Stratum vertical lithofacies superposition law analysis method, device, equipment and medium

Abstract

The invention discloses a stratum vertical lithofacies superposition law analysis method, device and equipment and a medium. The method comprises the following steps: acquiring logging data of a target stratum, and dividing the target stratum into a plurality of unit stratums according to lithology data in the logging data; according to a preset numbering scheme, determining numbering information of each unit stratum; the serial number information is used for reflecting lithology of a unit stratum; determining a target Markov index of the target stratum according to the number information of each unit stratum corresponding to the target stratum from bottom to top; determining a first preset number of candidate stratums; determining candidate Markov indexes of the candidate stratums according to the number information of the unit stratums corresponding to the candidate stratums from bottom to top; and 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 or not according to the target similarity. According to the technical scheme, accurate analysis of the stratum vertical lithofacies superposition rule is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of sedimentary geology, and in particular to a method, device, equipment and medium for analyzing vertical lithofacies superposition rules of strata. Background Art

[0002] Many current sedimentology and sequence stratigraphy foundations are based on traditional qualitative analysis and interpretation, and on this basis, the development laws and formation patterns of strata are found. In the vertical direction, the law of strata can be defined as a specific arrangement of lithofacies or unit stratigraphic thickness. It must have a discernible upward trend or superposition pattern and is unlikely to occur by chance. Therefore, it requires some specific geological processes to act systematically to form. Traditional stratigraphic interpretation based on sedimentary cycles is often completely qualitative, so it is relatively subjective and has considerable uncertainty. Sometimes the understanding and interpretation of laws is just an implicit assumption of the sequence stratigraphic model, which brings difficulties and uncertainties to our construction of basin sedimentary stratigraphic framework, testing of existing geological understanding, and analysis of underground oil and gas reservoirs. Summary of the invention

[0003] The present invention provides a method, device, equipment and medium for analyzing the vertical lithofacies superposition law of a stratum, so as to realize accurate analysis of the vertical lithofacies superposition law of a stratum.

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

[0005] Obtaining logging data of a target formation, and dividing the target formation into a plurality of unit formations according to lithology data in the logging data; all the unit formations constitute the target formation, and each of the unit formations corresponds to a lithology; the lithology includes the particle size of rock particles in the formation;

[0006] Determine the numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme in which the corresponding strata are numbered 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] 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;

[0008] Determining 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 strata;

[0009] Determine a candidate Markov index of the candidate stratum according to the number information of each unit stratum corresponding to the candidate stratum from bottom to top;

[0010] The target similarity is determined based on the target Markov indicator and the candidate Markov indicator, and the target similarity reflects whether the lithology change of the target formation is a periodic change law; the target similarity is the similarity between the target formation and the candidate formation.

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

[0012] A first formation determination module is used to obtain logging data of a target formation, and divide the target formation into a plurality of 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 includes the particle size of rock particles in the formation;

[0013] An information determination module, used to determine the numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme in which the corresponding strata are numbered 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] A first indicator determination module is used to determine a target Markov indicator of the target stratum according to the number information of each unit stratum corresponding to the target stratum from bottom to top;

[0015] A second stratum determination module is used to 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 stratum;

[0016] A second indicator determination module is used to determine the candidate Markov indicator of the candidate stratum according to the number information of each unit stratum corresponding to the candidate stratum from bottom to top;

[0017] The analysis module is used to determine the target similarity based on the target Markov indicator and the candidate Markov indicator, and reflect whether the lithology change of the target formation is a periodic change law according to the target similarity; the target similarity is the similarity between the target formation and the candidate formation.

[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, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for analyzing the vertical lithofacies superposition rules of formations described in any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for analyzing the vertical lithofacies superposition rules of formations described in any embodiment of the present invention when executed.

[0023] The technical solution of the embodiment of the present invention obtains the logging data of the target stratum, divides the target stratum into a plurality of unit strata according to the lithology data in the logging data; all the unit strata constitute the target stratum, and each unit stratum corresponds to a lithology; the lithology includes the particle size of the rock particles in the stratum; further determines the numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme of setting the numbering information from small to large for the corresponding strata according to the lithology of the stratum from fine to coarse, and the numbering information is used to reflect the lithology of the unit stratum; thereby facilitating the determination of the 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; in order to enhance the data noise capability, further determines the first preset number of candidate strata Select 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; then, according to 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; finally, determine the target similarity based on the target Markov index and the candidate Markov index, and reflect whether the lithology change of the target stratum is a periodic change law according to the target similarity; the target similarity is the similarity between the target stratum and the candidate stratum, that is, this scheme switches from qualitative analysis to quantitative analysis by comparing the target Markov index with the candidate Markov index. The principle is simple and easy to use, and the results are objective, reproducible and easy to understand, so as to realize accurate and rapid analysis of the vertical lithofacies superposition law of the strata.

[0024] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 is a flow chart of a method for analyzing vertical lithofacies superposition rules of strata provided according to an embodiment of the present invention;

[0027] Figure 2 is a schematic diagram of correspondence between numbering information applicable to an embodiment of the present invention and a target stratum;

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

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

[0030] Figure 5 is an example diagram of a target Markov indicator and a probability density function applicable to an embodiment of the present invention;

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

[0032] Figure 7 is a moving-expanding window analysis result of a certain structure H5 section of a reference area depression to which an embodiment of the present invention is applicable;

[0033] Figure 8 It is a schematic structural diagram of a device for analyzing vertical lithofacies superposition rules of strata provided according to an embodiment of the present invention;

[0034] Fig. 9 It is a schematic diagram of the structure of an electronic device for implementing the method for analyzing the vertical lithofacies superposition law of strata according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] Embodiment 1

[0038] Figure 1 The flowchart of a method for analyzing the vertical lithofacies superposition law of strata provided in an embodiment of the present invention is applicable to the case of analyzing the vertical lithofacies superposition law of strata. The method can be executed by a device for analyzing the vertical lithofacies superposition law of strata. The device for analyzing the vertical lithofacies superposition law of strata can be implemented in the form of hardware and / or software. The device for analyzing the vertical lithofacies superposition law of strata can be configured in any electronic device with network communication function. Figure 1 As shown, the method for analyzing the vertical lithofacies superposition law of the stratum of the present invention includes the following process:

[0039] S110, acquiring logging data of a target formation, and dividing the target formation into a plurality of unit formations according to lithology data in the logging data; each unit formation corresponds to a lithology, and the lithology data includes the particle size of rock particles in the formation.

[0040] Among them, the lithology data is mainly controlled by the particle size of rock particles and reflects the characteristics of the sedimentary environment.

[0041] Specifically, the target stratum is a stratum with a certain stratum thickness. The lithology of strata with a certain thickness in the target stratum is consistent, and the stratum corresponding to the thickness is a unit stratum. Therefore, the logging data of the target stratum is obtained, and the lithology data in the logging data is analyzed, so that the target stratum can be divided into multiple unit strata from bottom to top. All unit strata are connected in sequence according to the corresponding positions of the strata to form the target stratum.

[0042] S120, determining the numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme in which numbering information is set from small to large for corresponding strata 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, the preset numbering scheme can be determined according to the characteristics of the sedimentary environment. Generally, the particle size of sediment increases as the hydrodynamic force becomes stronger, so the lithology numbers increase from mudstone to sandstone to conglomerate; that is, the preset numbering scheme can be understood as setting a numbering information for each lithology, and the coarser the rock particle size corresponding to the lithology, the larger the corresponding numbering information. Figure 3 An example of a preset numbering scheme is shown. The rock particle sizes of mudstone, mudstone, siltstone, sandstone and conglomerate are increasingly coarse, and the corresponding numbering information is 1, 2, 3, 4 and 5.

[0044] S130 , 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.

[0045] Among them, the Markov index can be understood as the index information reflecting the superposition law of the target stratum lithology.

[0046] Specifically, the target stratum from bottom to top refers to the geographical location corresponding to the target stratum from bottom to top. The combination of the numbering information of each unit stratum corresponding to the target stratum from bottom to top has an associated relationship with the Markov indicator, and the target Markov indicator of the target stratum can be accurately determined according to the associated relationship and the numbering information of each unit stratum corresponding to the target stratum from bottom to top. The associated relationship can be a relationship that describes the scheme by which the numbering information of each unit stratum corresponding to the target stratum from bottom to top is obtained.

[0047] In this embodiment, optionally, the target Markov indicator of the target formation is determined according to the numbering information of each unit formation corresponding to the target formation from bottom to top, including: determining the 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 indicator of the target formation according to the Markov probability matrix.

[0048] Specifically, the lithology change information may be the transformation of the numbering information of adjacent unit strata, for example Figure 2 The target stratum shown in the figure shows the numbering information of each unit stratum, so that from bottom to top, it is possible to 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] The lithology change information can be further converted into a Markov probability matrix. For example, Figure 2 Taking the target stratum as an example, Figure 3 As an example of the preset numbering scheme, the Markov probability matrix can be as follows Figure 4 Matrix information shown.

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

[0051] Where F is the total number of rows in 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 and maximum values ​​in the sequence; diag is a function for finding all the elements in the diagonal line j displacements away from the main diagonal line in the Markov probability matrix; ∑diag(T j ) and ∑diag(T -(F-j) ) is the sum of the corresponding diagonal element values ​​in the Markov probability matrix.

[0052] The technical solution of this embodiment determines the lithology change information of the target stratum from bottom to top according to the number information of each unit stratum; converts the lithology change information into a Markov probability matrix, and determines the target Markov index of the target stratum according to the Markov probability matrix, and accurately calculates m=max in a quantitative manner. diag -min diag ;

[0053]

[0054] The target Markov index is accurately determined.

[0055] S140, determining 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, there is randomness in the target Markov indicator analysis of the vertical lithology superposition law of the target stratum determined separately. Therefore, a second preset number of unit strata with different lithologies are selected from the target stratum, and the operation of selecting the second preset number of unit strata with different lithologies is performed for the first preset number of times, and the operation of exchanging the positions of the second preset number of unit strata with different lithologies in the target stratum is performed, so as to obtain the first preset number of candidate strata, thereby ensuring data diversity.

[0057] S150, determining candidate Markov indicators of the candidate strata according to the numbering information of each unit stratum corresponding to the candidate strata from bottom to top.

[0058] Specifically, in S120, the numbering information of each unit stratum has been confirmed, and then the candidate Markov index of each candidate stratum can be accurately determined according to 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, it includes: determining the candidate lithology change information of the candidate strata from bottom to top according to the numbering information of each unit stratum; converting the candidate lithology change information into a candidate Markov probability matrix, and determining the candidate Markov indicator of the candidate stratum according to the candidate Markov probability matrix.

[0060] Among them, the candidate Markov index m of the candidate stratum can be determined by the following formula:

[0061] m=max diag -min diag ;

[0062]

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

[0064] S160, determining target similarity based on the target Markov index and the candidate Markov index, and reflecting whether the lithology change of the target formation is a periodic change law based on the target similarity; the target similarity is the similarity between the target formation and the candidate formation.

[0065] Among them, the 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 according to all candidate Markov indicators; the probability density function is used to reflect the probability of different candidate Markov indicators appearing in all candidate Markov indicators; and the target similarity is further determined according to the target Markov indicator and the probability density function.

[0067] Accordingly, the target similarity is determined according to the target Markov indicator and the 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 and the horizontal axis on the right side of the target Markov indicator; and taking the ratio of the reference area to the target area as the target similarity. For example, Figure 5The example diagram of the target Markov indicator and probability density function shown in the figure, the red line is the target Markov indicator, the blue border is the probability density function, and the blue coverage area is 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 on the right side of the target Markov indicator and the horizontal axis is zero, then the target similarity is zero.

[0068] Furthermore, the target similarity value is in the range of 0 to 1, and the target similarity reflects whether the lithology change of the target stratum is a periodic change law, including: when the target similarity is less than the preset similarity, it is determined that the lithology change of the target stratum is a periodic change law; when the target similarity is greater than the preset similarity, it is determined that the lithology change of the target stratum is not a periodic change law. The preset similarity can be 0, or a value close to 0.

[0069] Optionally, the Markov index of the method, the number information of each unit stratum, the Markov probability matrix and the probability density function can be displayed in the display interface, such as Figure 2 , Figure 4 , Figure 5 Furthermore, because the information can be displayed in the display interface, the thickness of the target formation can be adjusted by moving or expanding the analysis window, so that the sensitivity analysis of the method can be systematically performed, and the data information can be observed intuitively.

[0070] The technical solution of the embodiment of the present invention obtains the logging data of the target stratum, divides the target stratum into a plurality of unit strata according to the lithology data in the logging data; all the unit strata constitute the target stratum, and each unit stratum corresponds to a lithology; the lithology includes the particle size of the rock particles in the stratum; further determines the numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme of setting the numbering information from small to large for the corresponding strata according to the lithology of the stratum from fine to coarse, and the numbering information is used to reflect the lithology of the unit stratum; thereby facilitating the determination of the 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; in order to enhance the data noise capability, further determines the first preset number of candidate strata Select 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; then, according to 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; finally, determine the target similarity based on the target Markov index and the candidate Markov index, and reflect whether the lithology change of the target stratum is a periodic change law according to the target similarity; the target similarity is the similarity between the target stratum and the candidate stratum, that is, this scheme switches from qualitative analysis to quantitative analysis by comparing the target Markov index with the candidate Markov index. The principle is simple and easy to use, and the results are objective, reproducible and easy to understand, so as to realize accurate and rapid analysis of the vertical lithofacies superposition law of the strata.

[0071] Embodiment 2

[0072] In order to further verify the vertical lithofacies stacking law analysis method, the vertical lithofacies stacking law analysis method was applied to six different target formations, each of which consisted of 50 unit formations, such as Figure 6 As shown. Among them, target strata 1 and target strata 2 have obvious periodic variation patterns, target strata 5 and target strata 6 are composed of random numbers generated by computers and have different lithology distribution frequencies, and target strata 3 and target strata 4 are dominated by two lithologies, but target stratum 3 has a regular arrangement, while target stratum 4 has no regularity. The experiment was repeated 50 times and a 95% confidence interval was constructed. The results showed that the vertical lithofacies superposition law analysis method of strata has a good degree of discrimination. Even if the strata are disrupted, the differences between the P value curves are still obvious, until about 15 times, that is, 30 unit strata are disrupted, the curves begin to overlap and become difficult to distinguish.

[0073] This shows that the analysis method of the vertical lithofacies superposition law of strata has a strong ability to resist data noise, and can effectively discover laws that are not easily perceived by the naked eye but are supported by mathematics and statistics from the stratigraphic superposition pattern. This is of great significance for clarifying how strata with periodic characteristics are controlled by potential factors, and then understanding the inheritance, periodicity and diversity of sedimentary systems in spatial distribution.

[0074] In addition, if Figure 7 As shown, the vertical lithofacies superposition law analysis method of the present invention is applied to the same layer (H5) of two different wells 3 km apart in a certain structure in the reference area depression, the target stratum is adjusted by moving-expanding the window, and the target similarity is determined according to the vertical lithofacies superposition law analysis method of the present invention, so as to reflect whether the lithology changes of each target stratum follow a periodic change law according to the target similarity.

[0075] from Figure 7 It can be seen that delta plain sedimentary facies with different vertical lithology superposition patterns can be identified and distinguished. The two wells have similar characteristics (lithology, sand-to-sand ratio), both of which have a thick sand body at the bottom, rich in mud, and occasionally sandstone or siltstone. The planar seismic attributes and sedimentary facies maps indicate that both are delta plain channels separated by mudstones in the inter-distributary bays, but it is difficult to distinguish them.

[0076] Figure 7 The results show that the two reference areas have very different Pm distribution patterns: the overall Pm value of the X-1 well is low, indicating that the formation has a strong periodic law, and Pm decreases with the increase of the window size. In contrast, the overall Pm value of the adjacent X-4 well is high, and Pm decreases first and then increases with the window size, which has a completely different trend from the X-1 well. The results strongly reveal that the two are different river channels, and therefore have different plane phase belts and sedimentary histories, which is consistent with the post-drilling results. That is, an accurate analysis of the vertical lithofacies superposition law of the formation is achieved.

[0077] Embodiment 3

[0078] Figure 8 The schematic diagram of the structure of a device for analyzing the vertical lithofacies superposition law of strata provided in an embodiment of the present invention is applicable to the case of analyzing the vertical lithofacies superposition law of strata. The device for analyzing the vertical lithofacies superposition law of strata can be implemented in the form of hardware and / or software. The device for analyzing the vertical lithofacies superposition law of strata can be configured in any electronic device with network communication function. Figure 8 As shown, the stratum vertical lithofacies superposition law analysis device comprises:

[0079] The first formation determination module 210 is used to obtain logging data of a target formation, and divide the target formation into a plurality of 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 includes the particle size of rock particles in the formation;

[0080] An 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 in which the corresponding strata are numbered 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] A first indicator determination module 230 is used to determine a target Markov indicator of the target stratum according to the number information of each unit stratum corresponding to the target stratum from bottom to top;

[0082] The second stratum determination module 240 is used to 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 stratum;

[0083] A second indicator determination module 250 is used to determine a candidate Markov indicator of the candidate stratum according to the number information of each unit stratum corresponding to the candidate stratum from bottom to top;

[0084] The analysis module 260 is used to determine the target similarity based on the target Markov indicator and the candidate Markov indicator, and reflect whether the lithology change of the target formation is a periodic change law according to the target similarity; the target similarity is the similarity between the target formation and the candidate formation.

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

[0086] Based on the above embodiment, optionally, the target Markov index m of the target formation is determined by the following formula:

[0087] m=max diag -min diag ;

[0088]

[0089] Wherein, 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 and maximum values ​​from the sequence; diag is a function for finding all the elements in the diagonal lines j displacements away from the main diagonal line from the Markov probability matrix; ∑diag(T j ) and ∑diag(T -(F-j)) is the sum of the corresponding diagonal element values ​​in the Markov probability matrix.

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

[0091] A function determination unit, used for constructing a probability density function according to all the candidate Markov indicators; the probability density function is used for reflecting the probability of different candidate Markov indicators appearing in all the candidate Markov indicators;

[0092] The similarity determination unit is used to determine the target similarity according to the target Markov index and the probability density function.

[0093] Based on the above embodiment, optionally, the similarity determination unit is also used to: determine a target area enclosed by the probability density function and the horizontal axis; determine a reference area enclosed by the probability density function and the horizontal axis on the right side of the target Markov indicator; and use the ratio of the reference area to the target area as the target similarity.

[0094] On the basis of the above embodiment, optionally, the value of the target similarity is in the range of 0 to 1, and the analysis module includes a rule analysis unit, which is used to determine that the lithology change of the target formation is a periodic change rule when the target similarity is less than a preset similarity; when the target similarity is greater than a preset similarity, it is determined that the lithology change of the target formation is not a periodic change rule.

[0095] On the basis of the above embodiment, optionally, a second indicator determination module is used to determine the candidate lithology change information of the candidate strata from bottom to top according to the numbering information of each unit strata; convert the candidate lithology change information into a candidate Markov probability matrix, and determine the candidate Markov indicator of the candidate strata according to the candidate Markov probability matrix.

[0096] The device for analyzing the laws of vertical lithofacies superposition of strata provided in the embodiment of the present invention can execute the method for analyzing the laws of vertical lithofacies superposition of strata provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0097] Embodiment 4

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

[0099] Fig. 9A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0100] like Fig. 9 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

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

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

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

[0104] Various implementations 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 chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

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

[0107] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0108] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0109] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0110] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0111] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing the vertical lithofacies superposition law of a stratum, characterized in that: The method comprises: Obtaining logging data of a target formation, and dividing the target formation into a plurality of 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 includes the particle size of rock particles in the formation; Determine the numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme in which the corresponding strata are numbered 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; 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; 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 strata; Determine a candidate Markov index of the candidate stratum according to the number information of each unit stratum corresponding to the candidate stratum from bottom to top; The target similarity is determined based on the target Markov indicator and the candidate Markov indicator, and the target similarity reflects whether the lithology change of the target formation is a periodic change law; the target similarity is the similarity between the target formation and the candidate formation.

2. The method according to claim 1, characterized in that: 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 includes: According to the number information of each unit stratum, determining the lithology change information of the target stratum from bottom to top; The lithology change information is converted into a Markov probability matrix, and a target Markov index of the target formation is determined according to the Markov probability matrix.

3. The method according to claim 2, characterized in that Determining a target Markov index of the target formation according to the Markov probability matrix includes: The target Markov index m of the target formation is determined by the following formula: m=max diag -min diag ; Wherein, 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 and maximum values ​​from the sequence; diag is a function for finding all the elements in the diagonal lines j displacements away from the main diagonal line from the Markov probability matrix; ∑diag(T j ) and ∑diag(T -(F-j) ) is the sum of the corresponding diagonal element values ​​in the Markov probability matrix.

4. The method according to claim 1, characterized in that: Determining target similarity according to the target Markov indicator and the candidate Markov indicator includes: Constructing a probability density function according to all the candidate Markov indicators; the probability density function is used to reflect the probability of different candidate Markov indicators appearing in all the candidate Markov indicators; The target similarity is determined according to the target Markov index and the probability density function.

5. The method according to claim 4, characterized in that Determining target similarity according to the target Markov index and the probability density function includes: Determine a target area enclosed by the probability density function and the horizontal axis; Determine a reference area enclosed by the probability density function and the horizontal axis on the right side of the target Markov indicator; The ratio of the reference area to the target area is taken as the target similarity.

6. The method according to claim 1 or 4, characterized in that: The target similarity value is in the range of 0 to 1, and the target similarity reflects whether the lithology change of the target formation is a periodic change law, including: 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 law; When the target similarity is greater than a preset similarity, it is determined that the lithology change of the target formation is not a periodic change pattern.

7. The method according to claim 1, characterized in that The numbering information of each unit stratum corresponding to the candidate stratum from bottom to top includes: Determine candidate lithology change information of the candidate strata from bottom to top according to the number information of each unit stratum; The candidate lithology change information is converted into a candidate Markov probability matrix, and a candidate Markov index of the candidate formation is determined according to the candidate Markov probability matrix.

8. A device for analyzing the vertical lithofacies superposition law of strata, characterized in that: The device comprises: A first formation determination module is used to obtain logging data of a target formation, and divide the target formation into a plurality of 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 includes the particle size of rock particles in the formation; An information determination module, used to determine the numbering information of each unit stratum according to a preset numbering scheme; the preset numbering scheme is a scheme in which the corresponding strata are numbered 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; A first indicator determination module is used to determine a target Markov indicator of the target stratum according to the number information of each unit stratum corresponding to the target stratum from bottom to top; A second stratum determination module is used to 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 stratum; A second indicator determination module is used to determine the candidate Markov indicator of the candidate stratum according to the number information of each unit stratum corresponding to the candidate stratum from bottom to top; The analysis module is used to determine the target similarity based on the target Markov indicator and the candidate Markov indicator, and reflect whether the lithology change of the target formation is a periodic change law according to the target similarity; the target similarity is the similarity between the target formation and the candidate formation.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to 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 so that the at least one processor can execute the method for analyzing the vertical lithofacies superposition rules of formations as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for analyzing the vertical lithofacies superposition rules of formations according to any one of claims 1 to 7 when executing the computer instructions.

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