A formation comparison method, device, storage medium and electronic device

Abstract

The present application discloses a formation comparison method, device, storage medium and electronic device, which relate to the field of formation comparison technology, wherein the method comprises: performing calculation processing based on the first initial logging parameters corresponding to the different formation depths of the wells to be compared within the target formation comparison interval, and obtaining a first data point sequence of the different formation depths corresponding to the wells to be compared; performing calculation processing based on the second initial logging parameters corresponding to the different formation depths of the standard wells within the target formation comparison interval, and obtaining a second data point sequence of the different formation depths corresponding to the standard wells; performing formation comparison based on the first data point sequence and the second data point sequence using a preset dynamic time warping method, and obtaining a formation comparison result of the wells to be compared relative to the standard wells. The formation comparison method of the present application can save manpower and improve the efficiency of formation comparison.

Description

Technical Field

[0001] The present invention relates to the technical field of formation comparison, and in particular to a formation comparison method, device, storage medium and electronic equipment. Background Art

[0002] Stratigraphic correlation is usually carried out within an oil field to compare the entire well section or the target layer section. Through stratigraphic correlation, geologists can understand the geological process at the time and place, and based on this, find the corresponding oil and gas-bearing areas in combination with subsequent work. Stratigraphic correlation plays a fundamental role in the exploration and development of oil and gas fields. At present, stratigraphic correlation work is mostly done by geologists directly using natural gamma, natural potential and resistivity logging curves to make similarity / correlation judgments based on their own experience, that is, relying on geologists' eyes, hands and brains to conduct comprehensive analysis. However, manual comparison is highly subjective, labor-intensive and inefficient. Summary of the invention

[0003] In view of this, the present invention provides a formation comparison method, device, storage medium and electronic device, the main purpose of which is to solve the problem that the current formation comparison through manual comparison is highly subjective, labor-intensive and inefficient.

[0004] In order to solve the above problems, the present application provides a formation correlation method, comprising:

[0005] Calculating and processing the first initial well logging parameters corresponding to the different formation depths of the wells to be compared within the target formation comparison interval to obtain a first data point sequence of the different formation depths corresponding to the wells to be compared;

[0006] Calculating and processing the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well;

[0007] Based on the first data point sequence and the second data point sequence, a preset dynamic time warping method is used to perform formation comparison to obtain a formation comparison result of the well to be compared relative to the standard well;

[0008] Wherein, each first data point in the first data point sequence includes a first target apparent skeleton density, a first target apparent skeleton acoustic wave time difference and a first target gamma value in the first target well logging skeleton parameters;

[0009] Each second data point in the second data point sequence includes a second target apparent skeleton density, a second target apparent skeleton acoustic wave time difference, and a second target gamma value among second target well logging skeleton parameters.

[0010] Optionally, the calculating and processing of the first initial logging parameters corresponding to the different formation depths of the wells to be compared within the target formation comparison interval to obtain a first data point sequence of the different formation depths corresponding to the wells to be compared specifically includes:

[0011] Performing data processing based on first density logging data, first neutron logging data, and first acoustic logging data of the well to be compared within the target formation comparison interval to obtain first logging skeleton parameters of different formation depths corresponding to the well to be compared;

[0012] Performing data processing based on the first natural gamma logging data of the well to be compared within the target formation comparison interval to obtain the first target gamma value of each of the different formation depths corresponding to the well to be compared;

[0013] Adjust the first well logging skeleton parameters at the same formation depth based on the first target gamma value at the same formation depth to obtain first target well logging skeleton parameters at different formation depths corresponding to the wells to be compared, wherein the first target well logging skeleton parameters include first target apparent skeleton density and first target apparent skeleton acoustic wave time difference;

[0014] Data is screened based on the first target logging skeleton parameters and the first target gamma value of each of the different formation depths within the target formation comparison interval to obtain the first data point sequence.

[0015] Optionally, the data processing is performed based on the first density logging data, the first neutron logging data and the first acoustic logging data of the well to be compared in the target formation comparison interval to obtain the first logging skeleton parameters of different formation depths corresponding to the well to be compared, specifically including:

[0016] Based on the first density porosity, the first neutron porosity, the first shale volume, the predetermined density logging shale porosity and the predetermined neutron logging shale porosity corresponding to each different formation depth of the well to be compared in the target formation comparison interval, calculation processing is performed to obtain the first density neutron intersection porosity of each different formation depth corresponding to the well to be compared;

[0017] Based on the first sonic porosity corresponding to each different formation depth of the well to be compared within the target formation comparison interval, the first neutron porosity, the first shale volume, the predetermined sonic logging shale porosity and the predetermined neutron logging shale porosity, calculation processing is performed to obtain the first sonic neutron intersection porosity of each different formation depth corresponding to the well to be compared;

[0018] Based on the first density neutron intersection porosity, the first acoustic velocity neutron intersection porosity, the first well logging volume density, the first well logging acoustic wave time difference, the predetermined fluid density and the predetermined fluid acoustic wave time difference at each of the different formation depths, calculation processing is performed to obtain initial well logging skeleton parameters at each of the different formation depths corresponding to the well to be compared;

[0019] Each of the initial well logging skeleton parameters is normalized to obtain the first well logging skeleton parameters of each of the different formation depths corresponding to the well to be compared.

[0020] Optionally, the data processing is performed based on the first natural gamma logging data of the well to be compared within the target formation comparison interval to obtain the first target gamma value of each of the different formation depths corresponding to the well to be compared, specifically including:

[0021] Performing wavelet denoising processing based on initial gamma values ​​corresponding to different formation depths of the well to be compared within the target formation comparison interval to obtain first well logging gamma values ​​of different formation depths corresponding to the well to be compared;

[0022] The first well logging gamma values ​​are normalized to obtain first target gamma values ​​of the different formation depths corresponding to the wells to be compared.

[0023] Optionally, the step of calculating and processing the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well specifically includes:

[0024] Performing data processing based on the second density logging data, the second neutron logging data and the second acoustic logging data of the standard well within the target formation comparison interval to obtain the second logging skeleton parameters of different formation depths corresponding to the well to be compared;

[0025] Performing data processing based on the second natural gamma logging data of the standard well within the target formation comparison interval to obtain the second target gamma value of each of the different formation depths corresponding to the standard well;

[0026] Adjusting the second well logging skeleton parameters at the same formation depth based on the second target gamma value at the same formation depth to obtain second target well logging skeleton parameters at the different formation depths corresponding to the standard well;

[0027] Data is screened based on the second target logging skeleton parameters and the second target gamma value of each of the different formation depths within the target formation comparison interval to obtain the second data point sequence.

[0028] Optionally, performing formation comparison based on the first data point sequence and the second data point sequence using a preset dynamic time warping method to obtain a formation comparison result of the well to be compared relative to the standard well specifically includes:

[0029] Performing calculation based on each first data point in the first data point sequence and each second data point in the second data point sequence to obtain a distance matrix;

[0030] Perform cumulative distance calculation based on the distance matrix to obtain a cumulative distance matrix;

[0031] The shortest path backtracking is performed based on the accumulated distance matrix to obtain the shortest formation matching path of the well to be compared relative to the standard well.

[0032] Optionally, the method further includes:

[0033] Stratigraphic positioning is performed based on each predetermined stratigraphic layer of the standard well and the shortest matching path to obtain each target stratigraphic layer of the well to be compared that corresponds to each predetermined stratigraphic layer.

[0034] In order to solve the above problems, the present application provides a formation comparison device, comprising:

[0035] A first calculation module is used to perform calculation processing based on the first initial logging parameters corresponding to the different formation depths of the wells to be compared within the target formation comparison interval, so as to obtain a first data point sequence of the different formation depths corresponding to the wells to be compared; each first data point in the first data point sequence includes a first target apparent skeleton density, a first target apparent skeleton acoustic wave time difference and a first target gamma value in the first target logging skeleton parameters;

[0036] A second calculation module is used to perform calculation processing based on the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well; each second data point in the second data point sequence includes a second target apparent skeleton density, a second target apparent skeleton acoustic wave time difference, and a second target gamma value in the second target logging skeleton parameters;

[0037] The stratigraphic comparison module is used to perform stratigraphic comparison based on the first data point sequence and the second data point sequence using a preset dynamic time warping method to obtain a stratigraphic comparison result of the well to be compared relative to the standard well.

[0038] In order to solve the above problem, the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned stratigraphic comparison method are implemented.

[0039] In order to solve the above problems, the present application provides an electronic device, which at least includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above-mentioned stratigraphic comparison method when executing the computer program in the memory.

[0040] Beneficial effects of the present application: The present application uses a preset dynamic time warping method to perform stratigraphic comparison through a multidimensional data point sequence of the first data point sequence of the well to be compared within the target stratigraphic comparison interval and a second data point sequence of the standard well within the target stratigraphic comparison interval to obtain stratigraphic comparison results, which can save manpower and improve the efficiency of stratigraphic comparison.

[0041] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0043] Figure 1 A schematic diagram of a process flow of a formation correlation method provided in an embodiment of the present application is shown;

[0044] Figure 2 A schematic diagram of a process flow of a formation correlation method provided by another embodiment of the present application is shown;

[0045] Figure 3 A schematic diagram showing the principle of curved path calculation and path backtracking in a dynamic time warping algorithm in an embodiment of the present application is shown;

[0046] Figure 4 A schematic diagram showing an embodiment of the present application of obtaining approximate gamma signals a1-a5 and detail gamma signals d1-d5 after decomposition using wavelet basis functions;

[0047] Figure 5 A schematic diagram showing the reconstructed denoised natural gamma data according to an embodiment of the present application is shown;

[0048] Figure 6 A schematic diagram of the shortest path matching graph of two data point sequences obtained by the dynamic time warping method according to an embodiment of the present application is shown;

[0049] Figure 7A structural block diagram of a formation comparison device provided in another embodiment of the present application is shown. DETAILED DESCRIPTION

[0050] Various aspects and features of the present application are described herein with reference to the accompanying drawings.

[0051] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present application will occur to those skilled in the art.

[0052] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0053] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0054] It should also be understood that although the present application has been described with reference to some specific examples, those skilled in the art will be able to readily implement many other equivalent forms of the present application.

[0055] The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

[0056] Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments applied for are merely examples of the present application, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that obscure the present application. Therefore, the specific structural and functional details applied for herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present application in a variety of ways with substantially any suitable detailed structure.

[0057] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present application.

[0058] The present application embodiment provides a formation comparison method, such as Figure 1 As shown, including:

[0059] Step S101: performing calculation processing based on first initial logging parameters corresponding to different formation depths of the wells to be compared within the target formation comparison interval, to obtain a first data point sequence of different formation depths corresponding to the wells to be compared;

[0060] In the specific implementation of this step, the first initial logging parameters include first density logging data corresponding to the well to be compared, first neutron logging data corresponding to the well to be compared, first acoustic logging data corresponding to the well to be compared, and first natural gamma logging data corresponding to the well to be compared; specifically, data processing is performed based on the first density logging data, the first neutron logging data, and the first acoustic logging data of the well to be compared in the target formation comparison interval to obtain first logging skeleton parameters of different formation depths corresponding to the well to be compared; data processing is performed based on the first natural gamma logging data of the well to be compared in the target formation comparison interval to obtain first target gamma values ​​of different formation depths corresponding to the well to be compared; parameter adjustment is performed on the first logging skeleton parameters of the same formation depth based on the first target gamma value of the same formation depth to obtain first target logging skeleton parameters of different formation depths corresponding to the well to be compared; data screening is performed based on the first target logging skeleton parameters and the first target gamma value of different formation depths in the target formation comparison interval to obtain the first data point sequence. Each first data point in the first data point sequence includes three dimensions, namely, the first target apparent skeleton density and the first target apparent skeleton acoustic wave time difference in the first target logging skeleton parameter and the first target gamma value. The present application uses a standard well with a known geological layering structure to perform stratigraphic comparison with the well to be compared, so as to determine the position of the predetermined stratigraphic layer of the standard well in the well to be compared, and provide a theoretical basis for subsequent oil and gas field exploration.

[0061] Step S102: performing calculation processing based on the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well;

[0062] In the specific implementation of this step, the second initial logging parameters include second density logging data corresponding to the standard well, second neutron logging data corresponding to the standard well, second acoustic logging data corresponding to the standard well, and second natural gamma logging data corresponding to the standard well; specifically, data processing is performed based on the second density logging data, second neutron logging data, and second acoustic logging data of the standard well in the target formation comparison interval to obtain second logging skeleton parameters of different formation depths corresponding to the well to be compared; data processing is performed based on the second natural gamma logging data of the standard well in the target formation comparison interval to obtain second target gamma values ​​of different formation depths corresponding to the standard well; parameter adjustment is performed on the second logging skeleton parameters of the same formation depth based on the second target gamma value of the same formation depth to obtain second target logging skeleton parameters of different formation depths corresponding to the standard well; data screening is performed based on the second target logging skeleton parameters and the second target gamma value of each different formation depth in the target formation comparison interval to obtain the second data point sequence. Each second data point in the second data point sequence includes three dimensions, which are second target apparent skeleton density, second target apparent skeleton acoustic wave time difference and second target gamma value in the second target well logging skeleton parameters.

[0063] Step S103: Based on the first data point sequence and the second data point sequence, a preset dynamic time warping method is used to perform formation comparison to obtain a formation comparison result of the well to be compared relative to the standard well.

[0064] During the specific implementation of this step, calculations are performed based on each first data point in the first data point sequence and each second data point in the second data point sequence to obtain a distance matrix; cumulative distance calculations are performed based on the distance matrix to obtain a cumulative distance matrix; and shortest path backtracing is performed based on the cumulative distance matrix to obtain the shortest formation matching path of the well to be compared relative to the standard well.

[0065] The present application uses a preset dynamic time warping method to perform stratigraphic comparison through a multidimensional data point sequence of the first data point sequence of the well to be compared within the target stratigraphic comparison interval and a second data point sequence of the standard well within the target stratigraphic comparison interval to obtain stratigraphic comparison results, which can save manpower and improve the efficiency of stratigraphic comparison.

[0066] Another embodiment of the present application provides another formation comparison method, such as Figure 2 As shown, including:

[0067] Step S201: performing data processing based on the first density logging data, the first neutron logging data and the first acoustic logging data of the well to be compared within the target formation comparison interval to obtain the first logging skeleton parameters of different formation depths corresponding to the well to be compared;

[0068] In the specific implementation process of this step, based on the first density porosity corresponding to each different formation depth of the well to be compared in the target formation comparison interval, , first neutron porosity , the first mud volume , Predetermined density logging mud porosity And the predetermined neutron logging mud porosity Perform calculation processing to obtain the first density neutron intersection porosity of each of the different formation depths corresponding to the well to be compared Specifically, based on the first density porosity , the first mud volume and the predetermined density logging mud porosity Calculate and process to obtain the density logging mud-corrected porosity , the density logging mud-corrected porosity The mathematical formula for calculating can be shown as follows:

[0069] (1)

[0070] Then, based on the first neutron porosity , the first mud volume And the predetermined neutron logging mud porosity Calculate and process to obtain the neutron logging shale-corrected porosity ; The neutron logging mud-corrected porosity The mathematical formula for calculating can be shown as follows:

[0071] (2)

[0072] Finally, the porosity is corrected based on the density log shale quality 、The neutron logging mud correction porosity Perform calculation processing to obtain the first density neutron intersection porosity of each of the different formation depths corresponding to the well to be compared ; The first density neutron intersection porosity The mathematical expression of can be shown as follows:

[0073] (3)

[0074] Based on the first acoustic velocity porosity corresponding to each different formation depth of the well to be compared within the target formation comparison interval 、The first neutron porosity , the first mud volume , Predetermined sonic logging mud porosity And the predetermined neutron logging mud porosity Calculate and process to obtain the first acoustic velocity neutron intersection porosity at different formation depths corresponding to the well to be compared. ; Specifically, based on the first sonic porosity , the first mud volume , Predetermined sonic logging mud porosity Calculate and process to obtain the shale-corrected porosity of the sonic logging corresponding to the different formation depths of the well to be compared. ; Sonic logging mud-corrected porosity The mathematical formula for calculating can be shown as follows:

[0075] (4)

[0076] Then, the porosity is corrected based on the shale quality of the sonic logging. and the neutron logging shale-corrected porosity Calculate and process to obtain the first acoustic velocity neutron intersection porosity at different formation depths corresponding to the well to be compared. ; First sonic neutron intersection porosity The mathematical formula for calculating can be shown as follows:

[0077] (5)

[0078] The first density neutron intersection porosity based on the different formation depths 、The first sonic neutron intersection porosity , the first logging volume density , the first logging acoustic time difference , Predetermined fluid density and predetermined fluid acoustic time difference Perform calculation processing to obtain initial logging skeleton parameters of each of the different formation depths corresponding to the well to be compared; the initial logging skeleton parameters include initial apparent skeleton density and initial visual skeleton sound wave time difference Specifically, based on the first well logging volume density , first density neutron intersection porosity , Predetermined fluid density Perform calculation processing to obtain the initial apparent skeleton density of each of the different formation depths corresponding to the well to be compared ; The initial apparent skeleton density The mathematical expression of can be shown as follows:

[0079] (6)

[0080] Based on the first logging acoustic time difference , first sonic neutron intersection porosity , preset fluid acoustic time difference Perform calculation processing to obtain the initial visual skeleton acoustic wave time difference of each of the different formation depths corresponding to the well to be compared ; Initial visual skeleton sound wave time difference The mathematical expression of can be expressed as follows:

[0081] (7)

[0082] Each of the initial well logging skeleton parameters is normalized to obtain the first well logging skeleton parameters of each of the different formation depths corresponding to the well to be compared. The present application adopts the maximum and minimum normalization method to normalize the initial well logging skeleton parameters to obtain the first well logging skeleton parameters of each of the different formation depths corresponding to the well to be compared.

[0083] Step S202: performing data processing based on the first natural gamma logging data of the well to be compared within the target formation comparison interval to obtain the first target gamma value of each of the different formation depths corresponding to the well to be compared;

[0084] In the specific implementation process of this step, wavelet denoising is performed based on the initial gamma values ​​corresponding to the different formation depths of the wells to be compared within the target formation comparison interval to obtain the first well logging gamma values ​​of the different formation depths corresponding to the wells to be compared; specifically, wavelet decomposition is performed based on the initial gamma values ​​corresponding to the different formation depths of the wells to be compared within the target formation comparison interval to extract each wavelet coefficient; specifically, a wavelet basis function is constructed; the mathematical expression of the wavelet basis function can be expressed as the following formula (8):

[0085] (8)

[0086] in, is the scale parameter; is the translation parameter; Depends on the parameters , The wavelet basis function is Represents time information; for the natural gamma curve (GR) composed of the initial gamma values ​​corresponding to different stratum depths, the wavelet coefficients are obtained by wavelet transformation, and these wavelet coefficients contain the time-frequency information of the signal. The natural gamma curve is subjected to wavelet decomposition using the wavelet basis function to obtain the wavelet coefficients; the mathematical expression of the decomposition function of the wavelet decomposition can be shown as follows:

[0087] (9)

[0088] in, is a wavelet coefficient. Each of the wavelet coefficients is processed by using a preset threshold to obtain each target wavelet coefficient; specifically, each of the wavelet coefficients can be processed by a hard threshold method or a soft threshold method. When the hard threshold method is used, the wavelet coefficients less than the preset threshold are set to zero to obtain each of the target wavelet coefficients; when the soft threshold method is used, each of the wavelet coefficients is subtracted from the preset threshold to obtain each of the target wavelet coefficients. The preset threshold can be set according to actual needs and according to the adopted threshold method. The natural gamma curve is reconstructed based on each of the target wavelet coefficients to obtain a denoised gamma curve to obtain each of the first well logging gamma values; each of the first well logging gamma values ​​is normalized to obtain the first target gamma values ​​of each of the different formation depths corresponding to the well to be compared. The first well logging gamma value is normalized by using the maximum and minimum normalization method to obtain the first target gamma values ​​of each of the different formation depths corresponding to the well to be compared.

[0089] Step S203: adjusting the first well logging skeleton parameters at the same formation depth based on the first target gamma value at the same formation depth to obtain first target well logging skeleton parameters at different formation depths corresponding to the wells to be compared;

[0090] In the specific implementation of this step, specifically, calculation processing is performed based on each of the first target gamma values ​​to obtain the first weight coefficient corresponding to each of the first well logging skeleton parameters; multiplication processing is performed based on the same first well logging skeleton parameter and the first weight coefficient corresponding to the same first well logging skeleton parameter to obtain the first target well logging skeleton parameters of each of the different formation depths corresponding to the well to be compared. The first target well logging skeleton parameters include the first target apparent skeleton density and the first target apparent skeleton acoustic wave time difference. The high and low levels of the first target gamma values ​​at different formation depths can well reflect the mud content in the formation. When comparing formations with low mud content, the skeleton parameters should be used as the main basis for comparison. Correspondingly, in formations with high mud content, the gamma value should be used as the main basis, and the parameter value of the skeleton parameter should be lowered.

[0091] Step S204: performing data screening based on the first target logging skeleton parameters and the first target gamma value of each of the different formation depths within the target formation comparison interval to obtain the first data point sequence;

[0092] During the specific implementation of this step, data matching is performed based on the first target logging skeleton parameters and the first target gamma value at the same formation depth to obtain an initial first data point sequence; the initial first data point sequence is filtered to obtain the first data point sequence; each first data point in the first data point sequence includes three dimensions, namely, the first target apparent skeleton density and the first target apparent skeleton acoustic wave time difference in the first target logging skeleton parameters and the first target gamma value.

[0093] Step S205: performing data processing based on the second density logging data, the second neutron logging data and the second acoustic logging data of the standard well within the target formation comparison interval to obtain second logging skeleton parameters of different formation depths corresponding to the well to be compared;

[0094] During the specific implementation of this step, data processing is performed based on the second density logging data, the second neutron logging data and the second acoustic logging data of the standard well within the target formation comparison interval to obtain second logging skeleton parameters for each different formation depth corresponding to the well to be compared; data processing is performed based on the second natural gamma logging data of the standard well within the target formation comparison interval to obtain second target gamma values ​​for each of the different formation depths corresponding to the standard well; parameter adjustment is performed on the second logging skeleton parameters of the same formation depth based on the second target gamma value of the same formation depth to obtain second target logging skeleton parameters for each of the different formation depths corresponding to the standard well; data screening is performed based on the second target logging skeleton parameters and the second target gamma value for each of the different formation depths within the target formation comparison interval to obtain the second data point sequence.

[0095] Step S206: performing data processing based on the second natural gamma logging data of the standard well within the target formation comparison interval to obtain a second target gamma value of each of the different formation depths corresponding to the standard well;

[0096] During the specific implementation of this step, wavelet denoising is performed based on the second initial gamma values ​​corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain the second logging gamma values ​​of the different formation depths corresponding to the standard well; each of the second logging gamma values ​​is normalized to obtain the second target gamma values ​​of the different formation depths corresponding to the well to be compared.

[0097] Step S207: adjusting the second well logging skeleton parameters at the same formation depth based on the second target gamma value at the same formation depth to obtain second target well logging skeleton parameters at the different formation depths corresponding to the standard well;

[0098] In the specific implementation process of this step, a calculation process is performed based on each of the second target gamma values ​​to obtain a second weight coefficient corresponding to each of the second well logging skeleton parameters; a multiplication process is performed based on the same second well logging skeleton parameter and the second weight coefficient corresponding to the same second well logging skeleton parameter to obtain the second target well logging skeleton parameters of each of the different formation depths corresponding to the standard well. The second target well logging skeleton parameters include a second target visual skeleton density and a second target visual skeleton acoustic wave time difference.

[0099] Step S208: performing data screening based on the second target logging skeleton parameters and the second target gamma value of each of the different formation depths within the target formation comparison interval to obtain the second data point sequence;

[0100] In the specific implementation process of this step, data matching is performed based on the second target logging skeleton parameters and the second target gamma value at the same formation depth to obtain an initial second data point sequence; the initial second data point sequence is filtered to obtain the second data point sequence; each second data point in the second data point sequence includes three dimensions, namely, the second target apparent skeleton density and the second target apparent skeleton acoustic wave time difference in the second target logging skeleton parameters and the second target gamma value.

[0101] Step S209: performing calculation based on each first data point in the first data point sequence and each second data point in the second data point sequence to obtain a distance matrix;

[0102] In the specific implementation process of this step, distance calculation is performed based on each first data point in the first data point sequence and each second data point in the second data point sequence. The distance value between each first data point and each second data point is , based on each of the distance values, the distance matrix is ​​constructed Specifically, the mathematical expression for calculating the distance value from each first data point to each second data point can be expressed as the following formula (10):

[0103] (10)

[0104] in, is the first data point in the sequence The first data point and the second data point in the sequence The Euclidean distance of the second data point; Represents the first data point in the sequence The first data point data dimensions; Represents the first The second data point data dimensions; constructing a matrix based on each of the distance values ​​to obtain the distance matrix .

[0105] Step S210: performing cumulative distance calculation based on the distance matrix to obtain a cumulative distance matrix;

[0106] In the specific implementation process of this step, the cumulative distance calculation is performed based on the distance matrix to obtain the cumulative distance matrix ; The mathematical expression of the cumulative distance matrix can be expressed as follows:

[0107] (11)

[0108] in, Represents a data point in the distance matrix The minimum cumulative distance value of the previous path point. The mathematical expression of can be shown as follows:

[0109] (12)

[0110] in, Represents the starting point to Minimum distance between data points.

[0111] Step S211: performing shortest path backtracking based on the accumulated distance matrix to obtain the shortest formation matching path of the well to be compared relative to the standard well;

[0112] In the specific implementation process of this step, the cumulative distance value is screened based on the cumulative distance matrix to obtain the target cumulative distance value; the target cumulative distance value can be the cumulative distance value corresponding to the upper right corner element of the cumulative distance matrix. Based on the target cumulative distance value, each node to be matched in the neighborhood of the target cumulative distance value is determined to obtain each first neighborhood node; the first neighborhood node with the smallest value among the first neighborhood nodes is determined as the first backtracking node; the current target cumulative distance value is determined by the first backtracking node; based on the current target cumulative distance value, each node to be matched in the neighborhood of the current target cumulative distance value is determined to obtain each second neighborhood node; the second neighborhood node with the smallest value among the second neighborhood nodes is determined as the second backtracking node; until the second target cumulative distance value of the cumulative distance matrix is ​​backtracked to obtain the shortest formation matching path of the well to be compared relative to the standard well, and the shortest formation matching path is the formation comparison result. The second target cumulative distance value can be the first cumulative distance value in the lower left corner of the cumulative distance matrix. Since path regularization needs to meet boundary conditions, monotonicity and continuity, the shortest path obtained after regularization must start from the lower left corner and end at the upper right corner. The distance between the two signals is equal to the cumulative distance matrix The upper right corner of The corresponding cumulative distance value. From the continuity and monotonicity, we know that the end point of the path The previous point is ), and One of them, It is the abbreviation of path, which represents the path. For the path point, whose position is n columns and m rows in the cumulative distance matrix; since this path is the shortest path, the corresponding value of the previous point should be the smallest among the three points, so we only need to find the possible point in front of the path point , and The previous path point can be found by finding the minimum value of . Represents the cumulative distance matrix Line If there is a point with the same value as the previous point, it means that there is more than one shortest path. This situation is rare and has little impact on the overall comparison result. In this application, when D(n-1,m-1) is equal to the minimum value, it will take precedence as the shortest path point. is not the minimum value and and When both are minimum values, if , then select is the shortest path point, if , then select is the shortest path point. If the matrix boundary is touched during the backtracking process, there is only one possible previous path point. , then the previous path point must be , until you return to the starting point ; If traced back to , then the previous path point must be ; until you return to the starting point Form a complete shortest matching path, such as Figure 3 The figure shows a schematic diagram of the curved path calculation and path backtracking principle in the dynamic time warping algorithm.

[0113] Step S212: Performing formation positioning based on each predetermined formation layer of the standard well and the shortest matching path to obtain each target formation position of the well to be compared corresponding to each predetermined formation layer.

[0114] In the specific implementation process of this step, according to the shortest matching path result, the corresponding relationship between each depth point of the standard well and the well to be compared can be obtained, and then the well to be compared can be located through each predetermined stratigraphic layer of the standard well and the shortest matching path to obtain the position of each target stratigraphic layer of the well to be compared. Through stratigraphic comparison, geologists can understand the geological process at the time and local time, and based on this, find the corresponding oil and gas-bearing areas in combination with subsequent work. Stratigraphic comparison plays a fundamental role in the exploration and development of oil and gas fields.

[0115] This application makes full use of the data of the well to be compared and the data of the standard well to obtain the key parameters of the rock skeleton that are conducive to comparison; pre-processes the natural gamma curve data of the well to be compared by wavelet transform, greatly reducing the difference in curves between wells caused by noise; dynamically adjusts the skeleton parameters by referring to the size of the natural gamma curve, so that this method can be well applied to the comparison of complex formations; forms a multidimensional data point sequence by combining multiple parameters, and cites the Euclidean distance to measure the similarity between the data point sequences; obtains the formation comparison results of the standard well and the standard well according to the path with the shortest distance. The measurement result has a small error, saves manpower and material resources, and is more efficient.

[0116] The following is an explanation of the stratigraphic comparison of this application in combination with a specific application scenario: It is applied to the stratigraphic comparison of the Dengying Formation Deng IV reservoir in the Sinian System in the Gaoshiti block of a gas field. The stratigraphic comparison is performed on two exploration wells (Gaoshi 1 Well and Gaoshi 11 Well) in the block, of which Gaoshi 1 Well is used as the standard well and Gaoshi 11 Well is used as the well to be compared. The thickness of the compared layer is about 300m. The comparison steps are as follows:

[0117] Step 1: According to the existing manual comparison results, the comparison layer section is divided into 6 small layers, namely D421, D422, D423, D411, D412 and D413 from top to bottom. According to the first initial logging parameters corresponding to the different formation depths of the existing wells to be compared, the first data point sequence of the different formation depths corresponding to the wells to be compared is obtained;

[0118] Step 2: Calculate and process the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well;

[0119] Specifically, the natural gamma data of the comparison well and the standard well are subjected to wavelet denoising, and the Haar wavelet basis is used for 5-layer decomposition denoising, such as Figure 4 After decomposition, approximate gamma signals a1-a5 and detail gamma signals d1-d5 can be obtained. The hard threshold method is selected to set the wavelet coefficients less than the threshold to zero, and then the denoised natural gamma data is reconstructed. Figure 5 The target formation comparison interval is intercepted: the denoised natural gamma value, apparent skeleton density value and apparent skeleton acoustic wave time difference data from the top of D421 to the bottom of D413 are uniformly normalized and combined to form a three-dimensional data point sequence: the first data point sequence corresponding to the well to be compared and the second data point sequence corresponding to the standard well.

[0120] Step 3: Calculate the distance between the data point sequence elements corresponding to the two wells and form the distance matrix d, and then calculate the shortest cumulative distance from the starting point to each point and form the cumulative distance matrix D. Under the boundary conditions, monotonicity and continuity constraints of DTW, the path is gradually traced back from the end point to the starting point to obtain the shortest matching path: Figure 6 The figure shows a schematic diagram of the shortest path matching diagram of two data point sequences obtained by the dynamic time warping method of the present application;

[0121] Finally, according to the shortest matching path results, the corresponding relationship between the depth points of Gaoshi 1 Well and Gaoshi 11 Well can be obtained, and then the stratigraphic comparison results of each sublayer in Gaoshi 11 Well can be obtained. The thickness of the sublayer is generally 45-60m. This paper takes the layer thickness of 50m as the benchmark when calculating the relative error. The comparison results of Gaoshi 11 Well are compared with the original manual stratification. The calculated error is shown in Table 1. The absolute error is less than 1.25m and the relative error is less than 2.5%. It can be seen that the comparison results of this method are reliable;

[0122]

[0123] Another embodiment of the present application provides a formation comparison device, such as Figure 7 As shown, including:

[0124] A first calculation module 1 is used to perform calculation processing based on the first initial logging parameters corresponding to the different formation depths of the wells to be compared within the target formation comparison interval, to obtain a first data point sequence of the different formation depths corresponding to the wells to be compared; each first data point in the first data point sequence includes a first target apparent skeleton density, a first target apparent skeleton acoustic wave time difference, and a first target gamma value in the first target logging skeleton parameters;

[0125] A second calculation module 2 is used to calculate and process the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well; each second data point in the second data point sequence includes a second target apparent skeleton density, a second target apparent skeleton acoustic wave time difference, and a second target gamma value in the second target logging skeleton parameters;

[0126] The formation comparison module 3 is used to perform formation comparison based on the first data point sequence and the second data point sequence using a preset dynamic time warping method to obtain a formation comparison result of the well to be compared relative to the standard well.

[0127] In the specific implementation process, the first calculation module 1 is specifically used for: performing data processing based on the first density logging data, the first neutron logging data and the first acoustic logging data of the well to be compared in the target formation comparison interval to obtain the first logging skeleton parameters of each different formation depth corresponding to the well to be compared; performing data processing based on the first natural gamma logging data of the well to be compared in the target formation comparison interval to obtain the first target gamma values ​​of each different formation depth corresponding to the well to be compared; adjusting the first logging skeleton parameters of the same formation depth based on the first target gamma value of the same formation depth to obtain the first target logging skeleton parameters of each different formation depth corresponding to the well to be compared, the first target logging skeleton parameters including the first target apparent skeleton density and the first target apparent skeleton acoustic wave time difference; performing data screening based on the first target logging skeleton parameters and the first target gamma value of each different formation depth in the target formation comparison interval to obtain the first data point sequence.

[0128] In the specific implementation process, the first calculation module 1 is also used to: perform calculation processing based on the first density porosity, the first neutron porosity, the first mud volume, the predetermined density logging mud porosity and the predetermined neutron logging mud porosity corresponding to each different formation depth of the well to be compared in the target formation comparison interval, to obtain the first density neutron intersection porosity of each different formation depth corresponding to the well to be compared; based on the first sound velocity porosity, the first neutron porosity, the first mud volume, the predetermined sound velocity logging mud porosity and the predetermined neutron logging mud porosity corresponding to each different formation depth of the well to be compared in the target formation comparison interval The logging mud porosity is calculated and processed to obtain the first acoustic velocity neutron intersection porosity of each of the different formation depths corresponding to the well to be compared; the first density neutron intersection porosity, the first acoustic velocity neutron intersection porosity, the first logging volume density, the first logging acoustic wave time difference, the predetermined fluid density and the predetermined fluid acoustic wave time difference of each of the different formation depths are calculated and processed to obtain the initial logging skeleton parameters of each of the different formation depths corresponding to the well to be compared; each of the initial logging skeleton parameters is normalized to obtain the first logging skeleton parameters of each of the different formation depths corresponding to the well to be compared.

[0129] In the specific implementation process, the first calculation module 1 is also used to: perform wavelet denoising processing based on the initial gamma values ​​corresponding to the different formation depths of the wells to be compared within the target formation comparison interval to obtain the first logging gamma values ​​of the different formation depths corresponding to the wells to be compared; perform normalization processing on each of the first logging gamma values ​​to obtain the first target gamma values ​​of the different formation depths corresponding to the wells to be compared.

[0130] During the specific implementation process, the second calculation module 2 is specifically used to: perform data processing based on the second density logging data, the second neutron logging data and the second acoustic logging data of the standard well in the target formation comparison interval to obtain the second logging skeleton parameters of each different formation depth corresponding to the well to be compared; perform data processing based on the second natural gamma logging data of the standard well in the target formation comparison interval to obtain the second target gamma values ​​of each different formation depth corresponding to the standard well; adjust the second logging skeleton parameters of the same formation depth based on the second target gamma value of the same formation depth to obtain the second target logging skeleton parameters of each different formation depth corresponding to the standard well; perform data screening based on the second target logging skeleton parameters and the second target gamma value of each different formation depth in the target formation comparison interval to obtain the second data point sequence.

[0131] In the specific implementation process, the formation comparison module 3 is specifically used to: perform calculations based on each first data point in the first data point sequence and each second data point in the second data point sequence to obtain a distance matrix; perform cumulative distance calculations based on the distance matrix to obtain a cumulative distance matrix; perform shortest path backtracing based on the cumulative distance matrix to obtain the shortest formation matching path of the well to be compared relative to the standard well.

[0132] In the specific implementation process, the device also includes a positioning module, which is specifically used to: perform formation positioning based on each predetermined formation layer of the standard well and the shortest matching path to obtain each target formation of the well to be compared corresponding to each predetermined formation layer.

[0133] The present application uses a preset dynamic time warping method to perform stratigraphic comparison through a multidimensional data point sequence of the first data point sequence of the well to be compared within the target stratigraphic comparison interval and a second data point sequence of the standard well within the target stratigraphic comparison interval to obtain stratigraphic comparison results, which can save manpower and improve the efficiency of stratigraphic comparison.

[0134] Another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the following method steps are implemented:

[0135] Step 1: Calculate and process the first initial logging parameters corresponding to the different formation depths of the wells to be compared within the target formation comparison interval to obtain a first data point sequence of the different formation depths corresponding to the wells to be compared; each first data point in the first data point sequence includes a first target apparent skeleton density, a first target apparent skeleton acoustic wave time difference, and a first target gamma value in the first target logging skeleton parameters;

[0136] Step 2: Calculate and process the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well; each second data point in the second data point sequence includes a second target apparent skeleton density, a second target apparent skeleton acoustic wave time difference, and a second target gamma value in the second target logging skeleton parameters;

[0137] Step three: Based on the first data point sequence and the second data point sequence, a preset dynamic time warping method is used to perform formation comparison to obtain a formation comparison result of the well to be compared relative to the standard well.

[0138] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0139] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0140] The specific implementation process of the above method steps can be found in the embodiments of any of the above-mentioned stratigraphic correlation methods, and this embodiment will not be repeated here.

[0141] The present application uses a preset dynamic time warping method to perform stratigraphic comparison through a multidimensional data point sequence of the first data point sequence of the well to be compared within the target stratigraphic comparison interval and a second data point sequence of the standard well within the target stratigraphic comparison interval to obtain stratigraphic comparison results, which can save manpower and improve the efficiency of stratigraphic comparison.

[0142] Another embodiment of the present application provides an electronic device, which may be a server, and the electronic device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external client via a network connection. When the electronic device program is executed by the processor, it implements a function or step on the server side of a formation comparison method.

[0143] In one embodiment, an electronic device is provided, which may be a client. The electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external server via a network connection. When the electronic device program is executed by the processor, a function or step on the client side of a formation comparison method is implemented.

[0144] Another embodiment of the present application provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the following method steps when executing the computer program in the memory:

[0145] Step 1: Calculate and process the first initial logging parameters corresponding to the different formation depths of the wells to be compared within the target formation comparison interval to obtain a first data point sequence of the different formation depths corresponding to the wells to be compared; each first data point in the first data point sequence includes a first target apparent skeleton density, a first target apparent skeleton acoustic wave time difference, and a first target gamma value in the first target logging skeleton parameters;

[0146] Step 2: Calculate and process the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well; each second data point in the second data point sequence includes a second target apparent skeleton density, a second target apparent skeleton acoustic wave time difference, and a second target gamma value in the second target logging skeleton parameters;

[0147] Step three: Based on the first data point sequence and the second data point sequence, a preset dynamic time warping method is used to perform formation comparison to obtain a formation comparison result of the well to be compared relative to the standard well.

[0148] The specific implementation process of the above method steps can be found in the embodiments of any of the above-mentioned stratigraphic correlation methods, and this embodiment will not be repeated here.

[0149] The present application uses a preset dynamic time warping method to perform stratigraphic comparison through a multidimensional data point sequence of the first data point sequence of the well to be compared within the target stratigraphic comparison interval and a second data point sequence of the standard well within the target stratigraphic comparison interval to obtain stratigraphic comparison results, which can save manpower and improve the efficiency of stratigraphic comparison.

[0150] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A formation correlation method, characterized in that: include: Calculating and processing the first initial well logging parameters corresponding to the different formation depths of the wells to be compared within the target formation comparison interval to obtain a first data point sequence of the different formation depths corresponding to the wells to be compared; Calculating and processing the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well; Based on the first data point sequence and the second data point sequence, a preset dynamic time warping method is used to perform formation comparison to obtain a formation comparison result of the well to be compared relative to the standard well; Wherein, each first data point in the first data point sequence includes a first target apparent skeleton density, a first target apparent skeleton acoustic wave time difference and a first target gamma value in the first target well logging skeleton parameters; Each second data point in the second data point sequence includes a second target apparent skeleton density, a second target apparent skeleton acoustic wave time difference and a second target gamma value in the second target well logging skeleton parameters; The step of calculating and processing the first initial well logging parameters corresponding to the different formation depths of the wells to be compared within the target formation comparison interval to obtain a first data point sequence of the different formation depths corresponding to the wells to be compared specifically includes: Performing data processing based on first density logging data, first neutron logging data, and first acoustic logging data of the well to be compared within the target formation comparison interval to obtain first logging skeleton parameters of different formation depths corresponding to the well to be compared; Performing data processing based on the first natural gamma logging data of the well to be compared within the target formation comparison interval to obtain the first target gamma value of each of the different formation depths corresponding to the well to be compared; Adjust the first well logging skeleton parameters at the same formation depth based on the first target gamma value at the same formation depth to obtain first target well logging skeleton parameters at different formation depths corresponding to the wells to be compared, wherein the first target well logging skeleton parameters include first target apparent skeleton density and first target apparent skeleton acoustic wave time difference; Performing data screening based on the first target logging skeleton parameters and the first target gamma value of each of the different formation depths within the target formation comparison interval to obtain the first data point sequence; The data processing is performed based on the first density logging data, the first neutron logging data and the first acoustic logging data of the well to be compared in the target formation comparison interval to obtain the first logging skeleton parameters of different formation depths corresponding to the well to be compared, specifically including: Based on the first density porosity, the first neutron porosity, the first shale volume, the predetermined density logging shale porosity and the predetermined neutron logging shale porosity corresponding to each different formation depth of the well to be compared in the target formation comparison interval, calculation processing is performed to obtain the first density neutron intersection porosity of each different formation depth corresponding to the well to be compared; Based on the first sonic porosity corresponding to each different formation depth of the well to be compared within the target formation comparison interval, the first neutron porosity, the first shale volume, the predetermined sonic logging shale porosity and the predetermined neutron logging shale porosity, calculation processing is performed to obtain the first sonic neutron intersection porosity of each different formation depth corresponding to the well to be compared; Based on the first density neutron intersection porosity, the first acoustic velocity neutron intersection porosity, the first well logging volume density, the first well logging acoustic wave time difference, the predetermined fluid density and the predetermined fluid acoustic wave time difference at each of the different formation depths, calculation processing is performed to obtain initial well logging skeleton parameters at each of the different formation depths corresponding to the well to be compared; Each of the initial well logging skeleton parameters is normalized to obtain the first well logging skeleton parameters of each of the different formation depths corresponding to the well to be compared.

2. The method according to claim 1, characterized in that The step of performing data processing based on the first natural gamma logging data of the well to be compared within the target formation comparison interval to obtain the first target gamma value of each of the different formation depths corresponding to the well to be compared specifically includes: Performing wavelet denoising processing based on initial gamma values ​​corresponding to different formation depths of the well to be compared within the target formation comparison interval to obtain first well logging gamma values ​​of different formation depths corresponding to the well to be compared; The first well logging gamma values ​​are normalized to obtain first target gamma values ​​of the different formation depths corresponding to the wells to be compared.

3. The method according to claim 1, characterized in that The step of calculating and processing the second initial logging parameters corresponding to the different formation depths of the standard wells within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard wells specifically includes: Performing data processing based on the second density logging data, the second neutron logging data and the second acoustic logging data of the standard well within the target formation comparison interval to obtain the second logging skeleton parameters of different formation depths corresponding to the well to be compared; Performing data processing based on the second natural gamma logging data of the standard well within the target formation comparison interval to obtain the second target gamma value of each of the different formation depths corresponding to the standard well; Adjusting the second well logging skeleton parameters at the same formation depth based on the second target gamma value at the same formation depth to obtain second target well logging skeleton parameters at the different formation depths corresponding to the standard well; Data is screened based on the second target logging skeleton parameters and the second target gamma value of each of the different formation depths within the target formation comparison interval to obtain the second data point sequence.

4. The method according to claim 1, characterized in that The step of performing formation comparison based on the first data point sequence and the second data point sequence using a preset dynamic time warping method to obtain a formation comparison result of the well to be compared relative to the standard well specifically includes: Perform calculation based on each first data point in the first data point sequence and each second data point in the second data point sequence to obtain a distance matrix; Perform cumulative distance calculation based on the distance matrix to obtain a cumulative distance matrix; The shortest path backtracking is performed based on the accumulated distance matrix to obtain the shortest formation matching path of the well to be compared relative to the standard well.

5. The method according to claim 4, characterized in that The method further comprises: Based on each predetermined stratigraphic layer of the standard well and the shortest matching path, stratigraphic positioning is performed to obtain each target stratigraphic position of the well to be compared that corresponds to each predetermined stratigraphic layer.

6. A formation comparison device, characterized in that: include: The first calculation module is used to perform calculation processing based on the first initial logging parameters corresponding to the different formation depths of the wells to be compared in the target formation comparison interval, so as to obtain the first data point sequence of the different formation depths corresponding to the wells to be compared; specifically comprising: performing data processing based on the first density logging data, the first neutron logging data and the first acoustic logging data of the wells to be compared in the target formation comparison interval, so as to obtain the first logging skeleton parameters of the different formation depths corresponding to the wells to be compared; performing data processing based on the first density logging data, the first neutron logging data and the first acoustic logging data of the wells to be compared in the target formation comparison interval, so as to obtain the first logging skeleton parameters of the different formation depths corresponding to the wells to be compared; The logging skeleton parameters specifically include: calculating and processing the first density porosity, the first neutron porosity, the first mud volume, the predetermined density logging mud porosity and the predetermined neutron logging mud porosity corresponding to each different formation depth of the well to be compared within the target formation comparison interval, so as to obtain the first density neutron intersection porosity of each different formation depth corresponding to the well to be compared; calculating and processing the first sonic velocity porosity, the first neutron porosity, the first mud volume, the predetermined sonic velocity logging mud porosity and the predetermined neutron logging mud porosity corresponding to each different formation depth of the well to be compared within the target formation comparison interval, so as to obtain the first density neutron intersection porosity of each different formation depth corresponding to the well to be compared. first acoustic neutron intersection porosity; based on the first density neutron intersection porosity, the first acoustic neutron intersection porosity, the first logging volume density, the first logging acoustic wave time difference, the predetermined fluid density and the predetermined fluid acoustic wave time difference at each of the different formation depths, calculate and process to obtain the initial logging skeleton parameters of each of the different formation depths corresponding to the well to be compared; normalize each of the initial logging skeleton parameters to obtain the first logging skeleton parameters of each of the different formation depths corresponding to the well to be compared; perform data processing based on the first natural gamma logging data of the well to be compared in the target formation comparison interval to obtain the first target gamma logging parameters of each of the different formation depths corresponding to the well to be compared value; adjusting the first logging skeleton parameters of the same formation depth based on the first target gamma value of the same formation depth to obtain the first target logging skeleton parameters of each of the different formation depths corresponding to the well to be compared, the first target logging skeleton parameters including the first target apparent skeleton density and the first target apparent skeleton acoustic wave time difference; performing data screening based on the first target logging skeleton parameters of each of the different formation depths within the target formation comparison interval and the first target gamma value to obtain the first data point sequence; each first data point in the first data point sequence includes the first target apparent skeleton density, the first target apparent skeleton acoustic wave time difference and the first target gamma value in the first target logging skeleton parameters; A second calculation module is used to perform calculation processing based on the second initial logging parameters corresponding to the different formation depths of the standard well within the target formation comparison interval to obtain a second data point sequence of the different formation depths corresponding to the standard well; each second data point in the second data point sequence includes a second target apparent skeleton density, a second target apparent skeleton acoustic wave time difference, and a second target gamma value in the second target logging skeleton parameters; The stratigraphic comparison module is used to perform stratigraphic comparison based on the first data point sequence and the second data point sequence using a preset dynamic time warping method to obtain a stratigraphic comparison result of the well to be compared relative to the standard well.

7. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the stratigraphic comparison method described in any one of claims 1 to 5 are implemented.

8. An electronic device, characterized in that: The method at least comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the stratigraphic comparison method according to any one of claims 1 to 5 when executing the computer program on the memory.

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