Full-automatic logging curve form comparison method and device

Abstract

The invention discloses a full-automatic logging curve form comparison method and device. The method comprises the following steps: extracting a logging curve section of a target interval of a to-be-compared well and a standard well in a target well area; constructing a cumulative distance matrix between sampling points of the standard well logging curve section and the to-be-compared well logging curve section; searching a shortest path between an upper left corner element and a lower right corner element in the cumulative distance matrix, and establishing a one-to-one mapping relation of sampling points of two logging curve sections; the sum of the distances between the two sampling points with the mapping relation serves as the distance between the two logging curve sections; and carrying out normalization processing on the obtained distance, and obtaining a similarity plane distribution diagram of the target interval of the target well region through interpolation. According to the method, based on logging curve characteristics, similarity comparison is automatically carried out on curves between wells, the similarity between the wells is quantitatively analyzed, and a reference basis is provided for sedimentary facies division; the method is also suitable for areas with large deposition changes.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas geophysical exploration and well logging technology, and in particular to a fully automatic logging curve morphology comparison method and device. Background Art

[0002] Well logs record the changes in formation physical properties with well depth and are a comprehensive reflection of the various physical properties of underground geological bodies. They can well reflect the changes in environmental energy during sediment deposition, providing valuable data for identifying sedimentary facies and studying underground material storage. Sequences deposited under different hydrodynamic conditions exhibit differences in sorting, grain size, and mud content, resulting in different log morphological characteristics. The morphology and changing relationships of different log curves reflect different sedimentary environments, serving as indicators of sedimentary facies and a key indicator for sequence stratigraphic division and identification.

[0003] Currently, well logging data is mainly used to evaluate curve morphology. There are mainly the following methods:

[0004] Lu Jingan (1996) used fractal theory to characterize the morphology of different logging curves in a well after stretching the logging curves; Wang Nan (2008) used cluster analysis to compare the curve morphology and constructed five indicators reflecting the digital characteristics of the logging curve morphology, including median, arithmetic mean, relative center of gravity, relative sawtooth number, and variance variation number, using mathematical geological methods; Gao Xing

[0005] (2013) applied Gaussian wavelet transform to extract key feature points of logging curves based on the characteristics of logging curves, and proposed an algorithm for determining the similarity of logging curves using discrete Fréchet distance through a variety of improvement measures.

[0006] Currently, the most commonly used methods for similarity determination are similarity function determination and eigenvalue method. The similarity function determination method relies on curve fitting data processing to approximate a curve composed of discrete points with a continuous curve, forming a function that fits the curve. The similarity of the curves is then calculated using the similarity definition rule. The eigenvalue method is relatively widely used and mainly extracts the multidimensional key characteristic parameters of the curve and then analyzes and studies these characteristic parameters using relevant artificial intelligence theories. Summary of the Invention

[0007] The inventors discovered that both the similarity function method and the eigenvalue method, commonly used to determine the similarity of well logging curves, have certain flaws. The similarity function method suffers from large errors during the curve fitting process for complex curves like well logging curves, leading to suboptimal final results. The eigenvalue method typically has high algorithmic complexity and poor stability. Furthermore, these methods require manual intervention and are only suitable for areas with relatively stable lateral sedimentation. They are difficult to implement in areas with rapid lateral variations in formation thickness.

[0008] In order to at least partially solve the technical problems existing in the prior art, the inventors have made the present invention. Through specific implementation methods, they provide a fully automatic logging curve morphology comparison method and device, which directly compares the similarity of curves between wells based on the morphological characteristics of the logging curves and quantitatively analyzes the similarity between wells.

[0009] In a first aspect, an embodiment of the present invention provides a fully automatic logging curve morphology comparison method, comprising:

[0010] Constructing a cumulative distance matrix between sampling points of the standard well logging curve segment and the well logging curve segment to be compared;

[0011] Finding the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix, and establishing a one-to-one mapping relationship between the sampling points of the two logging curve segments;

[0012] The sum of the distances between the two sampling points having a mapping relationship is used as the distance between the two well logging curve segments, so as to characterize the similarity between the two well logging curve segments.

[0013] In some embodiments, constructing a cumulative distance matrix between sampling points of the standard well logging curve segment and the well logging curve segment to be compared includes:

[0014] The distance between the first sampling point of the well logging curve segment of the standard well and the first sampling point of the well logging curve segment to be compared is used as the upper left corner element of the cumulative distance matrix between the sampling points of the well logging curve segment of the standard well and the well logging curve segment to be compared;

[0015] According to formula (1), the other elements of the first column in the cumulative distance matrix are determined in order from top to bottom:

[0016] D[i,0]=dis(A i ,B0)+D[i-1,0] (1);

[0017] According to formula (2), the other elements of the first row in the cumulative distance matrix are determined in order from left to right:

[0018] D[0,j]=dis(A0,B j)+D[0,j-1] (2);

[0019] According to formula (3), determine the other elements outside the first row and the first column in the cumulative distance matrix:

[0020] D[i,j]=dis(A i ,B j )+min(D[i-1,j],D[i,j-1],D[i-1,j-1]) (3);

[0021] In formulas (1)-(3), D[i,j] represents the cumulative distance between the i-th sampling point of the standard well logging curve segment and the j-th sampling point of the well logging curve segment to be compared, dis(A i ,B j ) represents the i-th sampling point A of the standard well logging curve segment i The jth sampling point B of the well logging curve segment to be compared j The distance between.

[0022] In some embodiments, finding the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix includes:

[0023] Starting from the lower right corner element in the cumulative distance matrix, find the element with the smallest value among the three elements to the upper left of the current element in turn, and find the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix by backtracking.

[0024] In some embodiments, the distance between two sampling points refers to the absolute value of the difference between the well logging values ​​of the two sampling points.

[0025] In a second aspect, an embodiment of the present invention provides another fully automatic logging curve morphology comparison method, comprising:

[0026] Extract the logging curve segments of the target layer sections of the wells to be compared and the standard wells in the target well area;

[0027] According to any of the above methods, determining the distance between the well logging curve segment of the well to be compared and the well logging curve segment of the reference well;

[0028] The obtained distances are normalized, and a similarity plane distribution map of the target layer section in the target well area is obtained by interpolation.

[0029] In some embodiments, the standard wells are screened by:

[0030] Screening wells whose core sampling data of target layer intervals in the target well area meet the set requirements;

[0031] Based on the principle of high fidelity of logging curve data and as many data points as possible in the target layer, standard wells are selected from the screened wells.

[0032] In some embodiments, the interpolation comprises Kriging interpolation.

[0033] In a third aspect, an embodiment of the present invention provides a fully automatic logging curve morphology comparison device, comprising:

[0034] A cumulative distance matrix construction module is used to construct a cumulative distance matrix between sampling points of the standard well logging curve segment and the well logging curve segment to be compared;

[0035] A sampling point mapping relationship establishment module is used to find the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix, and establish a one-to-one mapping relationship between the sampling points of the two logging curve segments;

[0036] The distance determination module is used to use the sum of the distances between two sampling points having a mapping relationship as the distance between the two well logging curve segments, so as to characterize the similarity between the two well logging curve segments.

[0037] In a fourth aspect, an embodiment of the present invention provides another fully automatic logging curve morphology comparison device, comprising:

[0038] The logging curve segment extraction module is used to extract the logging curve segments of the target layer segments of the wells to be compared and the standard wells in the target well area;

[0039] a distance determination module, configured to determine the distance between the well logging curve segment of the well to be compared and the well logging curve segment of the reference well according to any of the fully automatic well logging curve morphology comparison methods described in the first aspect;

[0040] The similarity analysis module is used to normalize the obtained distances and obtain a similarity plane distribution map of the target layer section in the target well area by interpolation.

[0041] In a fifth aspect, an embodiment of the present invention provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned fully automatic logging curve morphology comparison method is implemented.

[0042] In a sixth aspect, an embodiment of the present disclosure provides a server comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned fully automatic logging curve morphology comparison method when executing the program.

[0043] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0044] (1) The fully automatic logging curve morphology comparison method provided by the embodiment of the present invention, when comparing the similarity of logging curves with different numbers of sampling points, establishes a one-to-one mapping relationship between the curve comparison points of different wells through cumulative distance matrix construction and shortest path optimization, and sums the distances between them to characterize the similarity of the curve morphology. This method is driven by logging data, has few intermediate processes, avoids the influence of human factors, and has a higher reliability in curve similarity evaluation; it automatically compares the similarity of curve morphology between wells, quantitatively analyzes the similarity between wells, and provides a reference basis for the division of sedimentary phases; it is also applicable to areas with relatively large sedimentary changes.

[0045] (2) The fully automatic logging curve morphology comparison method provided by the embodiment of the present invention automatically compares the curve characteristics between different wells based on the extraction of the target layer curve, calculates the correlation of the logging curve morphology using a non-point-to-point distance calculation method, and obtains the lateral similarity feature distribution through interpolation on this basis.

[0046] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0047] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 This is a flow chart of the fully automatic logging curve morphology comparison method in Example 1 of the present invention;

[0050] Figure 2 Schematic diagram of the cumulative distance matrix and the shortest path in the first embodiment of the present invention;

[0051] Figure 3 This is a one-to-one mapping relationship diagram of the comparison points of the logging curves of the two wells in Example 1 of the present invention;

[0052] Figure 4 This is a flow chart of the fully automatic logging curve morphology comparison method in Example 2 of the present invention;

[0053] Figure 5 This is a plane distribution diagram of the similarity of the well logging curve morphology in the second embodiment of the present invention;

[0054] Figure 6Schematic diagram of the structure of a fully automatic logging curve morphology comparison device in an embodiment of the present invention;

[0055] Figure 7 Schematic diagram of the structure of another fully automatic logging curve morphology comparison device in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0057] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each smaller range between any intermediate value within a stated value or stated range and any other stated value or intermediate value within the stated range is also encompassed by the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0058] Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the invention belongs. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

[0059] Example 1

[0060] The first embodiment of the present invention provides a fully automatic logging curve morphology comparison method. Since the previous point-to-point Euclidean distance cannot effectively compare the similarity of logging curve segments of different lengths, this embodiment uses an algorithm to find the best comparison path. The process is as follows: Figure 1 As shown, the following steps are included:

[0061] Step S11: constructing a cumulative distance matrix between sampling points of the standard well logging curve segment and the well logging curve segment to be compared.

[0062] First, construct an N*M empty matrix, where the columns correspond to the standard well logging curve segments, denoted as curve segments A, and the rows correspond to the well logging curve segments to be compared, denoted as curve segments B. The number of sampling points in curve segment A is N, and the sampling point sequence is i. The number of sampling points in curve segment B is M, and the sampling point sequence is j. Each element in the grid represents the cumulative distance between the corresponding points.

[0063] The specific process of establishing the cumulative distance matrix is ​​as follows:

[0064] The distance between the first sampling point of the standard well logging curve segment and the first sampling point of the well logging curve segment to be compared is used as the upper left corner element of the cumulative distance matrix between the sampling points of the standard well logging curve segment and the well logging curve segment to be compared;

[0065] According to formula (1), the other elements of the first column in the cumulative distance matrix are determined in order from top to bottom:

[0066] D[i,0]=dis(A i ,B0)+D[i-1,0] (1);

[0067] According to formula (2), the other elements of the first row in the cumulative distance matrix are determined in order from left to right:

[0068] D[0,j]=dis(A0,B j )+D[0,j-1] (2);

[0069] According to formula (3), determine the other elements outside the first row and the first column in the cumulative distance matrix:

[0070] D[i,j]=dis(A i ,B j )+min(D[i-1,j],D[i,j-1],D[i-1,j-1]) (3);

[0071] In formulas (1)-(3), D[i,j] represents the cumulative distance between the i-th sampling point of the standard well logging curve segment and the j-th sampling point of the well logging curve segment to be compared, that is, the element in the i-th row and j-th column of the cumulative distance matrix, dis(A i ,B j ) represents the i-th sampling point A of the standard well logging curve segment i The jth sampling point B of the well logging curve segment to be compared j The distance between two sampling points refers to the absolute value of the difference between the logging values ​​of the two sampling points.

[0072] That is, starting from the upper left corner, then starting from the first column from top to bottom, in addition to calculating the distance of the corresponding element, it is also necessary to add the cumulative distance of the adjacent elements to the left, thereby achieving distance accumulation. The calculation formula is shown in formula (1). Similarly, from left to right, in addition to calculating the distance of the corresponding element in the top row, it is also necessary to add the cumulative distance of the adjacent elements to the left. The calculation formula is shown in formula (2). For the remaining elements, in addition to calculating the distance of the corresponding element, it is also necessary to find the minimum value of the three cumulative distances in the upper left (a total of three elements in the left, top, and upper left) and add them together. The calculation formula is shown in formula (3).

[0073] Optionally, you can also first construct an M*N empty matrix. The specific process of establishing the cumulative distance matrix is ​​similar and will not be repeated here.

[0074] Step S12: Find the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix, and establish a one-to-one mapping relationship between the sampling points of the two logging curve segments.

[0075] Specifically, starting from the lower right element in the cumulative distance matrix, the element with the smallest value among the three elements to the upper left of the current element can be found in sequence, and the shortest path between the upper left element and the lower right element in the cumulative distance matrix can be found by backtracking.

[0076] See also Figure 2 As shown in the figure, the cumulative distance matrix and the shortest path are established. The color represents the size of the cumulative distance corresponding to each element, and the line in the matrix is ​​the shortest path found.

[0077] Step S13: taking the sum of the distances between the two sampling points having a mapping relationship as the distance between the two well logging curve segments, so as to characterize the similarity between the two well logging curve segments.

[0078] The distance between logging curve segments represents the similarity between two logging curve segments. The smaller the distance, the more similar the waveforms are; conversely, the larger the distance, the greater the difference in curve morphology.

[0079] On the basis of step S12, it is equivalent to obtaining a one-to-one mapping relationship between the logging curve morphology comparison points of the two wells, see Figure 3 As shown, the 0th sampling point of well A is compared with the 0th to 10th sampling points of waveform B, and the distances between the two mapped points are summed to obtain the final distance sum D.

[0080] The fully automated well logging curve morphology comparison method provided in Example 1 of the present invention compares the similarity of well logging curves with varying numbers of sampling points. This method establishes a one-to-one mapping relationship between the curve comparison points of different wells through cumulative distance matrix construction and shortest path optimization. The distances between these points are then summed to characterize the similarity of the curve morphology. This method is driven by well logging data, has minimal intermediate steps, avoids the influence of human factors, and achieves higher reliability of curve similarity. It automatically compares the similarity of curve morphology between wells, quantitatively analyzes the similarity between wells, and provides a reference for sedimentary facies classification. The method is also applicable to areas with significant sedimentary variation.

[0081] Example 2

[0082] The second embodiment of the present invention provides a fully automatic logging curve morphology comparison method, the process of which is as follows Figure 4 As shown, the following steps are included:

[0083] Step S41: extracting the well logging curve segments of the target layer segments of the wells to be compared and the standard wells in the target well area.

[0084] Standard wells are screened by the following methods:

[0085] (1) Screen the wells whose core sampling data of the target layer section in the target well area meet the set requirements.

[0086] Screen wells that have abundant data such as core sampling and thin sections in the target interval.

[0087] (2) Select standard wells from the selected wells based on the principle that the fidelity of the logging curve data of the target layer is as high as possible and the number of data points is as large as possible.

[0088] Step S42: Determine the distance between the well logging curve segment of the well to be compared and the well logging curve segment of the standard well.

[0089] The specific determination method can be based on the method in Example 1, which will not be described in detail here.

[0090] Step S43: normalize the obtained distances and obtain a similarity plane distribution map of the target layer segment in the target well area by interpolation.

[0091] In order to facilitate comparative analysis, the curve morphology similarity (distance) is normalized and the calculation formula is:

[0092]

[0093] In formula (4), D and C represent the distance before normalization and the distance after normalization respectively. max 、D min They are respectively the maximum and minimum values ​​of the distance between the target layer of the well to be compared and the standard well in the target well area.

[0094] Assign the normalized correlation coefficient (distance) corresponding to each well to the corresponding (x, y) point, and then interpolate to obtain the plane similarity distribution map, see Figure 5 As shown, the larger the coefficient c is, the greater the correlation with the standard well is, and vice versa.

[0095] Furthermore, the interpolation method may be a Kriging interpolation method.

[0096] The fully automatic logging curve morphology comparison method provided in Example 2 of the present invention automatically compares the curve characteristics between different wells based on the extraction of the target layer curve, uses a non-point-to-point distance calculation method to calculate the correlation of the logging curve morphology, and on this basis obtains the horizontal similarity feature distribution through interpolation.

[0097] Based on the inventive concept of the present invention, the embodiment of the present invention further provides a fully automatic logging curve morphology comparison device, the structure of which is as follows: Figure 6 Shown, including:

[0098] The cumulative distance matrix construction module 61 is used to construct a cumulative distance matrix between sampling points of the standard well logging curve segment and the well logging curve segment to be compared;

[0099] The sampling point mapping relationship establishment module 62 is used to find the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix, and establish a one-to-one mapping relationship between the sampling points of the two well logging curve segments;

[0100] The distance determination module 63 is configured to use the sum of the distances between two sampling points having a mapping relationship as the distance between the two well logging curve segments, so as to characterize the similarity between the two well logging curve segments.

[0101] In some embodiments, the cumulative distance matrix construction module 61 constructs the cumulative distance matrix between the sampling points of the standard well logging curve segment and the well logging curve segment to be compared, for:

[0102] The distance between the first sampling point of the well logging curve segment of the standard well and the first sampling point of the well logging curve segment to be compared is used as the upper left corner element of the cumulative distance matrix between the sampling points of the well logging curve segment of the standard well and the well logging curve segment to be compared;

[0103] According to formula (1), the other elements of the first column in the cumulative distance matrix are determined in order from top to bottom:

[0104] D[i,0]=dis(A i ,B0)+D[i-1,0] (1);

[0105] According to formula (2), the other elements of the first row in the cumulative distance matrix are determined in order from left to right:

[0106] D[0,j]=dis(A0,B j )+D[0,j-1] (2);

[0107] According to formula (3), determine the other elements outside the first row and the first column in the cumulative distance matrix:

[0108] D[i,j]=dis(A i ,B j )+min(D[i-1,j],D[i,j-1],D[i-1,j-1]) (3);

[0109] In formulas (1)-(3), D[i,j] represents the cumulative distance between the i-th sampling point of the standard well logging curve segment and the j-th sampling point of the well logging curve segment to be compared, dis(A i ,B j ) represents the i-th sampling point A of the standard well logging curve segment i The jth sampling point B of the well logging curve segment to be compared j The distance between.

[0110] In some embodiments, the sampling point mapping relationship establishing module 62 searches for the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix to:

[0111] Starting from the lower right corner element in the cumulative distance matrix, find the element with the smallest value among the three elements to the upper left of the current element in turn, and find the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix by backtracking.

[0112] Based on the inventive concept of the present invention, the embodiment of the present invention also provides another fully automatic logging curve morphology comparison device, the structure of which is as follows: Figure 7 Shown, including:

[0113] The logging curve segment extraction module 71 is used to extract the logging curve segments of the target layer segments of the wells to be compared and the standard wells in the target well area;

[0114] a distance determination module 72 for determining the distance between the well logging curve segment of the well to be compared and the well logging curve segment of the reference well according to any of the fully automatic well logging curve morphology comparison methods described in the first aspect;

[0115] The similarity analysis module 73 is used to normalize the obtained distances and obtain a similarity plane distribution map of the target layer section in the target well area by interpolation.

[0116] In some embodiments, the apparatus further includes a standard well screening module 74 for screening standard wells in the following manner:

[0117] Wells whose core sampling data of the target layer section in the target well area meet the set requirements are screened; standard wells are selected from the screened wells based on the principle that the fidelity of the logging curve data of the target layer section is as high as possible and the number of data points is as large as possible.

[0118] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0119] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned fully automatic logging curve morphology comparison method is implemented.

[0120] Based on the inventive concept of the present invention, an embodiment of the present invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the processor implements the above-mentioned fully automatic logging curve morphology comparison method when executing the program.

[0121] Unless otherwise specifically stated, terms such as process, calculate, compute, determine, display, and the like may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, that manipulate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within the memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0122] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0123] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0124] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0125] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in the user terminal as discrete components.

[0126] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0127] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A fully automatic logging curve morphology comparison method, characterized in that: include: Constructing a cumulative distance matrix between sampling points of the standard well logging curve segment and the well logging curve segment to be compared; Finding the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix, and establishing a one-to-one mapping relationship between the sampling points of the two logging curve segments; The sum of the distances between the two sampling points having a mapping relationship is used as the distance between the two well logging curve segments, so as to characterize the similarity between the two well logging curve segments.

2. The method according to claim 1, characterized in that The method of constructing a cumulative distance matrix between sampling points of the standard well logging curve segment and the well logging curve segment to be compared comprises: The distance between the first sampling point of the well logging curve segment of the standard well and the first sampling point of the well logging curve segment to be compared is used as the upper left corner element of the cumulative distance matrix between the sampling points of the well logging curve segment of the standard well and the well logging curve segment to be compared; According to formula (1), the other elements of the first column in the cumulative distance matrix are determined in order from top to bottom: D[i,0]=dis(A i ,B0)+D[i-1,0] (1); According to formula (2), the other elements of the first row in the cumulative distance matrix are determined in order from left to right: D[0,j]=dis(A0,B j )+D[0,j-1] (2); According to formula (3), determine the other elements outside the first row and the first column in the cumulative distance matrix: D[i,j]=dis(A i ,B j )+min(D[i-1,j],D[i,j-1],D[i-1,j-1]) (3); In formulas (1)-(3), D[i,j] represents the cumulative distance between the i-th sampling point of the standard well logging curve segment and the j-th sampling point of the well logging curve segment to be compared, dis(A i ,B j ) represents the i-th sampling point A of the standard well logging curve segment i The jth sampling point B of the well logging curve segment to be compared j The distance between.

3. The method according to claim 2, characterized in that The step of finding the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix includes: Starting from the lower right corner element in the cumulative distance matrix, find the element with the smallest value among the three elements to the upper left of the current element in turn, and find the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix by backtracking.

4. The method according to any one of claims 1 to 3, characterized in that: The distance between two sampling points refers to the absolute value of the difference in logging values ​​between the two sampling points.

5. A fully automatic logging curve morphology comparison method, characterized in that: include: Extract the logging curve segments of the target layer sections of the wells to be compared and the standard wells in the target well area; According to the method of any one of claims 1 to 4, determining the distance between the well logging curve segment of the well to be compared and the well logging curve segment of the reference well; The obtained distances are normalized, and a similarity plane distribution map of the target layer section in the target well area is obtained by interpolation.

6. The method according to claim 5, characterized in that The standard wells were screened by the following methods: Screening wells whose core sampling data of target layer intervals in the target well area meet the set requirements; Based on the principle of high fidelity of logging curve data and as many data points as possible in the target layer, standard wells are selected from the screened wells.

7. The method according to claim 5, characterized in that The interpolation includes Kriging interpolation.

8. A fully automatic logging curve morphology comparison device, characterized in that: The device comprises: A cumulative distance matrix construction module is used to construct a cumulative distance matrix between sampling points of the standard well logging curve segment and the well logging curve segment to be compared; A sampling point mapping relationship establishment module is used to find the shortest path between the upper left corner element and the lower right corner element in the cumulative distance matrix, and establish a one-to-one mapping relationship between the sampling points of the two logging curve segments; The distance determination module is used to use the sum of the distances between two sampling points having a mapping relationship as the distance between the two well logging curve segments, so as to characterize the similarity between the two well logging curve segments.

9. A fully automatic logging curve morphology comparison device, characterized in that: The device comprises: The logging curve segment extraction module is used to extract the logging curve segments of the target layer segments of the wells to be compared and the standard wells in the target well area; A distance determination module, configured to determine the distance between the well logging curve segment of the well to be compared and the well logging curve segment of the reference well according to the method described in any one of claims 1 to 4; The similarity analysis module is used to normalize the obtained distances and obtain a similarity plane distribution map of the target layer section in the target well area by interpolation.

10. A computer storage medium, characterized in that The computer storage medium stores computer executable instructions, which, when executed by a processor, implement the fully automatic logging curve morphology comparison method according to any one of claims 1 to 7.

11. A server, characterized in that: include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the program, the fully automatic logging curve morphology comparison method according to any one of claims 1 to 7 is implemented.

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