A method of seismic prediction of fold-belt strata fractures

Abstract

This invention relates to a seismic prediction method for fractures and fissures in folded belt formations, belonging to the field of oil and gas exploration and development technology. The seismic prediction method for fractures and fissures in folded belt formations of this invention, by dividing the boundary points of the axial and limb regions, performing planar phase-controlled seismic adjacent channel gradient scanning, calculating the seismic trend volume of the formation, and then predicting the development of fractures and fissures in the formation, determines the distribution of fractures and fissures throughout the folded belt. This seismic prediction method for fractures and fissures in folded belt formations of this invention can reduce the impact of seismic axial deformation and large phase differences on the calculated fractures in the axial and limb regions, improve the influence of small phase changes and frequency decay, and mitigate the impact of discontinuities in the seismic phase axis caused by non-fracture factors due to the large slope of the limb slope, thus presenting the development information of fractures and fissures in the axial and limb regions of the folded belt formations.

Description

Technical Field

[0001] This invention relates to a seismic prediction method for fractures and fissures in folded strata, belonging to the field of oil and gas exploration and development technology. Background Technology

[0002] As oil and gas exploration depths increase, the requirements for the accuracy of reservoir type prediction in favorable underground target areas are also becoming increasingly stringent. The evaluation of fractured reservoirs has become an important direction for oil and gas exploration and development. Fold belts are the main areas for fracture development, and seismic prediction of the development and distribution of fractures throughout the fold belt is crucial for evaluating fractured oil and gas reservoirs and for exploration and development.

[0003] According to geological standards, the bending of rock strata is called a folded structural zone, or simply a folded zone. A bend (called a fold) within a folded structure is its constituent unit, composed of anticlines (folds where rock strata bulge upwards) and synclines (folds where rock strata concave downwards). According to the standards for fold element composition, such as... Figure 1 As shown, a fold belt is divided into an axial section and limbs. A fold surface consists of an anticline axial section, limbs, and a syncline axial section. The slope area transitioning from the anticline axial section to the syncline axial section is called the limb, and the intermediate region connecting the anticline and syncline is the limb. Fractures and fissures in fold belts are often distributed throughout the entire fold belt, and drilling reveals their distribution in both the axial and limb sections. Fractures and fissures are crucial conditions for oil and gas connectivity in reservoirs and play a vital role in the exploration and evaluation of oil and gas reservoirs. Drilling practice in fold belt formations indicates that fractures and fissures in different parts of the axial and limb sections of fold belts deformed under stress exhibit different formation mechanisms, and both the axial and limb sections of fold belts contain areas with good natural gas accumulation. To meet exploration requirements, it is necessary to determine the distribution of fractures and fissures in the axial and flank parts of the fold belt, predict and map the fractures and fissures in the stratigraphic segments of the target area, objectively demonstrate the development of fractures and fissures in the axial and flank parts of the fold belt, and systematically explore and evaluate oil and gas resources.

[0004] In seismic testing areas of oil and gas exploration blocks, when predicting fractures and fissures in the strata of the entire fold belt, the large curvature of the fold belt axis and the steep slope of the strata in the limbs cause significant vertical undulations in adjacent seismic traces along the seismic reflection phase axis, resulting in large phase variations between adjacent traces. The phase variable of the phase axis is a key calculation element for predicting fracture and fissure values ​​in seismic testing; its magnitude determines the value of the fracture or fissure. Therefore, when using the curvature detection method, the large curvature of the axial region leads to a large phase variation in adjacent traces, resulting in a large detected fracture or fissure value; conversely, the small curvature of the limbs leads to a small phase variation, resulting in a small detected fracture or fissure value. Similarly, when using the edge detection method, the steep slope of the limbs leads to a large phase variation in adjacent traces, resulting in a large detected fracture or fissure value; the gentle slope of the axial region leads to a small phase variation, resulting in a small detected fracture or fissure value. In reality, strata fractures and fissures develop in both the axial and limb portions of folded belts. Therefore, both methods have drawbacks. During detection, the fracture values ​​predicted by seismic analysis will show unevenness and distortion, a phenomenon where some values ​​increase while others decrease. Figure 2 and Figure 3 As shown. Therefore, when using conventional seismic prediction methods such as curvature detection and edge detection to detect fractures and cracks in the entire stratum, it cannot objectively reflect the actual development of fractures and cracks in the strata of the entire planar fold belt, affecting the overall evaluation of fractures and cracks in the axial and limb parts of the fold belt strata. Summary of the Invention

[0005] The purpose of this invention is to provide a seismic prediction method for fractures and fissures in folded zones, which can solve the problem that current seismic prediction methods such as curvature detection and edge detection cannot take into account different structural parts of folded zones as a whole, resulting in uneven fracture and fissure values.

[0006] To achieve the above objectives, the technical solution adopted by the seismic prediction method for fractures and fissures in folded strata of the present invention is as follows:

[0007] A method for predicting earthquake fractures in folded strata includes the following steps:

[0008] (1) Based on the depth domain seismic data volume and seismic horizon interpretation data of the target layer in the target work area, the axis and wing part boundary points of the fold belt strata of the target layer are determined by calculating the curvature; according to the axis and wing part boundary points, the axis data and wing data in the depth domain seismic data volume and seismic horizon interpretation data are divided and determined.

[0009] (2) Perform recursive phase difference equalization processing on the wing data in the depth domain seismic data volume and seismic horizon interpretation data to obtain the corrected wing seismic data volume and wing horizon data that are consistent with the trend of the phase change slope of the boundary point of the axis wing.

[0010] (3) Merge and smooth the axial data and the corrected wing seismic data in the depth domain seismic data volume to obtain the target seismic data volume; merge and smooth the axial data and the corrected wing layer data in the seismic horizon interpretation data to obtain the target horizon data;

[0011] (4) Based on the target seismic data volume, perform seismic fracture prediction calculations to obtain fracture data volume; based on the fracture data volume and the target layer data, determine the planar distribution of fractures in the target layer.

[0012] The seismic prediction method for fractures and fissures in folded belts of this invention predicts the development of fractures and fissures by dividing the boundary points of the axial and limb sections, performing planar phase-controlled seismic gradient scanning, calculating the seismic trend volume of the strata, and then determining the distribution of fractures and fissures throughout the folded belt. This method reduces the impact of seismic axial deformation and large phase differences on the calculated fractures in the axial and limb sections, mitigates the influence of phase changes, and addresses the discontinuity of seismic phase axes caused by non-fracture factors due to the steep slope of the limbs. It objectively presents the development information of fractures and fissures in the axial and limb sections of the folded belt.

[0013] Preferably, along the strike of the target fold belt strata in the cross section, starting from the center point of the wing, the curvature change rate is calculated step by step towards the apex or bottom point of the adjacent shaft. When the curvature change rate of a certain grid point relative to the adjacent grid point is greater than 20%, that grid point is the boundary point of the shaft-wing section.

[0014] Preferably, the cross section is a cross section perpendicular to the strike of the target fold belt strata.

[0015] Preferably, the apex of the shaft is the grid point with the largest absolute value of curvature in the anticlinal region of the fold in a cross-section perpendicular to the fold direction of the target layer; the bottom point of the shaft is the grid point with the largest absolute value of curvature in the synclinal region of the fold in a cross-section perpendicular to the fold direction of the target layer; and the center point of the wing is the grid point with the smallest absolute value of curvature in the wing.

[0016] Preferably, the recursive phase difference equalization processing method is as follows: Starting from the boundary point of the wing section of the seismic survey line, a recursive optimization scan of the adjacent seismic channels is performed towards the wing section to calculate the phase difference of the adjacent channels. The grid points with a phase difference of more than 20% of the adjacent channels are subjected to phase time shift processing to reduce the phase difference of the adjacent channels of the grid points with a phase difference of more than 20% to 20%. Then, the seismic phase axes are connected to obtain the trend line of the gradient change of the phase difference of the adjacent seismic channels of the seismic survey line. Filtering and smoothing processing is performed along the trend line of the gradient change of the phase difference of the adjacent seismic channels.

[0017] Preferably, the depth-domain seismic data volume and seismic horizon interpretation data of the target layer in the target work area are obtained by a method including the following steps: using well-seismic calibration of the depth-velocity field, performing time-depth conversion on the three-dimensional post-stack seismic data volume and seismic horizon interpretation data to obtain the depth-domain seismic data volume and seismic horizon interpretation data.

[0018] Preferably, the grid density of the three-dimensional post-stack seismic interpretation data is such that it can identify the lateral bending undulations and slope variations of the fold belt structure.

[0019] Preferably, the three-dimensional post-stack seismic interpretation data is obtained by a method including the following steps: based on the three-dimensional post-stack seismic data volume of the target work area and the drilling formation layering data, well logging sonic curves, and well logging density curves of the target layer, the three-dimensional post-stack seismic data volume is interpreted geologically to obtain the three-dimensional post-stack seismic interpretation data. Attached Figure Description

[0020] Figure 1 This is a schematic diagram showing the division of the fold belt strata into an axial part and a wing part in this invention;

[0021] Figure 2 This is a planar schematic diagram showing the "increase" of the crack value at the shaft and the "decrease" at the wing in this invention;

[0022] Figure 3 This is a cross-sectional schematic diagram showing the "increase" of the crack value at the shaft and the "decrease" at the wing in this invention;

[0023] Figure 4 This is a schematic flowchart of the seismic prediction method for fractures and fissures in folded strata according to an embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram of the time-depth calibration of seismic synthetic records in an embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram illustrating how the distance between two grid points in seismic data can be calculated using the number of seismic traces in an embodiment of the present invention;

[0026] Figure 7 This is a cross-sectional view of the target layer fold direction extracted from the depth domain seismic data C in an embodiment of the present invention;

[0027] Figure 8 This is a schematic diagram of the process 1 for calculating the rate of curvature change from the bottom point of the shaft to the adjacent vertices in an embodiment of the present invention;

[0028] Figure 9 This is a schematic diagram of the process 2 for calculating the rate of curvature change from the bottom point of the shaft to the adjacent vertices in an embodiment of the present invention;

[0029] Figure 10This is a schematic diagram of the process of calculating the rate of curvature change from the bottom point of the shaft to the adjacent vertices in an embodiment of the present invention;

[0030] Figure 11 This is a schematic diagram of curvature calculation in an embodiment of the present invention;

[0031] Figure 12 This is a planar distribution diagram of the target layer fold axis and wing portion obtained in an embodiment of the present invention;

[0032] Figure 13 This is a schematic diagram of the wing seismic data after correction, which is consistent with the trend of the adjacent channel phase difference gradient change and the phase difference change trend of the boundary point of the shaft and wing section, obtained in an embodiment of the present invention.

[0033] Figure 14 This is a schematic diagram of the program for filtering and smoothing seismic data at splicing joints in an embodiment of the present invention;

[0034] Figure 15 This is a schematic diagram of the fracture data volume obtained in an embodiment of the present invention;

[0035] Figure 16 This is a planar distribution diagram of the fractures in the target layer obtained in an embodiment of the present invention;

[0036] Figure 17 This is a planar distribution diagram of the fractures in the target layer obtained as a comparative example of the present invention. Detailed Implementation

[0037] The seismic prediction method for fold belt strata fractures of the present invention is an improved invention. Addressing the problem that current seismic prediction methods such as curvature detection and edge detection cannot comprehensively consider different structural parts of the fold belt, resulting in uneven fracture values, this invention determines the distribution of strata fractures throughout the entire fold belt by dividing the axial and wing boundaries, performing planar phased-array seismic gradient scanning of adjacent channels, calculating the strata seismic trend volume, and then predicting the development of strata fractures.

[0038] The technical solution of the present invention will be described in detail below with reference to specific embodiments.

[0039] Example 1

[0040] The seismic prediction method for fractures in folded strata in this embodiment takes an oilfield with folded strata as the target work area and the T3 layer within the target work area as the target layer, such as... Figure 4 As shown, the specific steps include:

[0041] (1) Acquire the 3D post-stack seismic data volume of the target work area and the drilling formation data, well logging sonic curves, and well logging density curves of the target layer; interpret the geological stratigraphy of the 3D post-stack seismic data volume; and complete the time-depth calibration of the seismic synthetic record (e.g., Figure 5 As shown in the figure, the top and bottom interfaces of the seismic reflection layers, which are the bending and undulating morphology of the fold ridges of the target layer, are defined. The interpreted three-dimensional post-stack seismic data is called three-dimensional post-stack seismic interpretation data A.

[0042] (2) To enable precise identification of subsurface stratigraphic structures using seismic data volumes, the interpretation grid precision of the 3D post-stack seismic interpretation data A is adjusted so that the adjusted grid density meets the requirement of an interpretation layer grid density of 1×1, i.e., a grid precision of 25×25 meters in metric units. In this embodiment, the seismic trace element of the 3D post-stack seismic interpretation data A for the target work area is 25m, which means the seismic trace spacing is 25 meters, and the initial basic interpretation grid density is 4×4, i.e., a grid density of 100m×100m. To meet the geological research requirements for controlling the gradient changes of the axial and limb parts of the fold structure belt, the interpretation layer grid density is adjusted to 1×1, i.e., a grid density of 25m×25m, through trace-by-trace interpolation interpretation. The seismic data with adjusted grid precision is defined as seismic interpretation data B. Using seismic interpretation data B, the distance between two grid points in the seismic data can be calculated from the number of seismic traces, such as... Figure 6 As shown, the distance between two grid points in the horizontal x-axis direction can be determined by calculating the number of seismic traces, and the distance between two grid points in the vertical y-axis direction can be determined by calculating the depth. In this embodiment, the grid density of seismic interpretation data B, obtained by adjusting the grid accuracy of the three-dimensional post-stack seismic interpretation data A, can identify the changes in the lateral bending undulations and slope of the fold belt structure.

[0043] (3) By calibrating the depth velocity field through well seismic calibration, time-depth conversion is performed on the three-dimensional post-stack seismic data volume and seismic interpretation data B to obtain the depth domain seismic data volume C0 and the seismic horizon interpretation data C.

[0044] (4) In the depth-domain seismic data volume C0, a cross-section perpendicular to the strike of the target fold belt is extracted to obtain a two-dimensional coordinate cross-section diagram with trace spacing on the x-axis and depth on the y-axis. In this embodiment, a cross-section perpendicular to the strike of the target fold belt extracted from the depth-domain seismic data volume C0 is shown below. Figure 7 As shown, the distribution range of the target layer fold belt axial wing in the cross section is determined by the variation of lateral distance and longitudinal depth.

[0045] (5) Calculate the curvature of all seismic grid points in the target fold zone strata in the cross section, determine the coordinates of the grid point with the largest absolute curvature in the anticline zone, the grid point with the largest absolute curvature in the syncline zone, and the grid point with the smallest absolute curvature in the wing in the cross section, define the grid point with the largest absolute curvature in the anticline zone as the axial vertex a1 point, define the grid point with the largest absolute curvature in the syncline zone as the axial bottom point a2 point, and define the grid point with the smallest absolute curvature in the wing as the wing center point b point.

[0046] (6) Along the strike of the target fold belt strata in the cross-section, starting from the center point b of the wing, calculate the curvature change rate step by step towards the adjacent axial apex point a1 or axial bottom point a2. Initially, in the gentle wing section, the curvature change amplitude is small with each step forward, showing an increase of 1% to 15%. When the curvature change rate suddenly increases to 20%, it enters the high-curvature axial region. Grid points with a curvature change rate greater than 20% relative to adjacent grid points are defined as axial-wing boundary points c. Correction begins at point c, resulting in a more balanced start at both ends, better gradual change in seismic data volume, and greater facilitating objective calculation of fractures and fissures. The axial-wing boundary point between the axial and wing sections is a turning point in strata deformation and plays a crucial role in defining the planar division range of the axial and wing sections. In this embodiment, using... Figure 7 Taking the axial vertex a1 and the wing center point b adjacent to the axial bottom point a2 in the cross-section as an example, the calculation process is as follows: Figures 8-10 As shown, Figures 8-10 The calculated rates of curvature change are 0.10, 0.11, and 0.14, respectively. Therefore, Figure 10 The curvature change rate of the grid point shown in the figure relative to the adjacent grid point is greater than 20%. This grid point represents the axis-wing section boundary point c11 between the shaft vertex a1 and the wing center point b. Similarly, the axis-wing section boundary point c2 between the shaft bottom point a2 and the wing center point b can be calculated. Following the above method, the following can be calculated: Figure 7 The boundary point of the shaft wings between any adjacent shaft apex and shaft bottom point in the cross section.

[0047] For ease of calculation, this embodiment uses an iterative program to determine the boundary points of the wing section between adjacent vertices and bases of the axial section. The specific iterative calculation process is as follows: The curvature values ​​of the upper-layer curve points of each adjacent seismic trace are calculated iteratively along the direction of the seismic survey line. Iteration is a repetitive feedback process; each repetition of the process for adjacent traces is called an "iteration," and the result of each iteration serves as the initial value for the next iteration. That is, the results of traces 1 and 2 on a survey line become the starting points for traces 2 and 3, and so on. The purpose is to approximate the desired target, i.e., to find the point where the curvature change rate of the adjacent trace grid points is greater than 20%, i.e., the wing section boundary point c. The calculation principle is based on the curvature mathematical formula k = 1 / r (the radius r of the curve arc). The intersection points of adjacent seismic traces and the interpretation layer in a 3D seismic study are points A and B, respectively. Figure 11 As shown, the arc length AB is the seismic interpretation layer length (L), and the line segment AB is the chord length (trace distance) of the arc segment AB. Perpendicular lines are drawn at the tangents at points A and B, and the intersection of the two perpendicular lines is O, forming the central angle ∠AOB, where n° is the central angle measure. The arc length is calculated using the formula: L=n×π×r / 180, L=α×r, where n° is the central angle measure, r is the radius, L is the arc length of the central angle, and α is the central angle measure (in radians). Based on the arc length L and the chord length S, the radius r=2S / sin[L / (2r)]. Finally, the curvature of each recording point on the curve can be determined using the calculated radius r and the formula k=1 / r. In the depth-domain seismic volume 3D database, the computer treats the interpretation layers as curved arc surface grid data, performs iterative calculations of the trace distances before and after each survey line, and the numerical change of point c on each survey line depends on the magnitude changes of the maximum curvature point a and the minimum curvature point b before and after it. The calculation obtains information on countless points c, which is used for trend correction of the depth-domain seismic data volume C0 and the seismic layer interpretation data C, and plays a crucial role in the overall axial and wing planar division of the range.

[0048] (7) Repeat steps (4) to (6) to determine all the wing portion boundaries of the target layer fold in the depth domain seismic data volume C0.

[0049] (8) Repeat steps (4) to (7) to determine all the axial section boundaries of the target layer fold in the seismic horizon interpretation data C.

[0050] (9) Project all axis and wing boundary points of the target fold zone strata in depth domain seismic data volume C0 and all axis and wing boundary points of the target fold zone strata in seismic horizon interpretation data C onto a planar map. Connect all axis and wing boundary points in the projection map to generate a vector text file of axis and wing boundary lines. Separate the axis and wing ranges of the target fold zone strata, determine the planar distribution boundaries of the axis and wing ranges of the target fold zone strata, perform range vector segmentation, and draw as shown. Figure 12 The diagram shows the planar distribution of the axial and limb portions of the target layer fold belt. Data located in the axial portion of the depth-domain seismic data volume C0, determined based on the axial-limb boundary points, is defined as the target layer axial data volume E0. Data located in the axial portion of the seismic horizon interpretation data C, determined based on the axial-limb boundary points, is defined as the target layer axial horizon data volume E.

[0051] (10) Starting from the boundary point of the wing section of each seismic line in the seismic data volume C0, perform a recursive optimization scan of adjacent seismic channels towards the wing section, calculate the phase difference of adjacent channels, and perform phase time shift processing on grid points with adjacent channel phase differences greater than 20% (time shift: longitudinal time drift of the seismic channel, moving the seismic axis time position up and down, changing the original phase value), so that the adjacent channel phase difference of grid points with adjacent channel phase differences greater than 20% is reduced to 20%. Then connect the seismic phase axes to obtain the trend line of the gradient change of the adjacent seismic channel phase difference for each seismic line; filter and smooth the seismic data volume C0 along the trend line of the gradient change of the adjacent seismic channel phase difference to make the seismic phase axis smoother, and obtain the corrected wing seismic data volume D that is consistent with the trend of the phase difference change of the boundary point of the wing section. The specific process is as follows: Figure 13 As shown.

[0052] (11) Following the method in step (10), starting from the boundary point of the wing section of each seismic line in the seismic horizon interpretation data C, perform a recursive optimization scan of adjacent seismic channels towards the wing section, calculate the phase difference of adjacent channels, and perform phase time shift processing on grid points with a phase difference greater than 20% (time shift: longitudinal time drift of the seismic channel, moving the seismic axis time position up and down, changing the original phase value), so that the phase difference of adjacent channels of grid points with a phase difference greater than 20% is reduced to 20%, and then the seismic phase axis is connected to obtain the trend line of the gradient change of the phase difference of adjacent channels of each seismic line; the seismic horizon interpretation data C is filtered and smoothed along the trend line of the gradient change of the phase difference of adjacent channels to make the seismic phase axis smoother, and the corrected wing horizon data M is obtained, which is consistent with the trend of the phase difference change of the boundary point of the wing section.

[0053] (12) Merge and smooth the target layer axial data volume E0 obtained in step (9) and the corrected wing seismic data volume D obtained in step (10) to obtain seismic data volume G. The specific method is as follows: The target layer axial data volume E and the corrected wing seismic data volume D are spliced ​​along the line connecting the axial and wing boundaries to obtain the merged seismic data volume F. Seismic data volume filtering and smoothing are then applied to the adjacent channels on both sides of the splicing line of seismic data volume F to eliminate the influence of unevenness traces at the splicing point. In this embodiment, the following method is used... Figure 14 The program shown performs filtering and smoothing on the seismic data at the splicing joint to obtain the seismic data volume G. Figure 14 In this code, `func(x)` represents the approximate curve trajectory in a real-world application, modeled using trigonometric functions. `generateData()` is the data set output by the model's image, where `i` is the x-coordinate position of the independent variable, and `func(i)` represents the y-coordinate position of the curve model function's output. `option` specifies the program's image output attributes, including line attributes, axis attributes, and image data source.

[0054] (13) Following the method in step (12), merge and smooth the target layer axial stratigraphic data E obtained in step (9) and the corrected wing stratigraphic data M obtained in step (11) to obtain trend stratigraphic data N.

[0055] (14) Perform seismic fracture prediction calculations on the seismic data volume G obtained in step (12) to obtain the following results: Figure 15 The data volume H of the cracks and fissures is shown.

[0056] This invention overcomes the influence of excessive phase difference changes in seismic data volume caused by fold deformation in folded strata. It can reduce the impact of seismic axis deformation and large phase difference on the calculated fractures of the axis and limbs, improve the influence of phase change and the discontinuity of seismic phase axis caused by non-fracture factors due to the large slope of the limb slope, and can present the development information of fractures in the axis and limbs of folded strata. The fracture data volume obtained by this invention can identify the degree of fracture development in folded strata.

[0057] (15) Input the trend layer data N obtained in step (13) into the fracture data volume H in step (14), extract the plane fracture values ​​of a single layer segment, and plot as shown. Figure 16 The target layer fracture distribution diagram is shown.

[0058] Example 2

[0059] The difference between the earthquake prediction method for fractures and fissures in folded strata in this embodiment and the earthquake prediction method for fractures and fissures in folded strata in Embodiment 1 is only that in step (6) of the earthquake prediction method for fractures and fissures in folded strata in this embodiment, based on the principle of curve inflection points and the experimental results of geological brittle strata compression deformation, when the curvature difference between the axial and wing parts of the stratum curvature reaches 20%, the stratum surface is curved vertically and vertically, and the degree of fracture development is the highest. It is determined that the curvature of the boundary point of the axial and wing parts is equal to 20% of the difference in curvature between the vertex (or bottom point) of the adjacent axial part and the center point of the wing part. Therefore, in this invention, the boundary point of the axial and wing parts between the top and bottom points of the adjacent axial part can also be determined by calculating the difference in curvature between the vertex (or bottom point) of the adjacent axial part and the center point of the wing part.

[0060] Comparative Example

[0061] The difference between the seismic prediction method for fractures in folded strata in this comparative example and the seismic prediction method for fractures in folded strata in the embodiment is that steps (4), (5), (6), (7), and (8) are omitted in this comparative example. The planar distribution map of fractures in the target layer obtained in this comparative example is shown below. Figure 17 As shown.

[0062] Depend on Figure 16 and Figure 17As can be seen, in the plane distribution map of the target layer obtained by the comparative example, the phenomenon of fracture expansion and contraction is serious, and it is impossible to evaluate the entire area at the same level due to the incomplete coverage of some fractures. In the plane distribution map of the target layer obtained by the example, the fractures in the axial and wing parts of the fold belt are presented on the same plane, which can provide an overall evaluation of the development of the fracture reservoir in the entire fold belt and provide strong technical support for the exploration and deployment of new well locations.

Claims

1. A method for earthquake prediction of fractures and fissures in folded strata, characterized in that, Includes the following steps: (1) Based on the depth domain seismic data volume and seismic horizon interpretation data of the target layer in the target work area, the axis and wing part boundary points of the fold belt strata of the target layer are determined by calculating the curvature; according to the axis and wing part boundary points, the axis data and wing data in the depth domain seismic data volume and seismic horizon interpretation data are divided and determined. (2) Perform recursive phase difference equalization processing on the wing data in the depth domain seismic data volume and seismic horizon interpretation data to obtain the corrected wing seismic data volume and wing horizon data that are consistent with the trend of the phase change slope of the boundary point of the axis wing. (3) Merge and smooth the axial data and the corrected wing seismic data in the depth domain seismic data volume to obtain the target seismic data volume; The target horizon data is obtained by merging and smoothing the axial data and the corrected wing horizon data in the seismic horizon interpretation data. (4) Based on the target seismic data volume, perform seismic fracture prediction calculations to obtain fracture data volume; based on the fracture data volume and the target layer data, determine the planar distribution of fractures in the target layer.

2. The seismic prediction method for fractures and fissures in folded belt strata as described in claim 1, characterized in that, Along the strike of the target fold belt strata in the cross section, starting from the center point of the wing, calculate the rate of curvature change for each adjacent axial apex or axial basal point. When the rate of curvature change of a certain grid point relative to the adjacent grid point is greater than 20%, that grid point is the boundary point of the axial wing section.

3. The seismic prediction method for fractures and fissures in folded belt strata as described in claim 2, characterized in that, The cross-section is a cross-section perpendicular to the strike of the target layer fold belt strata.

4. The seismic prediction method for fractures and fissures in folded strata as described in claim 2, characterized in that, The apex of the axis is the grid point with the largest absolute value of curvature in the anticline region of the fold in the cross section perpendicular to the fold direction of the target layer; the bottom point of the axis is the grid point with the largest absolute value of curvature in the syncline region of the fold in the cross section perpendicular to the fold direction of the target layer. The center point of the wing is the grid point with the smallest absolute value of the wing curvature.

5. The seismic prediction method for fractures and fissures in folded belt strata as described in claim 1, characterized in that, The method for recursive phase difference equalization is as follows: Starting from the boundary point of the wing section of the seismic survey line, a recursive optimization scan of adjacent seismic channels is performed towards the wing section to calculate the phase difference of adjacent channels. Grid points with a phase difference greater than 20% are subjected to phase time shift processing to reduce the phase difference of adjacent channels of grid points with a phase difference greater than 20% to 20%. Then, the seismic phase axes are connected to obtain the trend line of the gradient change of the phase difference of adjacent seismic channels of the seismic survey line. Filtering and smoothing processing is performed along the trend line of the gradient change of the phase difference of adjacent seismic channels.

6. The seismic prediction method for fractures and fissures in folded belt strata as described in any one of claims 1-5, characterized in that, The depth-domain seismic data volume and seismic horizon interpretation data of the target layer in the target work area are obtained by a method including the following steps: using well-seismic calibration of the depth-velocity field, performing time-depth conversion on the three-dimensional post-stack seismic data volume and seismic horizon interpretation data to obtain the depth-domain seismic data volume and seismic horizon interpretation data.

7. The seismic prediction method for fractures and fissures in folded belt strata as described in claim 6, characterized in that, The grid density of the three-dimensional post-stack seismic interpretation data is based on the ability to identify changes in the lateral bending undulations and slopes of the fold belt structure.

8. The seismic prediction method for fractures and fissures in folded belt strata as described in claim 6, characterized in that, The three-dimensional post-stack seismic interpretation data is obtained by a method including the following steps: based on the three-dimensional post-stack seismic data volume of the target work area and the drilling formation layering data, well logging sonic curves, and well logging density curves of the target layer, the three-dimensional post-stack seismic data volume is interpreted geologically to obtain the three-dimensional post-stack seismic interpretation data.

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