Seismic data-based reservoir breaking joint body identification method and apparatus, and electronic device

Abstract

The invention relates to the technical field of breaking joint recognition, in particular to a reservoir breaking joint body recognition method and device based on seismic data and electronic equipment, and the method comprises the steps: obtaining a seismic data body set of a region to be recognized, carrying out the similarity recognition of seismic data bodies based on coherence analysis, and tracking and outputting breaking joint data bodies; and carrying out crack characterization on the breaking joint data body by utilizing the fracture and crack characterization sample library. According to the method, the breaking joint data body is tracked and extracted from the seismic data body based on coherence analysis, and the fracture and fracture representation sample library is utilized to perform fracture representation on the breaking joint data body, so that the reservoir breaking joint body can be effectively identified, the identification and representation accuracy of the reservoir breaking joint body is improved, and the drilling risk early warning precision is improved.

Description

Technical Field

[0001] The invention relates to the technical field of fault identification, and is a reservoir fault body identification method, device and electronic equipment based on seismic data. Background Art

[0002] The study of fracture bodies is of great significance in the field of oil and gas field exploration and development. As oil and gas exploration and development shift from conventional oil and gas reservoirs to complex oil and gas reservoirs such as tight sandstone, complex carbonate rock, volcanic rock, and shale, these oil and gas reservoirs all involve fracture prediction. In fractured oil and gas reservoirs, due to the action of directional tectonic stress, natural fractures usually present a directional arrangement phenomenon. The orientation, development density, fillings and other fractures of the fracture body are closely related to oil and gas migration, accumulation, accumulation, and reserves. In addition, fractures are also one of the main evaluation parameters of the "sweet spot" of reservoir engineering, so the quantitative prediction and evaluation of reservoir fractures is an important part of oil and gas exploration and development. However, there is no effective method for identifying reservoir fracture bodies in actual production. Therefore, major oil fields urgently need a method for identifying reservoir fracture bodies based on seismic data to achieve fine characterization of fracture bodies and improve the accuracy of risk warning for fracturing transformation and drilling and completion. Summary of the invention

[0003] The present invention provides a reservoir fault body identification method, device and electronic equipment based on seismic data, which overcomes the deficiencies of the above-mentioned prior art and can effectively solve the problem that the prior art cannot effectively identify reservoir fault bodies.

[0004] One of the technical solutions of the present invention is achieved by the following measures: a reservoir fault body identification method based on seismic data, comprising:

[0005] Obtain a set of seismic data volumes in the area to be identified, perform similarity identification on the seismic data volumes based on coherence analysis, and track and output the fault data volumes;

[0006] The fracture and crack characterization sample library is used to characterize the fracture data volume.

[0007] The following are further optimizations and / or improvements to the above technical solutions:

[0008] The above method obtains a set of seismic data volumes of the area to be identified, identifies similarity of the seismic data volumes based on coherence analysis, and tracks and outputs the fault data volumes, including:

[0009] Obtaining a set of seismic data volumes of the area to be identified, and preprocessing the set of seismic data volumes;

[0010] The coherence volume technique based on multi-channel similarity determines the similarity of adjacent channels in a seismic data volume set;

[0011] Scan the slope and similar bodies in the seismic data volume set to determine the location and direction of the fault body;

[0012] Extract and retain the local maximum values ​​in similar data volumes and output the fracture data volume.

[0013] The above-mentioned method of obtaining a set of seismic data volumes of the area to be identified and preprocessing the set of seismic data volumes includes:

[0014] Obtaining a set of seismic data volumes of the area to be identified, wherein the set of seismic data volumes of the area to be identified includes a plurality of seismic data volumes of the area to be identified;

[0015] Search each seismic data volume, remove the null / invalid values ​​in the seismic data volume, and extract the valid seismic data volume;

[0016] Digitally filtering the extracted seismic data volume;

[0017] The slope of each seismic data volume is calculated using the following formula, the seismic traces are aligned, and the slopes of the main survey line and the tie line are output, where:

[0018] The formula for the slope of a straight line passing through two points P1(x1,y1) and P2(x2,y2) is:

[0019]

[0020] The above-mentioned scanning of the slope and similar bodies in the seismic data volume set to determine the location and direction of the fault body includes:

[0021] (i) Azimuth estimation and analysis to determine the location of the fracture body;

[0022] azi=f{arctan{|e 13 | / |e 12 |}}

[0023] (ii) estimation and analysis of the inclination angle to determine the direction of the fracture body;

[0024]

[0025] Among them, P x is the reflection slope in the x direction; P y is the corresponding slope in the y direction.

[0026] The above-mentioned scanning of the slope and similar bodies in the seismic data volume set to determine the location and direction of the fault body includes:

[0027] (i) Azimuth estimation and analysis to determine the location of the fracture body;

[0028] azi=f{arctan{|e 13 | / |e12 |}}

[0029] (ii) estimation and analysis of the inclination angle to determine the direction of the fracture body;

[0030]

[0031] Among them, P x is the reflection slope in the x direction; P y is the corresponding slope in the y direction.

[0032] The second technical solution of the present invention is achieved by the following measures: a reservoir fault body identification device based on seismic data, comprising:

[0033] A similarity recognition unit obtains a set of seismic data volumes in the area to be recognized, performs similarity recognition on the seismic data volumes based on coherence analysis, and tracks and outputs the fracture data volumes;

[0034] The fracture characterization unit uses the fracture and fracture characterization sample library to perform fracture characterization on the fault data volume.

[0035] The following are further optimizations and / or improvements to the above technical solutions:

[0036] The similarity recognition unit includes:

[0037] A preprocessing module, which obtains a set of seismic data volumes of the area to be identified and preprocesses the set of seismic data volumes;

[0038] A similarity calculation module determines the similarity of adjacent traces in a seismic data volume set based on the multi-channel similarity coherence volume technology;

[0039] The first analysis module scans the slope and similar bodies in the seismic data volume set to determine the location and direction of the fault body;

[0040] The second analysis module extracts and retains the local maximum values ​​in the similar data volume and outputs the fracture data volume.

[0041] The above-mentioned preprocessing modules include:

[0042] A data acquisition module is used to obtain a set of seismic data volumes of the area to be identified, wherein the set of seismic data volumes of the area to be identified includes a plurality of seismic data volumes of the area to be identified;

[0043] The first preprocessing module searches each seismic data volume, removes null values / invalid values ​​in the seismic data volume, and extracts valid seismic data volumes;

[0044] The second preprocessing module performs digital filtering on the extracted seismic data volume;

[0045] The slope calculation module calculates the slope of each seismic data volume using the following formula, aligns the seismic traces, and outputs the slopes of the main survey line and the tie line direction, where:

[0046] The formula for the slope of a straight line passing through two points P1(x1,y1) and P2(x2,y2) is:

[0047]

[0048] The present invention tracks and extracts the fault data body in the seismic data body based on coherence analysis, and uses the fracture and crack characterization sample library to perform fracture characterization on the fault data body, so that the fault body identification of the reservoir is more effective and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Attached Figure 1 The present invention is a schematic flow chart of a method.

[0050] Attached Figure 2 This is a maximum likelihood calculation process diagram of the present invention.

[0051] Attached Figure 3 The present invention is another method flow chart.

[0052] Attached Figure 4 It is a schematic diagram of the device structure of the present invention.

[0053] Attached Figure 5 It is a structural schematic diagram of the similarity recognition unit in the present invention.

[0054] Attached Figure 6 It is a structural schematic diagram of the preprocessing module in the present invention. DETAILED DESCRIPTION

[0055] The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

[0056] The present invention will be further described below in conjunction with embodiments and drawings:

[0057] Embodiment 1: As attached Figure 1 As shown, the embodiment of the present invention discloses a reservoir fault body identification method based on seismic data, comprising:

[0058] Step S110, obtaining a set of seismic data volumes of the area to be identified, performing similarity identification on the seismic data volumes based on coherence analysis, and tracing and outputting the fracture data volumes;

[0059] Step S120 , using the fracture and crack characterization sample library to perform crack characterization on the fracture data volume.

[0060] The coherence analysis used in the above steps uses the coherence coefficient or coherence function to describe the mutual relationship or similarity between two signals. It can also be used to describe the relationship between the present value and the past value of the same signal, or to estimate the future value based on the past value and the present value. The properties of the coherence function make it of great value in the exploration and development of oil and gas fields, especially the coherence of the same frequency and the uncorrelation of different frequencies of the mutual coherence function, which provides a reliable way to extract useful information in the background of noise.

[0061] To improve the coherence effect, similarity is replaced by window-based directional averaging:

[0062]

[0063] in, s To represent the structural orientation smoothing of the seismic data body in brackets, the likelihood is defined as the opposite of the similarity, so the likelihood is the largest when the similarity is the smallest. The algorithm calculates the similarity in the elongated directional window and keeps the result with the smallest similarity. That is, the likelihood attribute can also be understood as the possibility of fault development at the sample point. The larger the similarity attribute Semblance is when the seismic phase axis is continuous, the smaller the likelihood attribute is, that is, the smaller the possibility of fault development is; at the breakpoint position, the seismic continuity becomes worse, the Semblance becomes smaller, and the likelihood attribute becomes larger, that is, the possibility of the existence of a fault increases.

[0064] This can be as attached Figure 2 In two-dimensional form:

[0065] The similarity is calculated for a discrete set of tilt directions and is given in the following Figure 2 The maximum likelihood is found in -c. It is calculated for each point in the image and the maximum likelihood value and the direction of the maximum likelihood value are stored. In 3D, each object is tested for a range of orientations, and instead of being calculated on a 2D rectangular shape, it is now a 3D rectangular volume oriented at different inclinations and orientations.

[0066] Attached Figure 2 Represents the maximum likelihood calculation process for a single point in two dimensions.

[0067] (a) The blue line is the fault, and the black dots are the locations where the likelihood is calculated;

[0068] (b) Similarity is calculated within one direction of the window;

[0069] (c) The direction with the minimum similarity.

[0070] The calculated attributes can detect and highlight fractures even if the seismic attributes are locally continuous. These fault attributes are more continuous and "fracture-like". Therefore, the likelihood volume, as a representative of fractures, helps to more effectively identify the development of fractures in unconventional reservoirs and provide a better basis for seismic exploration.

[0071] The present invention discloses a reservoir fault body identification method based on seismic data. The fault data body is tracked and extracted in the seismic data body based on coherence analysis, and the fracture characterization sample library is used to perform fracture characterization on the fault data body, so that the fault body identification of the reservoir is more effective and accurate.

[0072] Embodiment 2: As attached Figure 3 As shown, the embodiment of the present invention discloses a reservoir fault body identification method based on seismic data, comprising:

[0073] Step S210, obtaining a set of seismic data volumes of the area to be identified, and preprocessing the set of seismic data volumes.

[0074] This step specifically includes:

[0075] (1) obtaining a set of seismic data volumes of the area to be identified, wherein the set of seismic data volumes of the area to be identified includes a plurality of seismic data volumes of the area to be identified;

[0076] (2) Search each seismic data volume, remove the null values / invalid values ​​in the seismic data volume, and extract the valid seismic data volume;

[0077] This step searches the entire seismic data volume to remove null / invalid values ​​in the seismic data volume. Each sample point is one of two values: +2 = valid and has a value, -2 = no value and the sample point is invalid. This process also creates a breakpoint clear volume, that is, firstly, the null value is written as a "0" value by reading and writing the original input volume, and then finds the continuous areas with RMS amplitude values ​​less than 1% in the top and bottom areas, and then changes their values ​​to zero and writes them into the data volume as null values;

[0078] (3) digitally filtering the extracted seismic data volume;

[0079] This step helps to stabilize the similarity attribute calculation. Longer vertical smoothing lengths highlight structural faults, and shorter time windows are more suitable for highlighting stratigraphic features such as river channels.

[0080] The purpose of data volume filtering is to suppress noise. After the discrete signal passes through the designed system, the output signal is the effect of removing or suppressing noise. Assuming that the input signal consists of valid signals and interference signals, the noise is basically removed after digital filtering. Data volume filtering mainly refers to digital filtering of seismic data volumes. First, discrete sampling (sampling) is required. The sampling process must satisfy the sampling theorem, otherwise the spectrum will be aliased and false frequencies will be generated. The sampling theorem can be described by the following two formulas:

[0081] (a) Frequency domain:

[0082] ω s =2ω N ≥2ω max

[0083] Among them, ω s is the sampling frequency, ω N is the folding frequency, ω max is the highest frequency of the signal;

[0084] (b) Time domain:

[0085]

[0086] Where Δt is the sampling interval, f max is the highest frequency of the signal;

[0087] There are many kinds of data volume digital filters. The most commonly used in digital filtering is the linear time-invariant system. Its linear characteristics refer to the simultaneous satisfaction of superposition and homogeneity, which can be expressed by the following formula:

[0088]

[0089] The time-invariant characteristic is as follows:

[0090]

[0091] Among them, τ is the delay.

[0092] (4) The slope of each seismic data volume is calculated using the following formula, which will be used to align the seismic traces so that any related events are horizontal, which makes the similarity calculation results more stable. At the same time, the slope volume of the main survey line and the tie line direction is output, where:

[0093] The formula for the slope of a straight line passing through two points P1(x1,y1) and P2(x2,y2) is:

[0094]

[0095] In the above formula, the slope of a straight line can be expressed by the coordinates of any two points on the straight line, and there is no need to calculate the inclination angle of the straight line; when x1=x2, the formula is not applicable, and a=90°.

[0096] Step S220, determining the similarity of adjacent traces in the seismic data volume set based on the multi-trace similarity coherence volume technology.

[0097] The above-mentioned coherence volume technology based on multi-channel similarity determines the similarity of adjacent channels in the seismic data volume set, which is the calculation of similar numerator and denominator. By calculating the similarity of adjacent channels in a small range of sample points (3 channels in the two-dimensional direction and 9 channels in the three-dimensional direction), two data volumes (similar numerator volume and similar denominator volume) will be output to ensure the stability of the numerical value. These two data volumes are recorded in 32-bit floating point.

[0098] The coherence volume technology based on multi-channel similarity refers to defining an elliptical or rectangular analysis time window centered at the analysis point and containing J channels. The coherence quality C2 is the ratio of the energy of the average channel in the analysis time window to the energy of all channels, that is:

[0099]

[0100] Where: subscript j represents the jth channel falling within the analysis time window; x j and j It represents the distance between the jth track and the center point t in the analysis window in the x and y directions; p and q represent the apparent inclination angles in the x and y directions of the local reflection interface where the center point in the analysis window is located, respectively.

[0101] The algorithm based on multi-channel similarity can calculate the correlation of any multi-channel seismic data. The quality limit of seismic data is not very strict, and the coherence, dip and azimuth of noisy data can be calculated more accurately. Using an appropriately sized analysis window can better solve the contradiction between improving resolution and signal-to-noise ratio.

[0102] Step S230, scanning the slope and similar bodies in the seismic data body set to determine the position and direction of the fault body.

[0103] In this step, the slope and similarity volume scans help determine the location and orientation of the fault. The output volumes of this step are "fault likelihood volume", "fault strike volume" and "fault dip volume". The fault likelihood volume looks like a low-frequency extension of the possible fault location and orientation. The calculation steps are:

[0104] (I) Azimuth estimation analysis

[0105] (a) Perform standard deviation normalization on the three-dimensional array A1: A2 = ZScore(A1). And let

[0106] The purpose of using ZScore standardization is to get a mean of 0 and a standard deviation of 1, so that data of different dimensions can be placed in the same matrix.

[0107] (b) Find the correlation coefficient matrix of array A2:

[0108]

[0109] Among them: (i, j = 1, 2, 3) is the original variable a2 i With a2 j The correlation coefficient is calculated as follows:

[0110]

[0111] Among them, r ij =r ji , R3 is a third-order symmetrical square matrix;

[0112] (c) Calculate the eigenvalues ​​and eigenvectors of R3:

[0113] Use Jacobi method to find the eigenvalues ​​and arrange them in order of size, λ1≥λ2≥λ3, and then find their corresponding eigenvectors e1, e2, e3, e1=(e 11 , e 12 , e 13 ), e 12 =(e 21 , e 22 , e 23 ),

[0114] e3=(e 31 , e 32 , e 33 ), requiring ||e i ||=1, that is According to this requirement, the eigenvector is unique;

[0115] (d) Calculate the epicenter azimuth:

[0116] azi=f{arctan{|e 13 | / |e 12 |}}, specifically:

[0117] When e 11 <0,e 12 <0,e 13 <0, azimuth = arctan{|e 13 | / |e 12 |}

[0118] When e 11 <0,e12 <0,e 13 <0, azimuth angle azi=π-arctan{|e 13 | / |e 12 |}

[0119] When e 11 <0,e 12 <0,e 13 <0, azimuth angle azi=π+arctan{|e 13 | / |e 12 |}

[0120] When e 11 <0,e 12 <0,e 13 <0, azimuth angle azi=2π-arctan{|e 13 | / |e 12 |}

[0121] When e 11 >0,e 12 >0,e 13 >0, azimuth angle azi=π+arctan{|e 13 | / |e 12 |}

[0122] When e 11 >0,e 12 >0,e 13 >0, azimuth angle azi=2π-arctan{|e 13 | / |e 12 |}

[0123] When e 11 >0,e 12 >0,e 13 >0, azimuth angle azi=arctan{|e 13 | / |e 12 |}

[0124] When e 11 >0,e 12 >0,e 13 >0, azimuth angle azi=π-arctan{|e 13 | / |e 12 |}

[0125] The epicenter azimuth is the angle between the prime meridian passing through the station and the line connecting the station to the epicenter, and the clockwise direction is defined as positive.

[0126] (II) Tilt Angle Estimation Analysis

[0127] Dip and azimuth record the reflection direction and are the basic attributes of 3D seismic. They are derived from the reflection slope in the x and y directions. The reflection slope in the x direction P x yes:

[0128]

[0129] The corresponding slope in the y direction, P y yes:

[0130]

[0131] The slope magnitude p is a combination of the slopes px and py, and the inclination data volume is calculated according to the following formula.

[0132]

[0133] Step S240, extracting and retaining the local maximum values ​​in the similar data volume, and outputting the fracture data volume.

[0134] This step extracts and retains the local maximum of each row in the likelihood volume and sets the fault likelihood to zero elsewhere. In effect, this process thins the tomographic image and reduces the number of image samples that may be considered to have a fault.

[0135] Step S250 , using the fracture and crack characterization sample library to perform crack characterization on the fracture data volume.

[0136] In this step, the sample library for characterization of fractures and cracks is formed through historical data (including drilling stratification comparison, acoustic time difference jump, low fracture pressure, fracturing water string channel, injection-production relationship), based on the changes in logging curve characteristics, mud loss analysis, pressure string analysis, etc. to clarify the vertical development position and planar distribution of small faults and fractures in the current drilling, and other information.

[0137] In this step, the fracture data volume is characterized by using the fracture and crack characterization sample library, that is, the fracture data volume is compared with the fracture and crack characterization sample library, and the fracture data volume is corrected to obtain the final fracture data volume characterization result.

[0138] In this embodiment, the seismic data volume is a three-dimensional seismic data volume, which is regarded as different two-dimensional seismic data surfaces, and the two-dimensional seismic data surfaces are divided into different grid units as the initial active boundaries of each search. In each grid unit, a search is performed according to a certain probability (the probability of discontinuities such as faults and cracks is high). The probability formula is:

[0139]

[0140] Where: P i is the probability; C i, C J are the normalized coherence values ​​of the ith and jth data points, respectively.

[0141] After the starting point of crack identification is determined, the tracking direction needs to be determined. The formula is:

[0142]

[0143] Where: θ is the main direction of data block B; G x (i,j),G y (i,j) are the gradients of the data points in the data block respectively.

[0144] The next node that the crack identification moves to in the data block is determined by a certain probability formula. Its transition probability is:

[0145]

[0146] Where: τ(i,j), φ(i,j) are the information and heuristic information of (i,j) respectively; a, β are the control factors of information and heuristic information respectively, controlling the relative weights of pheromone and heuristic information;

[0147] The target data point of the search is determined according to the above formula, which is also the starting point of the next tracking. The search is within its initial separation range. After completing a search process, the information of each data point will be updated according to a certain algorithm:

[0148]

[0149]

[0150] Where: τ max is the upper limit of information; Δτ (i,j) is the increment of information on the data point (i, j) in this cycle; m is the data point Δτ in this cycle (i,j) (k) is the information left by the k-th search on the path of this cycle, and its value is proportional to the length of the searched path; C′ is the information update coefficient; L(K) is the length of the path searched by the k-th search in this cycle.

[0151] Each data block node is tracked along the fault, and finally the fault tracking of the entire 3D seismic data volume is completed to form a fault data volume.

[0152] Embodiment 3: As attached Figure 4 As shown, the embodiment of the present invention discloses a reservoir fault body identification device based on seismic data, comprising:

[0153] A similarity recognition unit obtains a set of seismic data volumes in the area to be recognized, performs similarity recognition on the seismic data volumes based on coherence analysis, and tracks and outputs the fracture data volumes;

[0154] The fracture characterization unit uses the fracture and fracture characterization sample library to perform fracture characterization on the fault data volume.

[0155] Embodiment 4: As attached Figure 5 As shown, the embodiment of the present invention discloses a reservoir fault body identification device based on seismic data. Further, the similarity identification unit includes:

[0156] A preprocessing module, which obtains a set of seismic data volumes of the area to be identified and preprocesses the set of seismic data volumes;

[0157] A similarity calculation module determines the similarity of adjacent traces in a seismic data volume set based on the multi-channel similarity coherence volume technology;

[0158] The first analysis module scans the slope and similar bodies in the seismic data volume set to determine the location and direction of the fault body;

[0159] The second analysis module extracts and retains the local maximum values ​​in the similar data volume and outputs the fracture data volume.

[0160] Among them, as attached Figure 6 As shown, the preprocessing module includes:

[0161] A data acquisition module is used to obtain a set of seismic data volumes of the area to be identified, wherein the set of seismic data volumes of the area to be identified includes a plurality of seismic data volumes of the area to be identified;

[0162] The first preprocessing module searches each seismic data volume, removes null values / invalid values ​​in the seismic data volume, and extracts valid seismic data volumes;

[0163] The second preprocessing module performs digital filtering on the extracted seismic data volume;

[0164] The slope calculation module calculates the slope of each seismic data volume using the following formula, aligns the seismic traces, and outputs the slopes of the main survey line and the tie line direction, where:

[0165] The formula for the slope of a straight line passing through two points P1(x1,y1) and P2(x2,y2) is:

[0166]

[0167] Embodiment 7: The embodiment of the present invention discloses a storage medium, on which a computer program readable by a computer is stored, and the computer program is configured to execute a reservoir fault body identification method based on seismic data when it is run.

[0168] The above storage medium may include, but is not limited to: a USB flash drive, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0169] Embodiment 8: The embodiment of the present invention discloses an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement a reservoir fault body identification method based on seismic data.

[0170] The processor may be a central processing unit (CPU), a general purpose processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. It may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The memory may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk.

[0171] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0172] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0173] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0174] The above technical features constitute the best embodiment of the present invention, which has strong adaptability and best implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims

1. A reservoir fault body identification method based on seismic data, characterized in that: include: Obtain a set of seismic data volumes in the area to be identified, perform similarity identification on the seismic data volumes based on coherence analysis, and track and output the fault data volumes; The fracture and crack characterization sample library is used to characterize the fracture data volume.

2. The reservoir fault body identification method based on seismic data according to claim 1, characterized in that: The method of obtaining a set of seismic data volumes of the area to be identified, performing similarity identification on the seismic data volumes based on coherence analysis, and tracking and outputting the fracture data volumes includes: Obtaining a set of seismic data volumes of the area to be identified, and preprocessing the set of seismic data volumes; The coherence volume technique based on multi-channel similarity determines the similarity of adjacent channels in a seismic data volume set; Scan the slope and similar bodies in the seismic data volume set to determine the location and direction of the fault body; Extract and retain the local maximum values ​​in similar data volumes and output the fracture data volume.

3. The reservoir fault body identification method based on seismic data according to claim 1 or 2, characterized in that: The step of obtaining a set of seismic data volumes of the area to be identified and preprocessing the set of seismic data volumes includes: Obtaining a set of seismic data volumes of the area to be identified, wherein the set of seismic data volumes of the area to be identified includes a plurality of seismic data volumes of the area to be identified; Search each seismic data volume, remove the null / invalid values ​​in the seismic data volume, and extract the valid seismic data volume; Digital filtering of the extracted seismic data volume; The slope of each seismic data volume is calculated using the following formula, the seismic traces are aligned, and the slopes of the main survey line and the tie line are output, where: The formula for the slope of a straight line passing through two points P1(x1,y1) and P2(x2,y2) is:

4. The reservoir fault body identification method based on seismic data according to claim 1 or 2, characterized in that: The scanning of the slope and similar bodies in the seismic data body set to determine the position and direction of the fault body includes: (i) Azimuth estimation and analysis to determine the location of the fracture body; today=f{arctan{|e 13 | / |e 12 |}} (ii) estimation and analysis of the inclination angle to determine the direction of the fracture body; Among them, P x is the reflection slope in the x direction; P y is the corresponding slope in the y direction.

5. The reservoir fault body identification method based on seismic data according to claim 3, characterized in that: The scanning of the slope and similar bodies in the seismic data body set to determine the position and direction of the fault body includes: (i) Azimuth estimation and analysis to determine the location of the fracture body; today=f{arctan{|e 13 | / |e 12 |}} (ii) estimation and analysis of the inclination angle to determine the direction of the fracture body; Among them, P x is the reflection slope in the x direction; P y is the corresponding slope in the y direction.

6. A reservoir fault body identification device based on seismic data using the method described in any one of claims 1 to 5, characterized in that: include: A similarity recognition unit obtains a set of seismic data volumes in the area to be recognized, performs similarity recognition on the seismic data volumes based on coherence analysis, and tracks and outputs the fracture data volumes; The fracture characterization unit uses the fracture and fracture characterization sample library to perform fracture characterization on the fault data volume.

7. The reservoir fault body identification device based on seismic data according to claim 6, characterized in that: The similarity identification unit comprises: A preprocessing module, which obtains a set of seismic data volumes of the area to be identified and preprocesses the set of seismic data volumes; A similarity calculation module determines the similarity of adjacent traces in a seismic data volume set based on the multi-channel similarity coherence volume technology; The first analysis module scans the slope and similar bodies in the seismic data volume set to determine the location and direction of the fault body; The second analysis module extracts and retains the local maximum values ​​in the similar data volume and outputs the fracture data volume.

8. The reservoir fault body identification device based on seismic data according to claim 6 or 7, characterized in that: The preprocessing module comprises: A data acquisition module is used to obtain a set of seismic data volumes of the area to be identified, wherein the set of seismic data volumes of the area to be identified includes a plurality of seismic data volumes of the area to be identified; The first preprocessing module searches each seismic data volume, removes null values / invalid values ​​in the seismic data volume, and extracts valid seismic data volumes; The second preprocessing module performs digital filtering on the extracted seismic data volume; The slope calculation module calculates the slope of each seismic data volume using the following formula, aligns the seismic traces, and outputs the slopes of the main survey line and the tie line direction, where: The formula for the slope of a straight line passing through two points P1(x1,y1) and P2(x2,y2) is:

9. A storage medium, characterized in that: The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the reservoir fault body identification method based on seismic data according to any one of claims 1 to 5 when running.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the reservoir fault body identification method based on seismic data as claimed in any one of claims 1 to 5.

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