Stratigraphic vertical lithofacies superposition rule analysis method, device, equipment and medium

Abstract

The application discloses a stratum vertical lithofacies superimposition rule analysis method, device, equipment and medium. The method comprises the following steps: obtaining well logging data of a target stratum, dividing the target stratum into multiple unit strata according to lithology data in the well logging data; determining the numbering information of each unit stratum according to a preset numbering scheme; the numbering information is used for reflecting the lithology of the unit stratum; determining a target Markov index of the target stratum according to the numbering information of each unit stratum corresponding to the target stratum from bottom to top; determining a first preset number of candidate strata; determining a candidate Markov index of the candidate stratum according to the numbering information of each unit stratum corresponding to the candidate stratum from bottom to top; determining a target similarity according to the target Markov index and the candidate Markov index, and reflecting whether the lithology change of the target stratum is a periodic change rule according to the target similarity. The technical scheme of the application realizes accurate analysis on the vertical lithofacies superimposition rule of the stratum.

Description

Technical Field

[0001] This invention relates to the field of sedimentary geology, and in particular to a method, apparatus, equipment, and medium for analyzing the vertical lithofacies superposition patterns of strata. Background Technology

[0002] Current sedimentology and sequence stratigraphy are largely based on traditional qualitative analysis and interpretation, which helps to identify stratigraphic development patterns and formation models. Vertically, stratigraphic regularity can be defined as a specific arrangement of lithofacies or per unit stratigraphic thickness. It must exhibit a identifiable upward trend or superposition pattern and is unlikely to occur by chance; therefore, it requires the systematic action of specific geological processes to form. Traditional stratigraphic interpretations based on sedimentary cycles are often entirely qualitative, thus relatively subjective and inherently uncertain. Sometimes, the interpretation of regularity is merely an implicit assumption of the sequence stratigraphic model. This introduces difficulties and uncertainties into constructing basin sedimentary stratigraphic frameworks, testing existing geological knowledge, and analyzing underground oil and gas reservoirs. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, and medium for analyzing the vertical lithofacies superposition pattern of strata, so as to achieve accurate analysis of the vertical lithofacies superposition pattern of strata.

[0004] According to one aspect of the present invention, a method for analyzing the vertical lithofacies superposition regularity of strata is provided, the method comprising:

[0005] Obtain logging data of the target formation, and divide the target formation into multiple unit formations based on the lithological data in the logging data; all the unit formations constitute the target formation, and each unit formation corresponds to a lithology; the lithology includes the grain size of the rock particles in the formation;

[0006] The numbering information of each unit stratum is determined according to a preset numbering scheme; the preset numbering scheme is a scheme that sets the corresponding strata with numbering information from small to large according to the lithology of the strata from fine to coarse, and the numbering information is used to reflect the lithology of the unit stratum;

[0007] The target Markov index of the target stratum is determined based on the numbering information of each unit stratum corresponding to the target stratum from bottom to top;

[0008] A first preset number of candidate strata are determined; the candidate strata are obtained by exchanging the positions of a second preset number of unit strata with different lithologies in the target strata.

[0009] Based on the numbering information of each unit stratum corresponding to the candidate strata from bottom to top, the candidate Markov index of the candidate strata is determined;

[0010] The target similarity is determined based on the target Markov index and the candidate Markov index. The target similarity reflects whether the lithological changes of the target strata follow a periodic pattern. The target similarity is the similarity between the target strata and the candidate strata.

[0011] According to another aspect of the present invention, a device for analyzing the vertical lithofacies superposition pattern of strata is provided, the device comprising:

[0012] The first formation determination module is used to acquire well logging data of the target formation and divide the target formation into multiple unit formations based on the lithological data in the well logging data; all the unit formations constitute the target formation, and each unit formation corresponds to a lithology; the lithology includes the grain size of the rock particles in the formation;

[0013] The information determination module is used to determine the numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme that sets the corresponding strata with numbering information from small to large according to the lithology of the strata from fine to coarse, and the numbering information is used to reflect the lithology of the unit stratum;

[0014] The first indicator determination module is used to determine the target Markov index of the target stratum based on the numbering information of each unit stratum corresponding to the target stratum from bottom to top.

[0015] The second stratigraphic determination module is used to determine a first preset number of candidate stratigraphic units; the candidate stratigraphic units are obtained by exchanging the positions of a second preset number of unit stratigraphic units with different lithologies in the target stratigraphic unit.

[0016] The second indicator determination module is used to determine the candidate Markov index of the candidate strata based on the numbering information of each unit stratum corresponding to the candidate strata from bottom to top.

[0017] The analysis module is used to determine the target similarity based on the target Markov index and the candidate Markov index, and to reflect whether the lithological changes of the target strata are periodic. The target similarity is the similarity between the target strata and the candidate strata.

[0018] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0019] At least one processor; and

[0020] A memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the method for analyzing the vertical lithofacies superposition of strata according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the method for analyzing the vertical lithofacies superposition law of strata as described in any embodiment of the present invention.

[0023] The technical solution of this invention involves acquiring logging data of a target formation, dividing the target formation into multiple unit formations based on lithological data in the logging data, and then using all unit formations to form the target formation, with each unit formation corresponding to a specific lithology. Lithology includes the grain size of the rock particles in the formation. Further, the numbering information for each unit formation is determined according to a preset numbering scheme. The preset numbering scheme assigns numbers from smallest to largest to the corresponding lithology, from finest to coarser, and the numbering information reflects the lithology of each unit formation. This facilitates the determination of the target Markov index of the target formation based on the numbering information of each unit formation corresponding to the target formation from bottom to top. To enhance data noise reduction capabilities, a first preset number of candidate formations are further determined. The process involves selecting candidate strata. Candidate strata are obtained by exchanging the positions of a second predetermined number of strata with different lithologies within the target strata. Then, based on the numbering information of each stratum corresponding to the candidate strata from bottom to top, candidate Markov indices are determined. Finally, the similarity between the target Markov indices and the candidate Markov indices is used to determine whether the lithological changes in the target strata follow a periodic pattern. The target similarity is the similarity between the target strata and the candidate strata. In other words, this scheme shifts from qualitative to quantitative analysis by comparing the target Markov indices and the candidate Markov indices. The principle is simple and easy to learn, and the results are objective, reproducible, and easy to understand, enabling accurate and rapid analysis of the vertical lithofacies superposition patterns of strata.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a method for analyzing the vertical lithofacies superposition pattern of strata according to an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram showing the correspondence between the numbering information applicable to the embodiments of the present invention and the target strata;

[0028] Figure 3 This is a schematic diagram of a preset numbering scheme applicable to embodiments of the present invention;

[0029] Figure 4 This is an example diagram of a Markov probability matrix applicable to embodiments of the present invention;

[0030] Figure 5 This is an example diagram of the target Markov index and probability density function applicable according to embodiments of the present invention;

[0031] Figure 6 This is a schematic diagram illustrating the analysis of six different target strata applicable to embodiments of the present invention;

[0032] Figure 7 The results are from the moving-expanding window analysis of the H5 segment of a certain structure in the reference area depression, which is applicable according to the embodiments of the present invention.

[0033] Figure 8 This is a schematic diagram of the structure of a stratigraphic vertical lithofacies superposition pattern analysis device provided in an embodiment of the present invention;

[0034] Figure 9 This is a schematic diagram of the structure of an electronic device for implementing the method for analyzing the vertical lithofacies superposition of strata according to an embodiment of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] Example 1

[0038] Figure 1 This is a flowchart illustrating a method for analyzing the vertical lithofacies superposition pattern of strata according to an embodiment of the present invention. This embodiment is applicable to the analysis of the vertical lithofacies superposition pattern of strata. This method can be executed by a device for analyzing the vertical lithofacies superposition pattern of strata. This device can be implemented in hardware and / or software and can be configured in any electronic device with network communication capabilities. Figure 1 As shown, the method for analyzing the vertical lithofacies superposition pattern of strata in this invention includes the following process:

[0039] S110. Obtain logging data of the target formation, and divide the target formation into multiple unit formations based on the lithological data in the logging data; each unit formation corresponds to a lithology, and the lithological data includes the grain size of the rock particles in the formation.

[0040] The lithological data is mainly controlled by the grain size of the rock particles and reflects the characteristics of the sedimentary environment.

[0041] Specifically, the target stratum is a stratum with a certain thickness. Within the target stratum, there are strata with consistent lithology of a certain thickness. The stratum corresponding to this thickness is a unit stratum. Therefore, by acquiring the logging data of the target stratum and analyzing the lithology data in the logging data, the target stratum can be divided into multiple unit strata from bottom to top. All unit strata can be connected sequentially according to their corresponding positions to form the target stratum.

[0042] S120. Determine the numbering information for each unit stratum according to the preset numbering scheme; the preset numbering scheme is a scheme that sets the corresponding strata with numbering information from small to large according to the lithology of the strata from fine to coarse, and the numbering information is used to reflect the lithology of the unit stratum.

[0043] Specifically, a pre-defined numbering scheme can be determined based on the characteristics of the sedimentary environment. Generally, the grain size of sediment particles increases with increasing hydrodynamic force, so the lithology numbers increase sequentially from mudstone to sandstone to conglomerate. That is, the pre-defined numbering scheme can be understood as assigning a number to each lithology, with larger grain sizes corresponding to larger numbers. For example... Figure 3 The image shows an example of a preset numbering scheme. The rock grain size of mudstone, argillaceous sandstone, siltstone, sandstone and conglomerate increases, and the corresponding numbering information is 1, 2, 3, 4 and 5.

[0044] S130. Based on the numbering information of each unit stratum corresponding to the target stratum from bottom to top, determine the target Markov index of the target stratum.

[0045] Among them, Markov indices can be understood as indicative information reflecting the superposition law of lithofacies of the target strata.

[0046] Specifically, "target strata from bottom to top" refers to the geographical locations corresponding to the target strata from bottom to top. The combination of the numbering information of each unit stratum corresponding to the target strata from bottom to top is correlated with Markov indices. Based on this correlation and the numbering information of each unit stratum corresponding to the target strata from bottom to top, the target Markov indices of the target strata can be accurately determined. The correlation can describe the method used to obtain the Markov indices from the numbering information of each unit stratum corresponding to the target strata from bottom to top.

[0047] In this embodiment, optionally, the target Markov index of the target stratum is determined according to the numbering information of each unit stratum corresponding to the target stratum from bottom to top, including: determining the lithological variation information of the target stratum from bottom to top according to the numbering information of each unit stratum; converting the lithological variation information into a Markov probability matrix; and determining the target Markov index of the target stratum according to the Markov probability matrix.

[0048] Specifically, lithological variation information can be the change in the numbering information of adjacent strata, for example, Figure 2 The target stratum is shown in the figure. The numbering information of each unit stratum is displayed in the figure. Thus, from bottom to top, we can know the number of times the numbering information changes from 1 to 2, as well as the changes in the numbering information of other adjacent unit strata.

[0049] Furthermore, lithological variation information can be transformed into a Markov probability matrix, for example, using... Figure 2 Taking the target stratum as an example, Figure 3 Taking the preset numbering scheme as an example, the Markov probability matrix can be as follows: Figure 4 The matrix information shown.

[0050] Furthermore, the target Markov index m of the target stratum is determined using the following formula:

[0051] Where F is the total number of rows in the Markov probability matrix, j is the difference between each diagonal and the main diagonal of the Markov probability matrix; arg min and arg max are functions for finding the minimum and maximum values ​​in the sequence; diag is a function for finding all elements within the diagonal at j displacements from the main diagonal of the Markov probability matrix; ∑diag(T) j ) and ∑diag(T -(F-j) ) represents the sum of the diagonal elements in the Markov probability matrix.

[0052] This embodiment's technical solution determines the lithological variation information of the target stratum from bottom to top based on the numbering information of each unit stratum; it then converts the lithological variation information into a Markov probability matrix, determines the target Markov index of the target stratum based on the Markov probability matrix, and quantitatively calculates m=max. diag -min diag ;

[0053]

[0054] The target Markov indicator was accurately determined.

[0055] S140. Determine a first preset number of candidate strata; the candidate strata are obtained by exchanging the positions of a second preset number of unit strata with different lithologies in the target strata.

[0056] Specifically, the vertical lithofacies superposition pattern of a target stratum is subject to chance when analyzing the target Markov index. Therefore, a second preset number of lithologically different unit strata are selected from the target stratum, and the operation of selecting the second preset number of lithologically different unit strata is performed a first preset number of times. The positions of the second preset number of lithologically different unit strata in the target stratum are also swapped to obtain a first preset number of candidate strata, thus ensuring the diversity of the data.

[0057] S150. Based on the numbering information of each unit stratum corresponding to the candidate strata from bottom to top, determine the candidate Markov index of the candidate strata.

[0058] Specifically, since the numbering information of each unit stratum in S120 has been confirmed, the candidate Markov indexes of each candidate stratum can be accurately determined based on the numbering information of each unit stratum corresponding to the candidate stratum from bottom to top.

[0059] Furthermore, based on the numbering information of each unit stratum corresponding to the candidate strata from bottom to top, the following steps are taken: determining the candidate lithological variation information of the candidate strata from bottom to top based on the numbering information of each unit stratum; converting the candidate lithological variation information into a candidate Markov probability matrix; and determining the candidate Markov index of the candidate strata based on the candidate Markov probability matrix.

[0060] The candidate Markov index m of the candidate strata can be determined using the following formula:

[0061] m = max diag -min diag ;

[0062]

[0063] Where F is the total number of rows in the candidate Markov probability matrix, j is the difference between each diagonal and the main diagonal of the candidate Markov probability matrix; arg min and arg max are functions for finding the minimum and maximum values ​​in the sequence; diag is a function for finding all elements within the diagonal at j displacements from the main diagonal of the Markov probability matrix; ∑diag(T) j ) is the candidate sum ∑diag(T) -(F-j) ) represents the sum of the diagonal elements in the candidate Markov probability matrix.

[0064] S160. Determine the target similarity based on the target Markov index and the candidate Markov index. The target similarity reflects whether the lithological changes of the target strata follow a periodic pattern. The target similarity is the similarity between the target strata and the candidate strata.

[0065] Target similarity can be understood as statistical significance, that is, the significance between the target stratum and the candidate stratum.

[0066] Specifically, a probability density function is constructed based on all candidate Markov indicators; the probability density function is used to reflect the probability of different candidate Markov indicators appearing among all candidate Markov indicators; further, the target similarity is determined based on the target Markov indicator and the probability density function.

[0067] Accordingly, target similarity is determined based on the target Markov index and probability density function, including: determining the target area enclosed by the probability density function and the horizontal axis; determining the reference area enclosed by the probability density function to the right of the target Markov index and the horizontal axis; and using the ratio of the reference area to the target area as the target similarity. For example, ... Figure 5The example graph shown depicts the target Markov index and probability density function. The red line represents the target Markov index, the blue border represents the probability density function, and the blue covered area represents the target area enclosed by the probability density function and the horizontal axis. Figure 5 If the reference area enclosed by the probability density function to the right of the Markov index of the target and the horizontal axis is zero, then the target similarity is zero.

[0068] Furthermore, the target similarity value is within the range of 0 to 1. The target similarity reflects whether the lithological changes in the target strata follow a periodic pattern. Specifically: if the target similarity is less than a preset similarity, the lithological changes in the target strata are determined to follow a periodic pattern; if the target similarity is greater than the preset similarity, the lithological changes in the target strata are determined not to follow a periodic pattern. The preset similarity can be 0 or a value close to 0.

[0069] Optionally, the Markov index, the numbering information of each unit stratum, the Markov probability matrix, and the probability density function of this method can be displayed on the display interface, such as... Figure 2 , Figure 4 , Figure 5 All information can be displayed on the interface. Furthermore, because it can be displayed on the interface, the thickness of the target stratum can be adjusted by moving or expanding the analysis window, thus enabling a systematic sensitivity analysis of this method and facilitating intuitive observation of the data.

[0070] The technical solution of this invention involves acquiring well logging data of a target formation, dividing the target formation into multiple unit formations based on lithological data in the well logging data, and then using all unit formations to form the target formation, with each unit formation corresponding to a specific lithology. Lithology includes the grain size of the rock particles in the formation. Furthermore, the numbering information for each unit formation is determined according to a preset numbering scheme. The preset numbering scheme assigns numbers from smallest to largest to the corresponding lithology, from finest to coarser, and the numbering information reflects the lithology of each unit formation. This facilitates the determination of the target Markov index of the target formation based on the numbering information of each unit formation corresponding to the target formation from bottom to top. To enhance data noise reduction capabilities, a first preset number of candidate formations is further determined. The process involves selecting candidate strata. Candidate strata are obtained by exchanging the positions of a second predetermined number of strata with different lithologies within the target strata. Then, based on the numbering information of each stratum corresponding to the candidate strata from bottom to top, candidate Markov indices are determined. Finally, the similarity between the target Markov indices and the candidate Markov indices is used to determine whether the lithological changes in the target strata follow a periodic pattern. The target similarity is the similarity between the target strata and the candidate strata. In other words, this scheme shifts from qualitative to quantitative analysis by comparing the target Markov indices and the candidate Markov indices. The principle is simple and easy to learn, and the results are objective, reproducible, and easy to understand, enabling accurate and rapid analysis of the vertical lithofacies superposition patterns of strata.

[0071] Example 2

[0072] To further verify the method for analyzing the vertical lithofacies superposition of stratigraphic units, this method will be applied to six different target strata, each consisting of 50 stratigraphic units. Figure 6 As shown in the figure, target strata 1 and 2 exhibit obvious periodic variations, while target strata 5 and 6 are composed of computer-generated random numbers with different lithological distribution frequencies. Target strata 3 and 4 are both dominated by two lithologies, but target strata 3 shows a regular arrangement, while target strata 4 shows no regularity. After repeating the experiment 50 times and constructing a 95% confidence interval, the results show that the method for analyzing the vertical lithofacies superposition of strata has good discriminative power. Even when the strata were disturbed, the differences between the P-value curves remained significant until approximately 15 disturbances (i.e., 30 strata units were disturbed) before the curves began to overlap and become indistinguishable.

[0073] This indicates that the method for analyzing the vertical lithofacies superposition of stratigraphy is highly resistant to data noise and can effectively discover patterns in stratigraphic superposition patterns that are not easily visible to the naked eye but are supported by mathematics and statistics. This is of great significance for clarifying how strata with periodic characteristics are controlled by potential factors, and further understanding the inheritance, periodicity and differences in the spatial distribution of sedimentary systems.

[0074] In addition, such as Figure 7 As shown, the method for analyzing the vertical lithofacies superposition of strata in this invention is applied to the same stratum (H5) of two different wells 3 km apart in a certain structure of a reference area depression. The target strata are adjusted by moving and expanding the window, and the target similarity is determined according to the method for analyzing the vertical lithofacies superposition of strata in this invention. Thus, the lithological changes of each target stratum are reflected according to the target similarity to see whether the changes are periodic.

[0075] from Figure 7 It can be seen that deltaic plain sedimentary facies with different vertical lithological superposition patterns can be identified and distinguished. The two wells have similar surface characteristics (lithology, sand-to-soil ratio), both have a thick sand body at the bottom, rich in mud, and occasionally contain sandstone or siltstone. Planar seismic properties and sedimentary facies diagrams indicate that both are deltaic plain channels separated by mudstone in interdistributary bays, but they are difficult to distinguish.

[0076] Figure 7 The results show that the two reference areas have very different Pm distribution patterns: the overall Pm value of well X-1 is low, indicating strong periodicity in the formation, and Pm decreases with increasing window size. In contrast, the overall Pm value of the adjacent well X-4 is high, and Pm first decreases and then increases with window size, showing a completely different trend from well X-1. These results strongly suggest that the two areas belong to different channels, thus possessing different planar facies zones and sedimentary histories, consistent with post-drilling results. This demonstrates an accurate analysis of the vertical lithofacies superposition patterns of the formations.

[0077] Example 3

[0078] Figure 8 This is a schematic diagram of a stratigraphic vertical lithofacies superposition pattern analysis device provided in an embodiment of the present invention. This embodiment is applicable to the analysis of stratigraphic vertical lithofacies superposition patterns. The stratigraphic vertical lithofacies superposition pattern analysis device can be implemented in hardware and / or software, and can be configured in any electronic device with network communication capabilities. Figure 8 As shown, the analytical apparatus for analyzing the vertical lithofacies superposition of the strata includes:

[0079] The first formation determination module 210 is used to acquire well logging data of the target formation, and divide the target formation into multiple unit formations based on the lithological data in the well logging data; all the unit formations constitute the target formation, and each unit formation corresponds to a lithology; the lithological data includes the grain size of the rock particles in the formation;

[0080] The information determination module 220 is used to determine the numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme that sets the corresponding strata with numbering information from small to large according to the lithology of the strata from fine to coarse, and the numbering information is used to reflect the lithology of the unit stratum;

[0081] The first indicator determination module 230 is used to determine the target Markov index of the target stratum based on the numbering information of each unit stratum corresponding to the target stratum from bottom to top.

[0082] The second stratigraphic determination module 240 is used to determine a first preset number of candidate stratigraphic layers; the candidate stratigraphic layers are obtained by exchanging the positions of a second preset number of unit stratigraphic layers with different lithologies in the target stratigraphic layer.

[0083] The second indicator determination module 250 is used to determine the candidate Markov index of the candidate strata based on the numbering information of each unit stratum corresponding to the candidate strata from bottom to top.

[0084] Analysis module 260 is used to determine the target similarity based on the target Markov index and the candidate Markov index, and to reflect whether the lithological changes of the target strata are periodic changes based on the target similarity; the target similarity is the similarity between the target strata and the candidate strata.

[0085] Based on the above embodiments, optionally, the first index determination module is used to: determine the lithological variation information of the target stratum from bottom to top according to the numbering information of each unit stratum; convert the lithological variation information into a Markov probability matrix; and determine the target Markov index of the target stratum according to the Markov probability matrix.

[0086] Based on the above embodiments, optionally, the target Markov index m of the target stratum can be determined using the following formula:

[0087] m = max diag -min diag ;

[0088]

[0089] Where F is the total number of rows in the Markov probability matrix, j is the difference between each diagonal and the main diagonal of the Markov probability matrix; arg min and arg max are functions for finding the minimum and maximum values ​​in the sequence; diag is a function for finding all elements within the diagonal at j displacements from the main diagonal of the Markov probability matrix; ∑diag(T) j ) and ∑diag(T -(F-j)) is the sum of the diagonal elements in the Markov probability matrix.

[0090] Based on the above embodiments, optionally, the analysis module includes a function determination unit and a similarity determination unit;

[0091] The function determination unit is used to construct a probability density function based on all the candidate Markov indices; the probability density function is used to reflect the probability of different candidate Markov indices appearing among all candidate Markov indices.

[0092] A similarity determination unit is used to determine the target similarity based on the target Markov index and the probability density function.

[0093] Based on the above embodiments, optionally, the similarity determination unit is further configured to: determine the target area enclosed by the probability density function and the horizontal axis; determine the reference area enclosed by the probability density function and the horizontal axis to the right of the target Markov index; and use the ratio of the reference area to the target area as the target similarity.

[0094] Based on the above embodiments, optionally, the target similarity value is in the range of 0 to 1, and the analysis module includes a pattern analysis unit. The pattern analysis unit is used to determine that the lithological change of the target stratum is a periodic change pattern when the target similarity is less than a preset similarity; and to determine that the lithological change of the target stratum is not a periodic change pattern when the target similarity is greater than the preset similarity.

[0095] Based on the above embodiments, optionally, the second index determination module is used to determine the candidate lithological variation information of the candidate strata from bottom to top according to the numbering information of each unit stratum; convert the candidate lithological variation information into a candidate Markov probability matrix; and determine the candidate Markov index of the candidate strata according to the candidate Markov probability matrix.

[0096] The stratigraphic vertical lithofacies superposition law analysis device provided in the embodiments of the present invention can execute the stratigraphic vertical lithofacies superposition law analysis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0097] Example 4

[0098] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0099] Figure 9A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0100] like Figure 9 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0101] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0102] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the method for analyzing the vertical lithofacies superposition patterns of stratigraphy.

[0103] In some embodiments, the method for analyzing the vertical lithofacies superposition of stratigraphic units can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for analyzing the vertical lithofacies superposition of stratigraphic units described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for analyzing the vertical lithofacies superposition of stratigraphic units by any other suitable means (e.g., by means of firmware).

[0104] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0106] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0107] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0108] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0109] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0110] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0111] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for analyzing the vertical lithofacies superposition pattern of stratigraphy, characterized in that, The method comprises: obtaining logging data of a target formation, and dividing the target formation into multiple unit formations according to lithology data in the logging data; all the unit formations constitute the target formation, and each unit formation corresponds to a lithology; the lithology data comprises grain size of rock particles in the formation; determining numbering information of each unit formation according to a preset numbering scheme; the preset numbering scheme is a scheme of setting small-to-large numbering information for corresponding formations from fine to coarse lithology of the formations, and the numbering information is used to reflect the lithology of the unit formation; determining a target Markov index of the target formation according to the numbering information of each unit formation corresponding to the target formation from bottom to top; determining a first preset number of candidate formations; the candidate formation is obtained by exchanging positions of a second preset number of unit formations with different lithologies in the target formation; determining a candidate Markov index of the candidate formation according to the numbering information of each unit formation corresponding to the candidate formation from bottom to top; determining a target similarity between the target Markov index and the candidate Markov index, and reflecting whether the lithology change of the target formation is a periodic change rule according to the target similarity; the target similarity is a similarity between the target formation and the candidate formation.

2. The method of claim 1, wherein, The method for determining the target Markov index of the target formation according to the numbering information of each unit formation corresponding to the target formation from bottom to top comprises: determining lithology change information of the target formation from bottom to top according to the numbering information of each unit formation; converting the lithology change information into a Markov probability matrix, and determining the target Markov index of the target formation according to the Markov probability matrix.

3. The method of claim 2, wherein, The method for determining the target Markov index of the target formation according to the Markov probability matrix comprises: determining the target Markov index m of the target formation by using the following formula: m = max diag - min diag ; where F is the total number of rows of the Markov probability matrix, j is the difference between each diagonal line and the main diagonal line of the Markov probability matrix; arg min and arg max are functions for finding the minimum value and the maximum value from a sequence; diag is a function for finding the elements in the diagonal line with a displacement of j from the main diagonal line in the Markov probability matrix; ∑diag(T j ) and ∑diag(T -(F-j) ) are the sums of the element values of the corresponding diagonal lines in the Markov probability matrix.

4. The method of claim 1, wherein, The method for determining the target similarity between the target Markov index and the candidate Markov index comprises: constructing a probability density function according to all the candidate Markov indexes; the probability density function is used to reflect probabilities of different candidate Markov indexes appearing in all the candidate Markov indexes; determining the target similarity according to the target Markov index and the probability density function.

5. The method of claim 4, wherein, The method for determining the target similarity between the target Markov index and the probability density function comprises: determining a target area surrounded by the probability density function and the horizontal axis; determining a reference area surrounded by the probability density function on the right side of the target Markov index and the horizontal axis; taking a ratio of the reference area to the target area as the target similarity.

6. The method according to claim 1 or 4, characterized in that, The value of the target similarity is in the range of 0 to 1, and whether the lithology change of the target formation is a periodic change rule is reflected according to the target similarity, which comprises: when the target similarity is less than a preset similarity, it is determined that the lithology change of the target formation is a periodic change rule; when the target similarity is greater than the preset similarity, it is determined that the lithology change of the target formation is not a periodic change rule.

7. The method of claim 1, wherein, According to the numbering information of each unit stratum corresponding to the candidate stratum from bottom to top, the candidate stratum is determined to have candidate lithology change information from bottom to top. According to the numbering information of each unit stratum, the candidate lithology change information of the candidate stratum from bottom to top is determined. The candidate lithology change information is converted into a candidate Markov probability matrix, and a candidate Markov index of the candidate stratum is determined according to the candidate Markov probability matrix.

8. A device for analyzing the vertical lithofacies superposition pattern of strata, characterized in that, The device comprises: A first stratum determination module is configured to obtain logging data of a target stratum, and divide the target stratum into a plurality of unit strata according to lithology data in the logging data; all the unit strata constitute the target stratum, and each unit stratum corresponds to one lithology; the lithology includes a particle size of rock particles in the stratum; An information determination module is configured to determine numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme of setting small-to-large numbering information for corresponding strata in the order of fine to coarse according to the lithology of the stratum, and the numbering information is used to reflect the lithology of the unit stratum; A first index determination module is configured to determine a target Markov index of the target stratum according to the numbering information of each unit stratum corresponding to the target stratum from bottom to top; A second stratum determination module is configured to determine a first preset number of candidate strata; the candidate strata are obtained by exchanging positions of a second preset number of unit strata with different lithologies in the target stratum; A second index determination module is configured to determine a candidate Markov index of the candidate stratum according to the numbering information of each unit stratum corresponding to the candidate stratum from bottom to top; An analysis module is configured to determine a target similarity between the target Markov index and the candidate Markov index according to the target similarity reflecting whether the lithology change of the target stratum is a periodic change rule; the target similarity is a similarity between the target stratum and the candidate stratum.

9. An electronic device, comprising: The electronic device comprises: At least one processor; and A memory connected in communication with the at least one processor; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the stratum vertical facies superimposition rule analysis method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the stratum vertical facies superimposition rule analysis method in any one of claims 1-7 when executed.

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