based on the obtained mudstone skeleton point MN value and the coordinates of any sampling point.
[0009] Preferably, in step 3, the constructed response equation set containing the formation clay content parameters of the study area is as follows:
[0010] in, Porosity; This represents the acoustic skeleton value for sandstone. This represents the acoustic skeleton value of mudstone. TOC acoustic skeleton value; For fluid acoustic wave transit time; This represents the density framework value of sandstone. This represents the density framework value of mudstone. For fluid density; TOC density skeleton value; This represents the neutron framework value for sandstone. This represents the neutron skeleton value in mudstone. It is a fluid neutron.
[0011] Preferably, the optimal porosity of the formation in the study area is calculated using the obtained response equations. Specifically, the method is as follows: The optimal porosity of the strata in the study area was determined by combining the least squares method with the response equation, and the optimization function was obtained. The optimal porosity of the formation in the study area is obtained by differentiating the optimal function.
[0012] An optimal porosity calculation system constrained by the MN value of a mudstone skeleton includes: The three-porosity correction unit is used to perform kerogen correction on the logging curve corresponding to any sampling point in the study area to obtain the corrected three-porosity curve. The formation mud content calculation unit is used to obtain the mudstone skeleton MN value based on the obtained corrected three-porosity curve, and to calculate the formation mud content of the study area based on the mudstone skeleton MN value. The optimal porosity calculation unit is used to construct a set of response equations containing the formation clay content parameters of the study area, and to calculate the optimal porosity of the formation in the study area using the obtained set of response equations.
[0013] A computer device, comprising: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, performs the method.
[0014] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method.
[0015] A computer program product comprising a computer program that, when executed by a processor, implements the method.
[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention provides an optimal porosity calculation method constrained by the MN value of the mudstone skeleton. Shale formations are highly heterogeneous with large variations in skeleton parameters, making it difficult to select rock skeleton parameters in commonly used porosity calculation methods, resulting in low calculation accuracy. This application uses conventional logging curves as a basis and constrains the formation by using the MN value of the mudstone skeleton, transforming the underdetermined equation into an overdetermined equation. The formation porosity is obtained by finding the optimal solution, overcoming the difficulty in selecting rock skeleton parameters caused by the rapid and large vertical variations and differences in mudstone components. At the same time, the MN value of the mudstone skeleton is obtained by obtaining the kerogen volume, and the kerogen is used to correct the logging curve to eliminate the influence of kerogen on the logging curve, thereby improving the calculation accuracy of porosity. Attached Figure Description
[0017] Figure 1 This is a flowchart of the invention; Figure 2 This is a schematic diagram of the M and N values of the mudstone skeleton obtained from the cross-plot method in the target work area. Figure 3 This is a schematic diagram illustrating the calculated optimal porosity of the target work area. Detailed Implementation
[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.
[0019] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.
[0020] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.
[0021] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."
[0022] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0023] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.
[0024] Example 1 This embodiment uses conventional well logging data as a basis, constrains the formation by using the MN value of the mudstone skeleton, transforms the underdetermined equation into an overdetermined equation, and obtains the formation porosity by finding the optimal solution.
[0025] Specifically, this embodiment provides an optimal porosity calculation method constrained by the MN value of the mudstone skeleton, which includes the following steps: S1. Establish an organic carbon content calculation model for the study area. Calculate the organic carbon content at any sampling point based on the obtained organic carbon content calculation model. Calculate the kerogen volume V corresponding to any sampling point based on the obtained organic carbon content. toc ; Based on the rock volume model corresponding to the study area, the kerogen volume of any sampling point is combined with the kerogen correction of the corresponding well logging curve to obtain the corrected three-porosity curve corresponding to any sampling point. S2, obtain the M and N values of the mudstone skeleton corresponding to any sampling point using the cross-plot method, denoted as M. sh N sh And calculate the formation clay content corresponding to any sampling point, denoted as V. sh ; S3. Construct the optimization equation, find the optimal solution that satisfies the conditions point by point, and realize the calculation of the optimal porosity of the formation corresponding to any sampling point.
[0026] Example 2 Based on Example 1, this example provides an optimal porosity calculation method constrained by the MN value of the mudstone skeleton. In S1, an organic carbon content calculation model for the study area is established by fitting, denoted as TOC, and its mathematical expression is:
[0027] Where: GR is natural gamma, API; RT is formation resistivity, ohm-meter; AC is acoustic transit time, microseconds / meter; This is a function for calculating organic carbon content.
[0028] Calculate the organic carbon content at any sampling point based on the obtained organic carbon content calculation model; The volume V of kerogen at any given sampling point is calculated using empirical formulas combined with organic carbon content. TOC The calculation formula is as follows:
[0029] In the formula: denoted as kerogen density; k is the kerogen conversion factor, typically taken as 1.2.
[0030] Kerogen correction is performed on the corresponding well logging curve by combining the rock volume model with the kerogen volume of any sampling point, wherein the rock volume model V = Vma (sandstone skeleton volume) + Vsh (mudstone volume) + φ (pore volume) + Vtoc (kerogen volume). The logging curves refer to: DEN (density), AC (acoustic), and CNL (neutron) curves; logging curves are data obtained by instruments. Finally, the corrected porosity curve AC was obtained. xz DEN xz CNL xz The specific correction formula is as follows:
[0031]
[0032]
[0033] In the formula: For acoustic wave transit time; DEN is density; CNL is compensating neutron; TOC DEN is the time difference of acoustic waves from kerogen. TOC CNL is the density of kerogen.TOC Neutron compensation for kerogen; XZ DEN XZ CNL XZ These are the acoustic transit time curve, density curve, and compensated neutron curve after kerogen correction.
[0034] Example 3 Based on Example 1, this example provides an optimal porosity calculation method constrained by the MN value of the mudstone skeleton. In S2, a stratigraphic MN cross-plot is created using the kerogen-corrected three-porosity curve. The mudstone line equations for the montmorillonite, illite, and kaolinite points in the cross-plot are expressed as follows:
[0035] Calculate the M and N values corresponding to any sampling point S in the intersection graph, denoted as M. S N S Its mathematical expression is:
[0036]
[0037] In the formula: The fluid acoustic wave transit time is typically taken as 620. The fluid density is typically taken as 1. This is the neutron value in the fluid, typically taken as 1; Draw a straight line g(M, N) from the sandstone point (0.796, 0.624) and any sampling point S in the cross-plot. This line intersects the mudstone line, and the intersection point is the mudstone skeleton point. The coordinates of the mudstone skeleton point are (M... SH N SH ); Calculate the mudstone content V of the formation corresponding to any sampling point based on the obtained mudstone skeleton point coordinates. sh Its mathematical expression is:
[0038] Example 4 Based on Example 1, this example provides an optimal porosity calculation method constrained by the MN value of the mudstone skeleton. In S3, a set of response equations corresponding to the acoustic transit time, density, compensated neutrons, and mudstone skeleton point coordinates are established:
[0039] in, Porosity; This represents the acoustic skeleton value for sandstone. This represents the acoustic skeleton value of mudstone. TOC acoustic skeleton value; For fluid acoustic wave transit time; This represents the density framework value of sandstone. This represents the density framework value of mudstone. For fluid density; TOC density skeleton value; This represents the neutron framework value for sandstone. This represents the neutron skeleton value in mudstone. It is a fluid neutron.
[0040] The optimal porosity of the formation at any sampling point is determined by combining the least squares method with the response equation, resulting in the optimization function F, the specific expression of which is:
[0041] In the formula, For AC, DEN, CNL; Here are the weighting coefficients for AC, DEN, and CNL in the response equation. The study area uses the following coefficients in sequence: , , ; These are the corresponding rock volume model components; These are the logging response values corresponding to the sandstone skeleton, mudstone skeleton, and fluid components; Taking the derivative of the optimal function F and setting it to zero, its mathematical expression is:
[0042] The porosity at any sampling point is the optimal porosity of the formation obtained by making the derivative equation approach 0 and obtaining the global optimal solution.
[0043] This embodiment proposes an optimal porosity calculation method based on conventional well logging curves, constrained by the MN value of the mudstone skeleton. This method eliminates the influence of kerogen on the well logging curves, obtains the MN value of the mudstone skeleton through cross-plotting, and converts the underdetermined equation into an overdetermined equation. The formation porosity is obtained by solving the optimal solution. The calculation process and approach have been changed compared to previous studies, specifically in the following aspects: For strata with high clay content and complex clay composition, the framework parameters vary greatly, and the three response equations no longer satisfy the four variables ( , , , To obtain the required values for the mudstone skeleton M and N, this application uses the cross-plot method to determine the values of the mudstone skeleton M and N. Using the values of the mudstone skeleton M and N, the three response equations are upgraded to five equations (AC, DEN, CNL, M). SH N SHThe response equation is then used to determine the optimal porosity.
[0044] In summary, this embodiment further explores the potential of conventional logging, overcomes the difficulty in selecting rock skeleton parameters due to the rapid and large differences in the vertical variation of mud components, and proposes a more accurate shale oil parameter evaluation method. To a certain extent, it reduces the dependence of shale oil evaluation on nuclear magnetic resonance logging and promotes cost reduction and efficiency improvement in oil fields.
[0045] Example 5 This example uses the XX oilfield as a case study, evaluating shale oil. The formations in this area are highly heterogeneous, with rapid and significant variations in the composition of minerals such as organic matter, montmorillonite, illite, and kaolinite. Initially, fixed rock framework parameters were used to calculate porosity, resulting in low accuracy and an inability to provide continuous porosity profiles. Later, nuclear magnetic resonance porosity was used to characterize formation reservoirs. However, this approach suffers from signal distortion in some layers due to factors such as pyrite, and also increases well logging costs, making it unsuitable for large-scale application and severely hindering the submission of oil reserves and the evaluation of geological sweet spots.
[0046] This embodiment uses conventional well logging data as a basis and constrains the formation by applying the MN value of the mudstone skeleton, transforming the underdetermined equations into overdetermined equations. The formation porosity is then obtained by finding the optimal solution. This method allows for rapid industrial application, improving the accuracy of parameter calculations and supporting oilfield exploration and development.
[0047] like Figure 1 As shown in the accompanying drawings and specific embodiments, this embodiment will be further described in detail below.
[0048] S1, Establish a calculation model for the organic carbon content of the study area, and convert it into kerogen volume V. toc Kerogen correction was performed on the logging curves based on the volumetric model. right The method is extended technically by selecting appropriate calibration parameters and replacing the fixed acoustic and resistivity baselines with acoustic-electric area, thus eliminating the influence of lithology and porosity on logging response. This is combined with natural gamma curve fitting to establish organic carbon content... The calculation formula, its mathematical expression is:
[0049] in:
[0050] In the formula: GR is natural gamma, API; It is the formation resistivity, in ohm-meters; AC is the acoustic transit time, in microseconds per meter. Calculate the organic carbon content at any sampling point based on the obtained organic carbon content calculation model; The volume V of kerogen at any given sampling point is calculated using empirical formulas combined with organic carbon content. TOC Its calculation formula is:
[0051] In the formula: denoted as kerogen density; K is the kerogen conversion factor, typically taken as 1.2.
[0052] Kerogen correction is performed on the logging curves using a rock volume model. The correction formula is as follows:
[0053]
[0054]
[0055] In the formula: For acoustic wave transit time; DEN is density; CNL is compensating neutron; TOC DEN is the time difference of acoustic waves from kerogen. TOC CNL is the density of kerogen. TOC Neutron compensation for kerogen; XZ DEN XZ CNL XZ These are the corrected acoustic time difference, density, and compensated neutrons, respectively. S2, the M and N values of the mudstone skeleton are obtained through the cross-plot method, denoted as M. sh N sh Calculate the formation clay content, denoted as V. sh ; like Figure 2 As shown, using the kerogen-corrected three-porosity curves to create the stratigraphic M-N intersection diagram, the mudstone line equation passing through montmorillonite, illite, and kaolinite points can be expressed as:
[0056] Calculate the M and N values corresponding to any sampling point S in the intersection graph, denoted as M. S N S Its mathematical expression is:
[0057]
[0058] In the formula: The fluid acoustic wave transit time is typically taken as 620. The fluid density is typically taken as 1. This is the neutron value in the fluid, typically taken as 1; Draw a straight line g(M, N) from the sandstone point (0.796, 0.624) and any sampling point S. The intersection of this line and the mudstone line is the mudstone skeleton point, and its corresponding coordinates are (M... SH N SH ); Calculate the formation clay content V sh Its mathematical expression is:
[0059] S3. Construct the optimization equation, find the optimal solution that satisfies the conditions point by point, and realize the calculation of the optimal porosity of the formation.
[0060] Establish the response equations corresponding to the acoustic transit time, density, compensated neutrons, and mudstone skeleton point coordinates at the sampling points:
[0061] In the formula, where, Porosity; This represents the acoustic skeleton value for sandstone. This represents the acoustic skeleton value of mudstone. TOC acoustic skeleton value; For fluid acoustic wave transit time; This represents the density framework value of sandstone. This represents the density framework value of mudstone. For fluid density; TOC density skeleton value; This represents the neutron framework value for sandstone. This represents the neutron skeleton value in mudstone. It is a fluid neutron.
[0062] The optimal porosity of a formation is determined using the least squares method, and the specific expression for the optimization function F is as follows:
[0063] In the formula, For AC, DEN, CNL; Here are the weighting coefficients for AC, DEN, and CNL in the response equation. The study area uses the following coefficients in sequence: , , ; These are the corresponding rock volume model components; These are the logging response values corresponding to the sandstone skeleton, mudstone skeleton, and fluid components; Taking the derivative of the optimization function F and setting it to zero, its mathematical expression is:
[0064] The porosity at which the global optimal solution is obtained is the optimal porosity of the formation corresponding to the sampling point.
[0065] The optimal porosity of the formation is calculated point by point according to the aforementioned method.
[0066] The example oilfield is a domestic shale oil reservoir, and its parameters are evaluated using an optimal porosity calculation method constrained by the MN value of the mudstone skeleton.
[0067] Figure 3 The figure shows a typical example of optimal porosity application. From left to right, the graph corresponds to: 1st channel: depth; 2nd channel: resistivity; 3rd channel: porosity curve; 4th channel: lithology curve; 5th channel: T2 spectrum; 6th channel: comparison of porosity calculated by the original model and core analysis; 7th channel: comparison of optimal porosity and core analysis; and 8th channel: lithological profile. The comparison in channel 7 shows that the optimal porosity calculated in this embodiment matches the core analysis well, and the accuracy is higher than the original evaluation method shown in channel 6.
[0068] Example 6 This embodiment provides an optimal porosity calculation system constrained by the MN value of the mudstone skeleton, comprising: The three-porosity correction unit is used to perform kerogen correction on the logging curve corresponding to any sampling point in the study area to obtain the corrected three-porosity curve. The formation mud content calculation unit is used to obtain the mudstone skeleton MN value based on the obtained corrected three-porosity curve, and to calculate the formation mud content of the study area based on the mudstone skeleton MN value. The optimal porosity calculation unit is used to construct a set of response equations containing the formation clay content parameters of the study area, and to calculate the optimal porosity of the formation in the study area using the obtained set of response equations.
[0069] Example 7 This embodiment 7 provides a computer device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of a computer method.
[0070] When the processor executes the computer program, it implements the steps of the above-described computer method.
[0071] Alternatively, the processor may execute the computer program to implement the functions of each module in the aforementioned system. The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above are examples of computer devices and do not constitute a limitation on the computer device; it may include more components than described above, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.
[0072] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor, or any conventional processor, etc. The processor is the control center of the computer device, connecting various parts of the computer device through various interfaces and lines.
[0073] The memory can be used to store the computer program and / or module, and the processor implements various functions of the computer device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.
[0074] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.
[0075] Example 8 This embodiment 8 also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described.
[0076] If the modules / units integrated in the computer system are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
[0077] Based on this understanding, all or part of the processes in the above-described method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-described computer method. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.
[0078] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.
[0079] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.
[0080] Example 9 This embodiment 9 provides a computer product, which includes a computer program stored in a computer-readable storage medium. The processor of the computer device reads the computer program from the computer-readable storage medium and executes the computer program, so that the computer device can perform the method in embodiment 1, which will not be described again here.
[0081] It should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods.
[0082] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for calculating optimal porosity constrained by the MN value of a mudstone skeleton, characterized in that, Includes the following steps: The corrected three-porosity curve is obtained by performing kerogen correction on the logging curve corresponding to any sampling point in the study area. The mudstone skeleton MN value is obtained from the corrected three-porosity curve, and the mudstone content of the strata in the study area is calculated based on the mudstone skeleton MN value. A set of response equations containing the clay content of the strata in the study area was constructed. The optimal porosity of the strata in the study area was calculated using the obtained set of response equations. The optimal porosity of the strata was then used to accurately evaluate the geological reserves.
2. The optimal porosity calculation method based on the MN value of the mudstone skeleton as described in claim 1, characterized in that, In step 1, the well logging curve corresponding to any sampling point in the study area is corrected using kerogen to obtain the corrected three-porosity curve. The specific method is as follows: Establish an organic carbon content calculation model corresponding to the study area, and calculate the organic carbon content of any sampling point in the study area based on the obtained organic carbon content calculation model. Based on empirical formulas and the obtained organic carbon content, the volume of kerogen corresponding to any sampling point is calculated. Based on the rock volume model corresponding to the study area, the well logging curve is corrected by kerogen correction using the kerogen volume of any sampling point, resulting in the corrected three-porosity curve corresponding to any sampling point.
3. The optimal porosity calculation method based on the MN value of the mudstone skeleton as described in claim 2, characterized in that, The expression for the established organic carbon content calculation model is as follows: In the formula: GR is natural gamma; RT is formation resistivity; AC is acoustic transit time.
4. The optimal porosity calculation method based on the MN value of the mudstone skeleton as described in claim 1, characterized in that, In step 2, the MN value of the mudstone skeleton is calculated based on the obtained corrected three-porosity curve. The mudstone content of the formation in the study area is then determined based on the MN value of the mudstone skeleton. The specific method is as follows: Construct a cross plot based on the obtained corrected three-porosity curves; Draw a straight line between the sandstone point in the intersection diagram and any sampling point in the intersection diagram. The intersection of the straight line and the mudstone line in the intersection diagram is the mudstone skeleton point. Calculate the MN value of the mudstone skeleton point; The mudstone content of any sampling point is calculated based on the obtained mudstone skeleton point MN value and the coordinates of any sampling point.
5. The optimal porosity calculation method based on the MN value of the mudstone skeleton as described in claim 1, characterized in that, In step 3, the constructed response equation set containing the formation clay content parameters of the study area is as follows: in, Porosity; This represents the acoustic skeleton value for sandstone. This represents the acoustic skeleton value of mudstone. TOC acoustic skeleton value; For fluid acoustic wave transit time; This represents the density framework value of sandstone. This represents the density framework value of mudstone. For fluid density; TOC density skeleton value; This represents the neutron framework value for sandstone. This represents the neutron skeleton value in mudstone. It is a fluid neutron.
6. The optimal porosity calculation method based on the MN value of the mudstone skeleton as described in claim 1, characterized in that, The optimal porosity of the formation in the study area was calculated using the obtained response equations. The specific method is as follows: The optimal porosity of the strata in the study area was determined by combining the least squares method with the response equation, and the optimization function was obtained. The optimal porosity of the formation in the study area is obtained by differentiating the optimal function.
7. An optimal porosity calculation system constrained by the MN value of a mudstone skeleton, characterized in that, include: The three-porosity correction unit is used to perform kerogen correction on the logging curve corresponding to any sampling point in the study area to obtain the corrected three-porosity curve. The formation mud content calculation unit is used to obtain the mudstone skeleton MN value based on the obtained corrected three-porosity curve, and to calculate the formation mud content of the study area based on the mudstone skeleton MN value. The optimal porosity calculation unit is used to construct a set of response equations containing the formation clay content parameters of the study area, and to calculate the optimal porosity of the formation in the study area using the obtained set of response equations.
8. A computer device, characterized in that, include: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, performs the method as described in any one of claims 1-6.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.
10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.