Volcanic rock lithology identification method, system and device based on extreme value statistics and medium

Abstract

The application discloses a volcanic rock lithology identification method, system, device and medium based on extreme value statistics, and comprises the following steps: selecting volcanic rocks with different lithology and facies as core samples, and determining the lithology types of the selected core samples; based on the sensitive logging curves of the selected core samples, obtaining the theoretical skeleton points of the sensitive logging curves of different types of volcanic lava, the distribution intervals of the sensitive logging curves and the extreme values of the broken points; based on the clay rock logging skeleton values of the regional volcanic rock sensitive logging curves, obtaining the volcanic lava broken trend, determining the extreme values of the sensitive logging curve broken boundary argillization points and the argillization boundary of different types of volcanic clastic rocks, drawing a sensitive curve crossplot, and obtaining the quantitative division interval of different types of volcanic lava and volcanic clastic rocks. The application establishes a volcanic rock lithology identification chart by the extreme values of the broken points, the broken trend, the extreme values of the argillization points and the argillization boundary, and has higher lithology identification precision and reliable theoretical basis. The application is simple in calculation and high in universality.

Description

Technical Field

[0001] This invention belongs to the field of formation logging technology for volcanic rock oil and gas reservoirs, and relates to a method, system, device and medium for identifying volcanic rock lithology based on extreme value statistics. Background Technology

[0002] In volcanic rock oil and gas exploration, reservoir lithology is closely related to parameters such as reservoir porosity, permeability, fracture development, alteration and fragmentation, and hydrocarbon saturation. Therefore, the quantitative classification of lithology is particularly important in volcanic rock well logging evaluation.

[0003] As oil and gas exploration continues to delve deeper and into ultra-deep formations, complex strata such as volcanic rocks are gradually becoming a focus of exploration. Compared with conventional clastic oil and gas reservoirs, volcanic rock reservoirs are characterized by multiple volcanic eruption periods, diverse lithofacies types, complex mineral content, varying degrees of weathering and fragmentation, and complex lithology. Currently, volcanic rock lithology identification mainly employs methods such as electrical imaging logging to delineate volcanic rock structure and elemental logging to delineate volcanic rock composition, as well as combining various conventional logging cross-plots to identify different types of volcanic rock lithology. Typically, multiple logging methods are used comprehensively to identify volcanic rock lithology, and this approach has achieved certain results in volcanic rock lithology evaluation. However, electrical imaging data and elemental logging data lack universality and require the establishment of large image databases. The method of combining multiple conventional logging cross-plots requires the establishment of numerous lithology identification charts for interactive use, lacking convenience, applicability, and scalability. Therefore, it is necessary to establish a universal volcanic rock lithology identification chart based on theoretical analysis. Summary of the Invention

[0004] The purpose of this invention is to address the problems in existing technologies where electrical imaging data and elemental logging data lack universality and require the establishment of large image libraries, and where the combination of various conventional logging cross-plots requires the establishment of a large number of lithology identification charts for interactive use, resulting in a lack of convenience, applicability, and scalability. This invention provides a method, system, device, and medium for identifying volcanic rock lithology based on extreme value statistics.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] Methods for identifying volcanic rock lithology based on extreme value statistics include:

[0007] Volcanic rocks with different lithologies and lithofacies were selected as core samples to determine the lithology of the selected core samples.

[0008] Log logging curves of core samples with different lithologies were obtained and response characteristic analysis was performed to determine the sensitive log logging curves of the selected core samples.

[0009] Based on sensitive logging curves, the theoretical framework points, distribution ranges, and fracture point extremes of sensitive logging curves for different types of volcanic lava are obtained through extreme value statistics theory and regional rock property models.

[0010] Based on the clay rock logging skeleton value of the sensitive logging curve of regional volcanic rocks, the fracturing trend of volcanic lava is obtained, and the extreme value of the mudification point and mudification boundary of the sensitive logging curve of different types of volcanic clastic rocks are determined.

[0011] Based on the theoretical framework points, distribution ranges, extreme values ​​of fracture points, and extreme values ​​of mudstone formation points and mudstone formation boundaries of sensitive logging curves for different types of volcanic lava, cross plots of sensitive logging curves are drawn to obtain quantitative division ranges for different types of volcanic lava and volcanic clastic rocks.

[0012] A further improvement of the present invention is that:

[0013] The lithology of the core samples was obtained through the thin section identification test method; the thin section identification test was conducted based on the industry standard "Thin Section Identification (SY / T5368-2016)".

[0014] The core samples included volcanic lava and pyroclastic rocks; the volcanic lava included basalt, andesite, dacite, and rhyolite; the pyroclastic rocks included basaltic volcanic breccia, andesitic volcanic breccia, dacite volcanic breccia, and rhyolitic volcanic breccia.

[0015] The core samples are one or more of the following: basalt, andesite, dacite, rhyolite, basaltic volcanic breccia, andesitic volcanic breccia, dacite volcanic breccia, and rhyolitic volcanic breccia.

[0016] Well logging curves of core samples with different lithologies were obtained and response characteristic analysis was performed to determine the sensitive well logging curves of the selected core samples; specifically:

[0017] As the lithology of volcanic lava changes from basic to acidic, the natural gamma logging value gradually increases while the density logging value gradually decreases. The natural gamma and density of volcanic lava are identified as the sensitive logging curves.

[0018] As the lithology of volcanic clastic rocks changes from basic to acidic, the natural gamma logging value gradually increases while the density logging value gradually decreases. The natural gamma and density logging curves of volcanic clastic rocks were determined to be the most sensitive logging curves.

[0019] Based on sensitive logging curves, and through extreme value statistics theory and regional rock property models, the theoretical framework points, distribution ranges, and fracture point extremes of sensitive logging curves for different types of volcanic lava are obtained. Specifically:

[0020] Based on the regional rock property model, the maximum density and minimum natural gamma of basic volcanic lava are obtained, i.e., the theoretical skeleton point a0(x0, y0) of basic volcanic lava. Based on the extreme value statistics theory, the minimum density and maximum natural gamma of intermediate-basic, intermediate-acidic, and acidic volcanic lava are obtained, and the extreme values ​​of the fracture points of various types of volcanic lava are determined, i.e., the natural gamma-density intersection points a1(x1, y1), a2(x2, y2), a3(x3, y3), a4(x4, y4), and the maximum natural gamma points b1(x1, 0), b2(x2, 0), b3(x3, 0), b4(x4, 0) of various types of volcanic lava.

[0021] Intermediate-basic volcanic lava is basalt; intermediate volcanic lava is andesite; intermediate-acidic volcanic lava is dacite; and acidic volcanic lava is rhyolite.

[0022] Based on the clay rock logging framework values ​​of the sensitive logging curves of regional volcanic rocks, the fracturing trend of volcanic lava is obtained, and the extreme values ​​of the mudification point and mudification boundary of the sensitive logging curves of different types of volcanic clastic rocks are determined; specifically:

[0023] Using a regional rock property model, the well logging skeleton values ​​of basic weathered claystone, namely the basic volcanic claystone skeleton point c1(x5, y5), are obtained. Based on the skeleton points of basic volcanic claystone and the extreme values ​​of basic volcanic lava fracturing points, the fracturing trend F of basic volcanic lava is constructed.

[0024]

[0025] Based on the regional rock property model, the logging skeleton value of acid weathered claystone is obtained. Combined with the fracturing trend, the extreme value of the acid mudstone point of complete clayification is obtained, c4(x8, y8).

[0026] The mudstone boundary is determined by connecting the skeleton point c1(x5, y5) of basic volcanic claystone and the extreme value c4(x8, y8) of acidic mudstone. The fracture boundaries of intermediate volcanic clastic rocks and intermediate-acidic volcanic clastic rocks are obtained based on the fracture trend F of basic volcanic lava and the extreme values ​​of fracture points of intermediate volcanic lava and intermediate-acidic volcanic lava. Then, the intersection points of the fracture boundaries of intermediate volcanic clastic rocks, intermediate-acidic volcanic clastic rocks and acidic volcanic clastic rocks with the mudstone boundary are obtained; that is, the extreme values ​​of each mudstone point c2(x6, y6), c3(x7, y7), and c4(x8, y8).

[0027] The intermediate volcanic breccia is andesitic volcanic breccia; the intermediate-acidic volcanic breccia is dacite volcanic breccia; and the acidic volcanic breccia is rhyolitic volcanic breccia.

[0028] A volcanic rock lithology identification system based on extreme value statistics includes:

[0029] The first determining module is used to select volcanic rocks with different lithologies and lithofacies as core samples and determine the lithology type of the selected core samples.

[0030] The response analysis module is used to acquire logging curves of core samples with different lithologies and perform response characteristic analysis to determine the sensitive logging curves of the selected core samples.

[0031] The acquisition module, based on sensitive logging curves, uses extreme value statistical theory and regional rock property models to acquire the theoretical skeleton points, distribution ranges, and fracture point extremes of sensitive logging curves for different types of volcanic lava.

[0032] The second determining module, based on the clay rock logging skeleton value of the regional volcanic rock sensitive logging curve, obtains the volcanic lava fracturing trend and determines the extreme value of the mudification point and mudification boundary of the sensitive logging curve of different types of volcanic clastic rocks.

[0033] The plotting module, based on the theoretical skeleton points of sensitive logging curves for different types of volcanic lava, the distribution range of sensitive logging curves, the extreme values ​​of fracture points, and the extreme values ​​of mudification points and mudification boundaries of sensitive logging curves for different types of volcanic clastic rocks, plots sensitive curve intersection diagrams to obtain quantitative division ranges for different types of volcanic lava and volcanic clastic rocks.

[0034] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0035] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] This invention selects volcanic rock cores with different lithologies and lithofacies, classifies and screens them as core samples, and then conducts thin-section identification experiments on the core samples to determine their specific lithology. The response characteristics of the experimental data of core samples with different lithologies on conventional logging curves are analyzed to determine sensitive logging curves reflecting lithology. Using the obtained sensitive logging curves, through extreme value statistics theory and regional rock property models, the theoretical framework points, distribution ranges, and extreme values ​​of breakage points of sensitive logging curves for different types of volcanic lava are determined. Based on the claystone logging framework values ​​of regional volcanic rock sensitive logging curves, the breakage trend of volcanic lava is constructed, thereby determining the extreme values ​​of mudstone breakage boundaries and mudstone boundaries of sensitive logging curves for different types of volcanic clastic rocks. Through the determined theoretical framework points, distribution ranges, and extreme values ​​of breakage points of sensitive logging curves for different types of volcanic lava, and the determined extreme values ​​of mudstone breakage boundaries and mudstone boundaries of sensitive logging curves for different types of volcanic clastic rocks, quantitative classification of different types of volcanic lava and volcanic clastic rocks can be achieved through sensitive curve cross-plots.

[0038] This invention solves the problems of existing volcanic rock lithology identification methods lacking convenience, applicability, and scalability. By introducing extreme values ​​of fracture points, fracture trends, extreme values ​​of mudification points, and mudification boundaries, a highly universal volcanic rock lithology identification chart is finally established. Theoretically, it is more consistent with the actual stratigraphic and rock physical characteristics, has higher lithology identification accuracy and reliable theoretical basis, is convenient and simple to calculate, highly universal, and widely applicable. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the volcanic rock lithology identification method based on extreme value statistics according to an embodiment of the present invention;

[0041] Figure 2 This is a structural diagram of the volcanic rock lithology identification system based on extreme value statistics according to an embodiment of the present invention;

[0042] Figure 3 The above are sensitivity analysis diagrams of logging curves for volcanic rock lithology in an embodiment of the present invention; wherein, (a) is a sensitivity analysis diagram of logging curves for different volcanic lava rocks in the embodiment, and (b) is a sensitivity analysis diagram of logging curves for different volcanic clastic rocks in the embodiment.

[0043] Figure 4The following are statistical charts showing the minimum density values ​​of volcanic lava in embodiments of the present invention; wherein, (a) is a statistical chart showing the minimum density values ​​of basalt; (b) is a statistical chart showing the minimum density values ​​of andesite; (c) is a statistical chart showing the minimum density values ​​of dacite; and (d) is a statistical chart showing the minimum density values ​​of rhyolite.

[0044] Figure 5 The following are statistical charts of natural gamma-ray maximum values ​​of volcanic lava according to embodiments of the present invention; wherein, (a) is a statistical chart of natural gamma-ray maximum values ​​of basalt; (b) is a statistical chart of natural gamma-ray maximum values ​​of andesite; (c) is a statistical chart of natural gamma-ray maximum values ​​of dacite; and (d) is a statistical chart of natural gamma-ray maximum values ​​of rhyolite density.

[0045] Figure 6 This is a volcanic rock lithology identification diagram according to an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0047] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0048] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0049] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0050] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0051] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0052] The present invention will now be described in further detail with reference to the accompanying drawings:

[0053] See Figure 1 This invention discloses a method for identifying volcanic rock lithology based on extreme value statistics, comprising:

[0054] S101, Select volcanic rocks with different lithologies and lithofacies as core samples and determine the lithology type of the selected core samples;

[0055] The lithology of the core samples was obtained through the thin section identification test method; the thin section identification test was conducted based on the industry standard "Thin Section Identification (SY / T5368-2016)".

[0056] The core samples included volcanic lava and pyroclastic rocks; the volcanic lava included basalt, andesite, dacite, and rhyolite; the pyroclastic rocks included basaltic volcanic breccia, andesitic volcanic breccia, dacite volcanic breccia, and rhyolitic volcanic breccia.

[0057] The core samples are one or more of the following: basalt, andesite, dacite, rhyolite, basaltic volcanic breccia, andesitic volcanic breccia, dacite volcanic breccia, and rhyolitic volcanic breccia.

[0058] S102, obtain logging curves of core samples with different lithologies and perform response characteristic analysis to determine the sensitive logging curves of the selected core samples;

[0059] As the lithology of volcanic lava changes from basic to acidic, the natural gamma logging value gradually increases while the density logging value gradually decreases. The natural gamma and density of volcanic lava are identified as the sensitive logging curves.

[0060] As the lithology of volcanic clastic rocks changes from basic to acidic, the natural gamma logging value gradually increases while the density logging value gradually decreases. The natural gamma and density logging curves of volcanic clastic rocks were determined to be the most sensitive logging curves.

[0061] S103, based on sensitive logging curves, uses extreme value statistical theory and regional rock property models to obtain the theoretical skeleton points, distribution ranges, and fracture point extremes of sensitive logging curves for different types of volcanic lava.

[0062] Based on the regional rock property model, the maximum density and minimum natural gamma of basic volcanic lava are obtained, i.e., the theoretical skeleton point a0(x0, y0) of basic volcanic lava. Based on the extreme value statistics theory, the minimum density and maximum natural gamma of intermediate-basic, intermediate-acidic, and acidic volcanic lava are obtained, and the extreme values ​​of the fracture points of various types of volcanic lava are determined, i.e., the natural gamma-density intersection points a1(x1, y1), a2(x2, y2), a3(x3, y3), a4(x4, y4), and the maximum natural gamma points b1(x1, 0), b2(x2, 0), b3(x3, 0), b4(x4, 0) of various types of volcanic lava.

[0063] The intermediate-basic volcanic lava is basalt; the intermediate volcanic lava is andesite; the intermediate-acidic volcanic lava is dacite; and the acidic volcanic lava is rhyolite.

[0064] S104, based on the clay rock logging skeleton value of the regional volcanic rock sensitive logging curve, obtains the volcanic lava fracturing trend and determines the extreme value of the mudification point and mudification boundary of the sensitive logging curve of different types of volcanic clastic rocks.

[0065] Based on the clay rock logging framework values ​​of the sensitive logging curves of regional volcanic rocks, the fracturing trend of volcanic lava is obtained, and the extreme values ​​of the mudification point and mudification boundary of the sensitive logging curves of different types of volcanic clastic rocks are determined; specifically:

[0066] Using a regional rock property model, the well logging skeleton values ​​of basic weathered claystone, namely the basic volcanic claystone skeleton point c1(x5, y5), are obtained. Based on the skeleton points of basic volcanic claystone and the extreme values ​​of basic volcanic lava fracturing points, the fracturing trend F of basic volcanic lava is constructed.

[0067]

[0068] Based on the regional rock property model, the logging skeleton value of acid weathered claystone is obtained. Combined with the fracturing trend, the extreme value of the acid mudstone point of complete clayification is obtained, c4(x8, y8).

[0069] The mudstone boundary is determined by connecting the skeleton point c1(x5, y5) of basic volcanic claystone and the extreme value c4(x8, y8) of acidic mudstone. The fracture boundaries of intermediate volcanic clastic rocks and intermediate-acidic volcanic clastic rocks are obtained based on the fracture trend F of basic volcanic lava and the extreme values ​​of fracture points of intermediate volcanic lava and intermediate-acidic volcanic lava. Then, the intersection points of the fracture boundaries of intermediate volcanic clastic rocks, intermediate-acidic volcanic clastic rocks and acidic volcanic clastic rocks with the mudstone boundary are obtained; that is, the extreme values ​​of each mudstone point c2(x6, y6), c3(x7, y7), and c4(x8, y8).

[0070] The neutral volcanic breccia is andesitic volcanic breccia; the intermediate-acidic volcanic breccia is dacite volcanic breccia; and the acidic volcanic breccia is rhyolitic volcanic breccia.

[0071] S105. Based on the theoretical framework points of sensitive logging curves for different types of volcanic lava, the distribution range of sensitive logging curves, the extreme values ​​of fracture points, and the extreme values ​​of mudification points and mudification boundaries of sensitive logging curves for different types of volcanic clastic rocks, a cross plot of sensitive curves is drawn to obtain the quantitative division range of different types of volcanic lava and volcanic clastic rocks.

[0072] See Figure 2 This invention discloses a volcanic rock lithology identification system based on extreme value statistics, comprising:

[0073] The first determining module is used to select volcanic rocks with different lithologies and lithofacies as core samples and determine the lithology type of the selected core samples.

[0074] The response analysis module is used to acquire logging curves of core samples with different lithologies and perform response characteristic analysis to determine the sensitive logging curves of the selected core samples.

[0075] The acquisition module, based on sensitive logging curves, uses extreme value statistical theory and regional rock property models to acquire the theoretical skeleton points, distribution ranges, and fracture point extremes of sensitive logging curves for different types of volcanic lava.

[0076] The second determining module, based on the clay rock logging skeleton value of the regional volcanic rock sensitive logging curve, obtains the volcanic lava fracturing trend and determines the extreme value of the mudification point and mudification boundary of the sensitive logging curve of different types of volcanic clastic rocks.

[0077] The plotting module, based on the theoretical skeleton points of sensitive logging curves for different types of volcanic lava, the distribution range of sensitive logging curves, the extreme values ​​of fracture points, and the extreme values ​​of mudification points and mudification boundaries of sensitive logging curves for different types of volcanic clastic rocks, plots sensitive curve intersection diagrams to obtain quantitative division ranges for different types of volcanic lava and volcanic clastic rocks.

[0078] The technical problem to be solved is further described in detail in the embodiments of the present invention.

[0079] 1. Based on the core and well logging data of the volcanic rock strata, core samples of volcanic rocks with different lithologies were selected and thin section analysis experiments were conducted to determine their lithology.

[0080] In this embodiment, a volcanic rock formation in an oilfield block was selected as the target layer for study. Core data and well logging data of the target layer were collected, and 337 representative volcanic rock core samples were selected based on these data. In this embodiment, the so-called representative core refers to volcanic rock samples with different characteristics shown by conventional well logging, well logging, core description, and special well logging data. The lithology type was determined according to the standard procedure of "Identification of Rock Thin Sections (SY / T5368-2016)".

[0081] 2. Well logging response is a comprehensive reflection of lithology, porosity, pore structure, and fluid properties, and is particularly sensitive to the lithology response of volcanic rocks. Volcanic reservoirs are characterized by multiple volcanic eruption periods, diverse lithofacies, complex mineral content, varying degrees of weathering and fragmentation, and complex lithology, leading to limitations in the use of a single well logging curve for lithological classification. When volcanic lava changes from basic to acidic, various curve characteristics change accordingly: natural gamma increases and density decreases; when volcanic lava changes to volcanic breccia, density decreases. Therefore, natural gamma and density logging curves are identified as lithology-sensitive curves, such as... Figure 3 As shown.

[0082] 3. Using established sensitive logging curves, the natural gamma ray logging value gradually increases and the density logging value gradually decreases as the lithology of volcanic lava transitions from basic to acidic. Through a regional rock property model, the maximum density and minimum natural gamma ray value of basalt are determined, i.e., the theoretical framework point a0(0, 2.85) of basalt. Analysis shows that homogeneous volcanic breccia and volcanic lava exhibit consistent natural gamma ray ranges, but significantly reduced density values. Using extreme value statistics theory, the minimum density and maximum natural gamma ray values ​​of basalt, andesite, dacite, and rhyolite are determined, thereby identifying the extreme values ​​of the fracture points for various volcanic rocks and lava. (See also...) Figure 4 and Figure 5 , namely the natural gamma-density intersection points a1(60, 2.60), a2(80, 2.55), a3(115, 2.50), a4(160, 2.40), and the maximum natural gamma points of various volcanic rocks and lava, b1(60, 0), b2(80, 0), b3(115, 0), b4(160, 0).

[0083] 4. Using the established sensitive logging curves, the natural gamma ray logging value gradually increases and the density logging value gradually decreases as the lithology of volcanic clastic rocks changes from basic to acidic, thus establishing natural gamma and density as sensitive logging curves. Through a regional rock property model, the density logging framework value for basaltic weathered claystone is determined, specifically the extreme value c1(0, 2.35) of the fully clay-formed basaltic mudstone argillaceous point. Combining this with logging theory, the volcanic lava fracturing trend F is constructed using the basaltic claystone framework point and the extreme value of the basalt fracturing point.

[0084]

[0085] Studies show that the brittleness index of various volcanic lava rocks is relatively large and the distribution range is consistent, thus the fracturing trend is consistent. By analyzing the regional rock skeleton response, the logging skeleton value of rhyolitic weathered claystone was determined. Combined with the fracturing trend, the extreme value of the rhyolitic mudstone point of complete clayification was determined as c4 (112, 2.20). The mudstone boundary was determined by connecting the extreme values ​​of the basaltic mudstone point and the rhyolitic mudstone point. Then, using the determined fracturing trend and the extreme values ​​of the fracturing points of andesite and dacite, the fracturing boundaries of andesitic volcanic breccia and dacite volcanic breccia were determined. Furthermore, the natural gamma-density intersection points of the fracturing boundaries and mudstone boundaries of andesitic volcanic breccia, dacite volcanic breccia, and rhyolitic volcanic breccia were determined, namely the extreme values ​​of mudstone point c2 (24.73, 2.32) and c3 (59.85, 2.27).

[0086] 5. Combining well logging theory, using the basalt framework point a0, the extreme values ​​of other volcanic lava fracture points a1, a2, a3, a4, the natural gamma maximum points b1, b2, b3, b4, and the natural gamma-density intersection points c1, c2, c3, c4 of various volcanic clastic rock fracture boundaries and mudstone boundaries, quantitative classification of volcanic lava and volcanic clastic rocks can be achieved.

[0087] See Figure 5Starting from the theoretical framework point a0(0, 2.85) of basic volcanic lava, connect a1(60, 2.60) and c1(0, 2.35); a1(60, 2.60) connects b1(60, 0), a2(80, 2.55) and c1(0, 2.35); a2(80, 2.55) connects c2(24.73, 2.32), b2(80, 0) and a3(115, 2.50); ​​a3(115, 2.50) connects c2(24.73, 2.32), b2(80, 0) and a3(115, 2.50); ​​a3(115, 2.50) connects a1(60, 2.60) and c1(0, 2.35) respectively. Connect b3(115, 0), a4(160, 2.40), and c3(59.85, 2.27); connect a4(160, 2.40) to b4(160, 0) and c4(112, 2.20); connect c1(0, 2.35) to c2(24.73, 2.32); connect c2(24.73, 2.32) to c3(59.85, 2.27); connect c3(59.85, 2.27) to c4(112, 2.20). Plot the sensitivity curve cross plot to obtain the quantitative division intervals of different types of volcanic lava and pyroclastic rocks.

[0088] In summary, this invention first selects volcanic rock cores with different lithologies and lithofacies, classifies and screens them as core samples, then conducts thin-section identification experiments on the core samples to determine their specific lithology; analyzes the response characteristics of the experimental data of core samples with different lithologies on conventional logging curves to determine sensitive logging curves reflecting lithology; using the obtained sensitive logging curves, through extreme value statistics theory and regional rock skeleton response, determines the theoretical skeleton points of the sensitive logging curves for basalt and the distribution ranges and breakpoints of the sensitive logging curves for andesite, dacite, and rhyolite. Extreme values; by identifying regional skeleton response characteristics, well logging skeleton values ​​for basaltic claystone and rhyolitic claystone are determined, and mudification boundaries are identified. Simultaneously, the fracturing trend of volcanic lava is constructed, thereby determining the fracturing boundaries of sensitive well logging curves for basaltic volcanic breccia, andesitic volcanic breccia, dacite volcanic breccia, and rhyolitic volcanic breccia, as well as the mudification point extreme values ​​for andesitic and dacite volcanic breccia. Based on the determined distribution range of sensitive well logging curves, fracturing point extreme values, fracturing boundaries, mudification point extreme values, and mudification boundaries, quantitative classification of volcanic lava and pyroclastic rocks can be achieved. This invention solves the problems of inconvenience, applicability, and scalability of existing volcanic rock lithology identification methods. By introducing fracturing point extreme values, fracturing trends, and mudification point extreme values, a highly universal volcanic rock lithology identification chart is ultimately established. Theoretically, it better conforms to actual stratigraphic and petrological characteristics, has higher lithology identification accuracy and reliable theoretical basis, is convenient and simple to calculate, highly universal, and widely applicable.

[0089] An embodiment of the present invention provides a terminal device. This terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0090] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0091] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0092] The processor may 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.

[0093] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0094] If the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments 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 various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0095] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying volcanic rock lithology based on extreme value statistics, characterized in that, include: Volcanic rocks with different lithologies and lithofacies were selected as core samples to determine the lithology of the selected core samples. Log logging curves of core samples with different lithologies were obtained and response characteristic analysis was performed to determine the sensitive log logging curves of the selected core samples. Based on sensitive logging curves, and through extreme value statistics theory and regional rock property models, the theoretical framework points, distribution ranges, and extreme values ​​of breakpoints of sensitive logging curves for different types of volcanic lava are obtained. Specifically, based on the regional rock property model, the maximum density and minimum natural gamma of basic volcanic lava are obtained, i.e., the theoretical framework points of basic volcanic lava; based on extreme value statistics theory, the minimum density and maximum natural gamma of intermediate-basic, intermediate-acidic, and acidic volcanic lava are obtained, and the extreme values ​​of breakpoints of various types of volcanic lava, i.e., the natural gamma-density intersection points, and the maximum natural gamma points of various types of volcanic lava are determined. Based on the clay rock logging skeleton value of the sensitive logging curve of regional volcanic rocks, the fracturing trend of volcanic lava is obtained, and the extreme value of the mudification point and mudification boundary of the sensitive logging curve of different types of volcanic clastic rocks are determined. Based on the theoretical framework points, distribution ranges, extreme values ​​of fracture points, and extreme values ​​of mudstone formation points and mudstone formation boundaries of sensitive logging curves for different types of volcanic lava, cross plots of sensitive logging curves are drawn to obtain quantitative division ranges for different types of volcanic lava and volcanic clastic rocks.

2. The volcanic rock lithology identification method based on extreme value statistics according to claim 1, characterized in that, The lithology of the core samples was obtained using the thin section identification method.

3. The volcanic rock lithology identification method based on extreme value statistics according to claim 2, characterized in that, The core samples included volcanic lava and pyroclastic rocks; The volcanic lava includes basalt, andesite, dacite, and rhyolite; the volcanic breccia includes basaltic volcanic breccia, andesitic volcanic breccia, dacite volcanic breccia, and rhyolitic volcanic breccia. The core samples are one or more of the following: basalt, andesite, dacite, rhyolite, basaltic volcanic breccia, andesitic volcanic breccia, dacite volcanic breccia, and rhyolitic volcanic breccia.

4. The volcanic rock lithology identification method based on extreme value statistics according to claim 3, characterized in that, The process involves obtaining logging curves from core samples of different lithologies and performing response characteristic analysis to determine the sensitive logging curves for the selected core samples; specifically: As the lithology of volcanic lava changes from basic to acidic, the natural gamma logging value gradually increases while the density logging value gradually decreases. The natural gamma and density of volcanic lava are identified as the sensitive logging curves. As the lithology of volcanic clastic rocks changes from basic to acidic, the natural gamma logging value gradually increases while the density logging value gradually decreases. The natural gamma and density logging curves of volcanic clastic rocks were determined to be the most sensitive logging curves.

5. The volcanic rock lithology identification method based on extreme value statistics according to claim 4, characterized in that, The intermediate-basic volcanic lava is basalt; the intermediate volcanic lava is andesite; the intermediate-acidic volcanic lava is dacite; and the acidic volcanic lava is rhyolite.

6. The volcanic rock lithology identification method based on extreme value statistics according to claim 5, characterized in that, The clay rock logging framework value based on the regional volcanic rock sensitive logging curve is used to obtain the volcanic lava fracturing trend and determine the extreme values ​​of the mudification point and mudification boundary of the sensitive logging curve for different types of volcanic clastic rocks; specifically: By using a regional rock property model, well logging framework values ​​for basic weathered claystone, i.e., basic volcanic claystone framework points, are obtained. Based on the framework points of basic volcanic claystone and the extreme values ​​of the fracturing points of basic volcanic lava, a fracturing trend of basic volcanic lava is constructed. : Based on the regional rock property model, well logging framework values ​​for acidic weathered claystone are obtained. Combined with the fracturing trend, the extreme values ​​of acidic mudstone at the point of complete clayification are obtained. ; Based on the framework points of basic volcanic claystone and the extreme value of acidic mud formation point Connecting the two lines determines the mudstone boundary; based on the fracturing trend of basic volcanic lava... By analyzing the extreme values ​​of fracture points in intermediate and intermediate-acidic volcanic lava, the fracture boundaries of intermediate and intermediate-acidic volcanic clastic rocks are obtained. Furthermore, the intersection points of these fracture boundaries with the mudstone boundaries are obtained; these are the extreme values ​​of each mudstone point. , , ; The neutral volcanic breccia is andesitic volcanic breccia; the intermediate-acidic volcanic breccia is dacite volcanic breccia; and the acidic volcanic breccia is rhyolitic volcanic breccia.

7. A volcanic rock lithology identification system based on extreme value statistics, used to implement the volcanic rock lithology identification method based on extreme value statistics as described in any one of claims 1-6, characterized in that, include: The first determining module is used to select volcanic rocks with different lithologies and lithofacies as core samples and determine the lithology type of the selected core samples. The response analysis module is used to acquire logging curves of core samples with different lithologies and perform response characteristic analysis to determine the sensitive logging curves of the selected core samples. The acquisition module, based on sensitive logging curves, uses extreme value statistical theory and regional rock property models to acquire the theoretical skeleton points, distribution ranges, and fracture point extremes of sensitive logging curves for different types of volcanic lava. The second determining module, based on the clay rock logging skeleton value of the regional volcanic rock sensitive logging curve, obtains the volcanic lava fracturing trend and determines the extreme value of the mudification point and mudification boundary of the sensitive logging curve of different types of volcanic clastic rocks. The plotting module, based on the theoretical skeleton points of sensitive logging curves for different types of volcanic lava, the distribution range of sensitive logging curves, the extreme values ​​of fracture points, and the extreme values ​​of mudification points and mudification boundaries of sensitive logging curves for different types of volcanic clastic rocks, plots sensitive curve intersection diagrams to obtain quantitative division ranges for different types of volcanic lava and volcanic clastic rocks.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of 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 steps of the method as described in any one of claims 1-6.

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