Quantitative calculation of strike-slip displacement method, device, medium and electronic equipment

Abstract

The application discloses a method and device for quantitatively calculating strike-slip displacement, a medium and an electronic device. The method comprises the following steps: determining a strike-slip structure form based on three-dimensional seismic data; determining a planar distribution of a strike-slip fault according to seismic reflection characteristics corresponding to the strike-slip structure form, drilling data and well logging data; determining a distribution of sand bodies on both sides of the strike-slip fault according to the planar distribution of the strike-slip fault and actual drilling data corresponding to both sides of the strike-slip fault, and taking each sand body as a geological body; obtaining attribute data bodies corresponding to the geological bodies; calculating feature vectors corresponding to the geological bodies according to the attribute data bodies; calculating similarities between the geological bodies according to the feature vectors, and determining centroid coordinates of two geological bodies corresponding to the highest similarity among the similarities, so as to calculate the strike-slip displacement based on the centroid coordinates. The technical scheme provided by the application can improve the accuracy of calculating the strike-slip displacement.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of petroleum geological exploration, and particularly relates to a method and device for quantitatively calculating strike-slip displacement, a medium and an electronic device. BACKGROUND

[0002] Strike-slip structure is a tectonic deformation of the crust or lithosphere under the action of shear stress with a nearly horizontal displacement vector, and is an important oil and gas bearing structure pattern. Strike-slip faults are mainly characterized by relative strike-slip displacement of two plates, and generally have three types of tensile-torsional, compressive-torsional and parallel-torsional displacement. The strike-slip displacement of strike-slip faults is an important index for determining the range of oil and gas migration affected by strike-slip faults and calibrating the strength of strike-slip faults, but the estimation of strike-slip displacement is difficult, and has been one of the hot and difficult points in the study of structural geology.

[0003] At present, the calculation methods of strike-slip displacement mainly include four types: two-plate geological body comparison method, paleomagnetic method, crustal deformation velocity method and basin subsidence (or uplift) rate and boundary fault strike-slip rate ratio method. However, the above four methods have their shortcomings and limitations. The two-plate geological body comparison method is the most intuitive and accurate method for estimating strike-slip displacement, and the key of this method is to find reliable geological reference points, but it is difficult, and previous scholars mainly select reference points according to paleontology, sedimentation and lithological combination and other geological characteristics, so the error is large.

[0004] Based on the above background, the application provides a method for quantitatively calculating strike-slip displacement to improve the accuracy of calculating strike-slip displacement. SUMMARY

[0005] Embodiments of the application provide a method and device for quantitatively calculating strike-slip displacement, which can improve the accuracy of calculating strike-slip displacement.

[0006] Other characteristics and advantages of the application will become apparent from the following detailed description, or will be learned by practice of the application.

[0007] According to a first aspect of the embodiments of the present application, a method for quantitatively calculating strike-slip displacement is provided, which comprises: establishing a seismic work area model of a target area based on three-dimensional seismic data, and determining a strike-slip structural pattern in the seismic work area model of the target area through coherent slices, curvature analysis, and artificial intelligence fracture identification; calibrating a target layer horizon of the three-dimensional seismic data according to seismic reflection characteristics corresponding to the strike-slip structural pattern, drilling data, and logging data, and determining a planar distribution of strike-slip faults of the target layer; determining sedimentary systems on both sides of the strike-slip faults according to the planar distribution of the strike-slip faults and corresponding real drilling data on both sides of the strike-slip faults, and using reservoir inversion to depict sand bodies on both sides of the strike-slip faults, so as to determine a distribution of the sand bodies on both sides of the strike-slip faults and take each sand body on both sides of the strike-slip faults as a geological body; obtaining attribute data bodies corresponding to each geological body, wherein the attribute data bodies comprise a seismic root mean square amplitude, a single-frequency seismic attribute, a wave impedance inversion attribute, a heavy mineral proportion index, and a sedimentary stratum sand ratio; obtaining information entropy, central tendency, a dispersion coefficient, average joint entropy, and average mutual information of each attribute data body according to the attribute data bodies, so as to obtain a feature vector corresponding to each geological body; calculating a similarity between each geological body according to the feature vector, and determining a barycentric coordinate of two geological bodies corresponding to the highest similarity in each similarity, so as to calculate a strike-slip displacement based on the barycentric coordinate.

[0008] In some embodiments of the present application, the information entropy is calculated by the following formula based on the foregoing scheme:

[0009]

[0010] wherein H(X) is the information entropy; p(x i ) is a density estimation of X; and X is a random variable in a data set corresponding to each attribute data body.

[0011] In some embodiments of the present application, the central tendency is calculated by the following formula based on the foregoing scheme:

[0012]

[0013] wherein is the central tendency; x i is any one numerical value in a data set corresponding to each attribute data body; and n is a data number in the data set corresponding to the current attribute data body.

[0014] In some embodiments of the present application, the dispersion coefficient is calculated by the following formula based on the foregoing scheme:

[0015]

[0016] wherein s is a dispersion coefficient, used to describe the degree of data deviating from the center; is a central tendency; x n is any one value in the data set corresponding to each attribute data body; n is the number of data in the data set corresponding to the current attribute data body.

[0017] In some embodiments of the present application, based on the foregoing scheme, the average joint entropy is calculated by the following formula:

[0018]

[0019] wherein H(X,Y) is the average joint entropy, used to represent the amount of entropy in a system with two random variables; p(x,y) is the probability of each pair of variables occurring simultaneously; (X,Y) is the entropy of the system of random variables X and Y combined.

[0020] In some embodiments of the present application, based on the foregoing scheme, the average mutual information is calculated by the following formula:

[0021]

[0022] wherein I(X;Y) is the average mutual information, used to measure the degree of dependence between different attributes of the data set; p(x,y) is the probability of each pair of variables occurring simultaneously; (X,Y) is the entropy of the system of random variables X and Y combined.

[0023] In some embodiments of the present application, based on the foregoing scheme, the similarity is calculated by the following formula:

[0024]

[0025] wherein d i ={W i-熵 ,W i-中心趋势 ,W i-离散系数 ,W i-平均联合熵 ,W i-平均互信息量}; d j ={W j-熵 ,W j-中心趋势 ,W j-离散系数 ,W j-平均联合熵 ,W j-平均互信息量}; d i and d j are the feature vectors of the geological bodies on both sides of the strike-slip fault.

[0026] Compared with the prior art, the present application at least includes the following beneficial effects:

[0027] The application establishes a target area seismic work area model through three-dimensional seismic data. Through coherence slices, curvature and artificial intelligence fracture identification and other means, the target area seismic work area model plane and longitudinal fracture system are combed, and the strike-slip tectonic form of the target area seismic work area model is described. According to the seismic reflection characteristics, combined with drilling data and logging data, the horizon of the seismic data is calibrated, and the strike-slip fault plane distribution of the target layer in the target area seismic work area model is determined. According to the strike-slip fault plane distribution and the real drilling data on both sides of the strike-slip fault, the sedimentary system on both sides of the strike-slip fault is combed, and the sand body is finely described by using reservoir inversion, so as to determine the sand body distribution on both sides of the strike-slip fault, and each sand body on both sides of the strike-slip fault is regarded as a separate geological body.

[0028] Using the three-dimensional seismic data, drilling data, logging data and analysis and test data of the target area seismic work area model, the root mean square amplitude, single frequency seismic attribute, wave impedance inversion attribute, heavy mineral proportion index and sedimentary formation sand ratio of each geological body on both sides of the strike-slip fault are extracted. Based on the extracted attribute data body, the information entropy, central tendency, dispersion coefficient, average joint entropy and average mutual information of each attribute data body are extracted, so as to obtain the feature vector corresponding to the geological body. Based on the feature vector of the geological body, the similarity of the geological bodies on both sides of the strike-slip fault is calculated, so that the centroid coordinates of the geological body with the highest similarity on both sides of the fracture are calculated, and the accurate calculation of the strike-slip displacement is realized.

[0029] Based on the quantitative calculation of the strike-slip displacement method provided by the application, the accuracy of calculating the strike-slip displacement can be improved.

[0030] According to a second aspect of the embodiments of the present application, a device for quantitatively calculating strike-slip displacement is provided, which comprises: a data input and mapping unit, configured to establish a seismic work area model of a target area based on three-dimensional seismic data, determine a strike-slip structure pattern in the seismic work area model of the target area, and determine a planar distribution of a strike-slip fault of a target layer according to a corresponding seismic reflection feature of the strike-slip structure pattern, drilling data, and well logging data, so as to determine a sedimentary system on both sides of the strike-slip fault according to the planar distribution of the strike-slip fault and corresponding real drilling data on both sides of the strike-slip fault, further depict sand bodies on both sides of the strike-slip fault through reservoir inversion, determine a distribution of the sand bodies on both sides of the strike-slip fault, and take each sand body on both sides of the strike-slip fault as a geological body; a feature extraction unit, configured to obtain attribute data bodies corresponding to each geological body, the attribute data bodies comprising a seismic root mean square amplitude, a single-frequency seismic attribute, a wave impedance inversion attribute, a heavy mineral proportion index, and a sedimentary stratum sand ratio, and obtain information entropy, central tendency, a dispersion coefficient, average joint entropy, and average mutual information of each attribute data body according to the attribute data bodies; a calculation unit, configured to calculate a feature vector corresponding to each geological body according to the information entropy, the central tendency, the dispersion coefficient, the average joint entropy, and the average mutual information of each attribute data body; and a strike-slip displacement determination unit, configured to calculate a similarity of geological bodies on both sides of the strike-slip fault according to the feature vector, determine a barycentric coordinate of two geological bodies corresponding to the highest similarity, and calculate a strike-slip displacement based on the barycentric coordinate.

[0031] According to a third aspect of the embodiments of the present application, a computer readable storage medium is provided, which stores at least one program code, and the at least one program code is loaded and executed by a processor to implement operations performed by the method.

[0032] According to a fourth aspect of the embodiments of the present application, an electronic device is provided, which comprises one or more processors and one or more memories, and the one or more memories store at least one program code, and the at least one program code is loaded and executed by the one or more processors to implement operations performed by the method.

[0033] The advantages of the above-mentioned second aspect to fourth aspect and each embodiment of the first aspect are as described above, which will not be repeated here.

[0034] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0035] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the application and, together with the description, further serve to explain the principles of the application. It is to be understood that the drawings are only schematic, and that they do not necessarily represent a limiting

[0036] Figure 1 A flow chart of a method for quantitatively calculating strike-slip displacement in an embodiment of the application is shown;

[0037] Figure 2 A strike-slip structure pattern recognition diagram in an embodiment of the application is shown;

[0038] Figure 3 A strike-slip fracture plane distribution diagram in an embodiment of the application is shown;

[0039] Figure 4 A flow chart of a method for obtaining attribute data and a feature vector in an embodiment of the application is shown;

[0040] Figure 5 Similarities between various geological bodies in an embodiment of the application are shown;

[0041] Figure 6 A structural schematic diagram of a device for quantitatively calculating strike-slip displacement in an embodiment of the application is shown;

[0042] Figure 7 A structural schematic diagram of an electronic device in an embodiment of the application is shown. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only some of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the application.

[0044] In addition, the described features, structures, or characteristics can be combined in any suitable way in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One of ordinary skill in the relevant art will recognize, however, that the application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known structures, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.

[0045] The block diagrams shown in the drawings are merely functional entities, and do not necessarily correspond to physically independent entities. That is, the functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0046] The flowcharts shown in the drawings are merely illustrative, and do not necessarily include all contents and operations / steps, nor are they necessarily executed in the order described. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to actual conditions.

[0047] Next, the present application will be described in detail:

[0048] Figure 1 A flowchart of a quantitative calculation of strike-slip displacement method in an embodiment of the present application is shown. The quantitative calculation of strike-slip displacement method can be executed by a device with calculation processing function, such as a quantitative calculation of strike-slip displacement device. Referring to Figure 1 The quantitative calculation of strike-slip displacement method includes at least steps 110 to 160, which are described in detail as follows:

[0049] Step 110, based on three-dimensional seismic data, a target area seismic work area model is established, and the strike-slip structural pattern in the target area seismic work area model is determined through coherent slice, curvature analysis and artificial intelligence fracture identification.

[0050] Step 120, according to the seismic reflection characteristics corresponding to the strike-slip structural pattern, drilling data, and logging data, the target layer horizon of the three-dimensional seismic data is calibrated, and the planar distribution of the strike-slip fault of the target layer is determined.

[0051] Step 130, according to the planar distribution of the strike-slip fault and the corresponding real drilling data on both sides of the strike-slip fault, the sedimentary system on both sides of the strike-slip fault is determined, and the sand body on both sides of the strike-slip fault is depicted by reservoir inversion, so as to determine the distribution of the sand body on both sides of the strike-slip fault, and each sand body on both sides of the strike-slip fault is taken as a geological body.

[0052] Step 140, the attribute data body corresponding to each geological body is obtained, which includes seismic root mean square amplitude, single frequency seismic attribute, wave impedance inversion attribute, heavy mineral proportion index and sedimentary stratum sand ratio.

[0053] Step 150, according to the attribute data body, the information entropy, central tendency, dispersion coefficient, average joint entropy and average mutual information corresponding to each attribute data body are obtained, so as to obtain the feature vector corresponding to each geological body.

[0054] Step 160, according to the feature vector, the similarity between each geologic body is calculated, and the highest similarity corresponding to the two geologic body centroid coordinates in each similarity is determined, so as to calculate the strike-slip displacement amount based on the centroid coordinates.

[0055] In the present application, referring to Figure 2 , the strike-slip structure pattern recognition diagram in the present application is shown. On the basis of establishing the target area seismic work area model based on three-dimensional seismic data, the strike-slip structure pattern in the target area seismic work area model is determined by means of coherence slice, curvature analysis and artificial intelligence fracture identification. Since the recognition of strike-slip structure pattern is the premise of calculating strike-slip displacement amount in the present application, therefore, whether the strike-slip structure pattern is accurate or not will directly affect the accuracy of the calculation of strike-slip displacement amount.

[0056] Referring to Figure 3 , the strike-slip fracture plane distribution diagram in the present application is shown. After accurately determining the strike-slip structure pattern, according to the seismic reflection characteristics corresponding to the current strike-slip structure pattern, drilling data, and logging data, the target layer horizon of the three-dimensional seismic data is calibrated, so as to determine the plane distribution of the strike-slip fracture of the target layer. Then, according to the plane distribution of the strike-slip fracture and the corresponding real drilling data on both sides of the strike-slip fracture, the sedimentary system on both sides of the strike-slip fracture is determined, and the sand body on both sides of the strike-slip fracture is described by reservoir inversion, so as to determine the distribution of the sand body on both sides of the strike-slip fracture. In the present application, each sand body on both sides of the strike-slip fracture is regarded as a separate geologic body.

[0057] For example, in Figure 3 , after 9 sand bodies are identified on both sides of the strike-slip fracture, the 9 sand bodies are regarded as 9 geologic bodies. In addition, the geologic bodies can be divided into upper plate geologic bodies and lower plate geologic bodies. The upper plate geologic bodies are D i (i=1, 2, 3…m), and the lower plate geologic bodies are D i '(i=1, 2, 3…m). It can be understood that if the current stratum occurs strike-slip, the strike-slip displacement amount is the distance between the upper plate geologic body and the lower plate geologic body corresponding to each other.

[0058] Referring to Figure 4, a flow chart for acquiring attribute data bodies and feature vectors in the embodiments of the present application is shown. After each sand body on both sides of the strike-slip fracture is taken as a geological body, based on the three-dimensional seismic data, drilling data, logging data and analysis and testing data of the target area seismic work area model, the feature data representing the geological body are acquired, so as to take the feature data representing the geological body as the attribute data bodies corresponding to each geological body. The attribute data bodies include seismic root mean square amplitude, single frequency seismic attribute, wave impedance inversion attribute, heavy mineral proportion index and sedimentary stratum sand ratio. Continuing to refer to Figure 4 After the attribute data bodies corresponding to each geological body are determined, attribute discretization processing is performed on the attribute data bodies, so as to calculate the feature vectors of the geological bodies, wherein the feature vectors include information entropy, central tendency, dispersion coefficient, average joint entropy and average mutual information.

[0059] For example, the information entropy of the geological body D1 is 0.8791, the central tendency is 0.3511, the dispersion coefficient is 0.3031, the average joint entropy is 1.0557 and the average mutual information is 0.1939.

[0060] For example, the information entropy of the geological body D2 is 0.9928, the central tendency is 0.4618, the dispersion coefficient is 0.1657, the average joint entropy is 2.1653 and the average mutual information is 0.4818.

[0061] For example, the information entropy of the geological body D3 is 0.971, the central tendency is 0.5, the dispersion coefficient is 0, the average joint entropy is 2.5892 and the average mutual information is 0.6374.

[0062] It should be noted that the feature vector is a series of feature indexes for data mining and can reflect the data characteristics of the data body itself. Since the number of attributes, the number of data points, the data values and the nature and background meaning of the data bodies are different, the similarity between two data bodies cannot be directly compared. The general solution to this problem is to ignore the specific application background of the data body, regard it as a simple mathematical model, extract feature parameters that can represent the internal association and overall properties of the data, combine to obtain the feature vector of the data body, and determine the similarity between the data bodies by comparing the feature vectors.

[0063] Further, in information theory, information entropy refers to the measure of information uncertainty, represents the average amount of information after excluding redundancy, and is an abstract concept. Therefore, the information entropy can be understood as the probability of the occurrence of a certain specific information. The information entropy is calculated by the following formula:

[0064]

[0065] wherein H(X) is the information entropy; p(xi ) is the density estimation of X; X is a random variable in the data set corresponding to each attribute data body.

[0066] Further, the central tendency is calculated by the following formula:

[0067]

[0068] wherein, is the central tendency; x i is any one value in the data set corresponding to each attribute data body; n is the number of data in the data set corresponding to the current attribute data body.

[0069] In some embodiments of the present application, based on the foregoing scheme, the dispersion coefficient is calculated by the following formula:

[0070]

[0071] wherein, s is the dispersion coefficient, used to describe the degree of data deviating from the center; is the central tendency; x n is any one value in the data set corresponding to each attribute data body; n is the number of data in the data set corresponding to the current attribute data body.

[0072] Further, the average joint entropy is calculated by the following formula:

[0073]

[0074] wherein, H(X, Y) is the average joint entropy, used to represent the amount of entropy in a system with two random variables; p(x, y) is the probability of each pair of variables occurring simultaneously; (X, Y) is the entropy of the system of random variables X and Y in combination.

[0075] Further, the average mutual information is used to measure the dependence degree between different attributes of the data set. The mutual information of two arbitrary variables is a quantity to measure the mutual dependence between the two variables. Generally, for two non-continuous variables X and Y, the average mutual information is calculated by the following formula:

[0076]

[0077] wherein, I(X; Y) is the average mutual information, used to measure the dependence degree between different attributes of the data set; p(x, y) is the probability of each pair of variables occurring simultaneously; (X, Y) is the entropy of the system of random variables X and Y in combination.

[0078] Referring to Figure 5, the similarity between each geologic body in the embodiment of the application is shown. After the feature vectors of each geologic body are obtained through the above steps 110 to 150, the similarity of the geologic bodies on both sides of the strike-slip fault is calculated. The feature of each geologic body corresponds to a feature vector, so the similarity of the geologic bodies is essentially the calculation of the distance or the included angle between vectors, and the two vectors with the smallest distance or included angle represent the highest similarity of the data sets.

[0079] Further, the similarity is calculated by the following formula:

[0080]

[0081] wherein d i ={W i-熵 ,W i-中心趋势 ,W i-离散系数 ,W i-平均联合熵 , W i-平均互信息量}; d j ={W j-熵 ,W j-中心趋势 ,W j-离散系数 ,W j-平均联合熵 , W j-平均互信息量}; d i and d j are the feature vectors of the geologic bodies on both sides of the strike-slip fault.

[0082] After the similarity of each geologic body is calculated, the barycentric coordinates of the two geologic bodies corresponding to the highest similarity in each similarity are determined, so as to calculate the strike-slip displacement amount based on the barycentric coordinates.

[0083] Specifically, continuing to refer to Figure 5 For example, the geologic bodies corresponding to the highest similarity calculated are geologic body 3 and geologic body 7. The barycentric coordinates of geologic body 3 are C(x, y, z), and the barycentric coordinates of geologic body 7 are C'(x', y', z'). Then the strike-slip displacement amount can be calculated according to the strike-slip displacement amount calculation formula.

[0084] Further, the strike-slip displacement amount is calculated by the following formula:

[0085]

[0086] Based on the same inventive concept, the application further provides a device for quantitatively calculating a strike-slip displacement amount, which refers to Figure 6, a structure diagram of a quantitative calculation strike-slip displacement amount device in an embodiment of the present application is shown. The quantitative calculation strike-slip displacement amount device 600 comprises: a data input and mapping unit 601, configured to establish a target area seismic work area model based on three-dimensional seismic data, determine a strike-slip structure form in the target area seismic work area model, and determine a planar distribution of a strike-slip fault of a target layer according to a corresponding seismic reflection feature of the strike-slip structure form, drilling data, and logging data, so as to determine a sedimentary system on both sides of the strike-slip fault according to the planar distribution of the strike-slip fault and corresponding real drilling data on both sides of the strike-slip fault, further depict sand bodies on both sides of the strike-slip fault through reservoir inversion, determine a distribution of the sand bodies on both sides of the strike-slip fault, and take each sand body on both sides of the strike-slip fault as a geological body; a feature extraction unit 602, configured to obtain attribute data bodies corresponding to each geological body, the attribute data bodies comprising a seismic root mean square amplitude, a single-frequency seismic attribute, a wave impedance inversion attribute, a heavy mineral proportion index, and a sedimentary stratum sand ratio, and obtain information entropy, a central tendency, a dispersion coefficient, an average joint entropy, and an average mutual information amount corresponding to each attribute data body according to the attribute data bodies; a calculation unit 603, configured to calculate a feature vector corresponding to each geological body according to the information entropy, the central tendency, the dispersion coefficient, the average joint entropy, and the average mutual information amount corresponding to each attribute data body; and a strike-slip displacement amount determination unit 604, configured to calculate a similarity of geological bodies on both sides of the strike-slip fault according to the feature vector, determine a barycentric coordinate of two geological bodies corresponding to a highest similarity, and calculate a strike-slip displacement amount based on the barycentric coordinate.

[0087] For details not disclosed in the device embodiments of the present application, refer to the above-mentioned method embodiments of the present application.

[0088] Based on the same inventive concept, the present application can also provide a computer readable storage medium, which stores at least one program code, the at least one program code is loaded and executed by a processor to implement the operations performed by the method

[0089] Based on the same inventive concept, the present application further provides an electronic device, which refers to Figure 7 , Figure 7 A structure diagram of an electronic device in an embodiment of the present application is shown.

[0090] The electronic device comprises one or more memories 704, one or more processors 702, and at least one computer program (program code) stored in the memory 704 and executable on the processor 702, and the processor 702 executes the computer program to implement the method as described above.

[0091] Among them, in Figure 7In particular embodiments, bus architecture (represented generally by the bus 700) can include any number of interconnecting buses and bridges, the bus 700 linking together various circuits such as the processor 702 represented by one or more processors and the memory 704 represented by the memory. The bus 700 can also link together various other circuits which are well described in the art, thus, the bus 700, in further embodiments, is not limited to the scope of this description. The bus interface 705 provides an interface between the bus 700 and the receiver 701 and the transmitter 703. The receiver 701 and the transmitter 703 can be the same component, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 702 is responsible for managing the bus 700 and general processing, while the memory 704 can be used for storing data used by the processor 702 in executing operational processes.

[0092] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions can be stored on or transferred over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions can also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations. Also, as technology evolves, the "functionalities" described can be implemented by different hardware components at a later time.

[0093] In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other manners. The described device embodiments are merely illustrative, and the division of units can be different from the above. For example, the units can be combined or integrated into another system, or some features can be ignored or not implemented. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, and can be in electrical, mechanical or other forms.

[0094] The units described as separate components can or can not be physically separate, and the components of the control device can or can not be physical units, i.e., they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0095] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk and various media that can store program codes.

[0096] The above only describes the embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A method for quantitatively calculating an amount of strike-slip displacement, characterized by, The method comprises: Based on three-dimensional seismic data, a seismic work area model of a target area is established, and a strike-slip structure pattern in the seismic work area model of the target area is determined through coherent slices, curvature analysis, and artificial intelligence fracture identification; According to seismic reflection characteristics corresponding to the strike-slip structure pattern, drilling data, and logging data, target layer horizon calibration is performed on three-dimensional seismic data, and the planar distribution of strike-slip faults of the target layer is determined; According to the planar distribution of the strike-slip faults and corresponding real drilling data on both sides of the strike-slip faults, a sedimentary system on both sides of the strike-slip faults is determined, and reservoir inversion is used to depict sand bodies on both sides of the strike-slip faults, so as to determine the distribution of the sand bodies on both sides of the strike-slip faults, and each sand body on both sides of the strike-slip faults is taken as a geological body; Attribute data bodies corresponding to each geological body are obtained, the attribute data bodies including seismic root mean square amplitude, single-frequency seismic attribute, wave impedance inversion attribute, heavy mineral proportion index, and sedimentary stratum sand ratio; According to the attribute data bodies, information entropy, central tendency, dispersion coefficient, average joint entropy, and average mutual information corresponding to each attribute data body are obtained, so as to obtain a feature vector corresponding to each geological body; According to the feature vector, the similarity between each geological body is calculated, and the barycentric coordinates of two geological bodies corresponding to the highest similarity in each similarity are determined, so as to calculate a strike-slip displacement amount based on the barycentric coordinates.

2. The method of claim 1, wherein, The information entropy is calculated by the following formula: where H(X) is the information entropy; p(x i ) is the density estimation of X; X is a random variable in the data set corresponding to each attribute data body.

3. The method of claim 1, wherein, The central tendency is calculated by the following formula: wherein is the central tendency; x i is any one value in the data set corresponding to the respective attribute data body; n is the number of data in the data set corresponding to the current attribute data body.

4. The method of claim 1, wherein, The dispersion coefficient is calculated by the following formula: where s is a dispersion coefficient, used to describe the degree of data deviating from the center; is the central tendency; x n is any one value in the data set corresponding to the individual attribute data body; n is the number of data in the data set corresponding to the current attribute data body.

5. The method of claim 1, wherein, The average joint entropy is calculated by the following formula: Wherein, H(X,Y) is the average joint entropy, used to represent the entropy of a system with two random variables; p(x,y) is the probability of each pair of variables occurring simultaneously; (X,Y) is the entropy of the system of random variables X and Y.

6. The method of claim 1, wherein, The average mutual information is calculated by the following formula: Wherein, I(X;Y) is the average mutual information, used to measure the dependence degree between different attributes of data sets; p(x,y) is the probability of each pair of variables occurring simultaneously; (X,Y) is the entropy of the system of random variables X and Y.

7. The method of claim 1, wherein, The similarity is calculated by the following formula: where d i = {W i-熵 ,W i-中心趋势 ,W i-离散系数 ,W i-平均联合熵， W i-平均互信息量}; d j = {W j-熵 ,W j-中心趋势 ,W j-离散系数 ,W j-平均联合熵， W j-平均互信息量}; d i and d j are the eigenvectors of the geological bodies on both sides of the strike-slip fault, respectively.

8. A device for quantitatively calculating slip displacement, characterized in that, The device comprises: A data input and mapping unit is configured to establish a seismic work area model of a target area based on three-dimensional seismic data, determine a strike-slip structure pattern in the seismic work area model of the target area, and determine the planar distribution of strike-slip faults of a target layer according to seismic reflection characteristics corresponding to the strike-slip structure pattern, drilling data, and logging data, so as to determine a sedimentary system on both sides of the strike-slip faults according to the planar distribution of the strike-slip faults and corresponding real drilling data on both sides of the strike-slip faults, and further determine the distribution of sand bodies on both sides of the strike-slip faults by reservoir inversion depicting the sand bodies on both sides of the strike-slip faults, and take each sand body on both sides of the strike-slip faults as a geological body. The feature extraction unit is configured to obtain attribute data bodies corresponding to the geological bodies, the attribute data bodies including seismic root mean square amplitudes, single-frequency seismic attributes, wave impedance inversion attributes, heavy mineral proportion indexes, and sedimentary formation sandstone-to-strata ratios, and obtain information entropy, central tendency, dispersion coefficient, average joint entropy, and average mutual information corresponding to each attribute data body according to the attribute data bodies; The calculation unit is configured to calculate feature vectors corresponding to the geological bodies according to the information entropy, central tendency, dispersion coefficient, average joint entropy, and average mutual information corresponding to each attribute data body. The strike-slip displacement determination unit is configured to calculate the similarity of the geological bodies on both sides of the strike-slip fault according to the feature vectors, determine the barycentric coordinates of two geological bodies corresponding to the highest similarity, and calculate the strike-slip displacement based on the barycentric coordinates.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the operations performed by the method of any one of claims 1 to 7.

10. An electronic device, comprising: The electronic device includes one or more processors and one or more memories, and the one or more memories store at least one program code, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the method of any one of claims 1 to 7.

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