Method for quickly depicting ancient micro geomorphologic map of stratum

Abstract

The invention belongs to the technical field of seismic exploration, and discloses a method for quickly depicting a stratum ancient micro-landform map, which comprises the following steps of: 1, picking up time or depth of a target stratum to obtain total data of horizon elevation; and 2, performing low-pass filtering on the total data of the horizon elevation at the cut-off frequency to obtain low-pass filtering data. And a third step of performing median filtering on the total data of the horizon elevation, adjusting the size of a filtering window until the similarity between the obtained median filtering data and the low-pass filtering data reaches more than 90%, and taking the data as low-frequency data. And 4, solving a difference value between the total data of the horizon elevation and the low-frequency data to obtain medium-high frequency data. And 5, calculating curvature data of the total data of the horizon elevation. And 6, carrying out RGB pixel fusion on the low-frequency data, the medium-high-frequency data and the curvature data of the horizon elevation to form a map, and obtaining a stratum ancient micro-landform map. According to the method, the breakpoint fault features in the stratum ancient landform are reserved, and the ancient micro landform can be clearly presented.

Description

Technical Field

[0001] The present invention belongs to the technical field of seismic exploration. More specifically, the present invention relates to a method for quickly depicting the paleo-microtopography map of strata. Background Art

[0002] The paleo-microtopography of strata is a microscopic mapping of the surface morphology in the geological history period, reflecting the comprehensive effects of geological processes such as tectonic movement, sedimentation, and erosion. As the smallest geomorphic morphological unit in the paleogeomorphology, its fine depiction has important value for understanding basin evolution, fault activity, paleogeographical environment reconstruction, and resource exploration, and helps to identify paleochannel sand bodies and tectonic traps and predict reservoir distribution.

[0003] Early studies mainly extracted geomorphic features through geostatistics or simple morphological filtering. First, multi-scale feature separation was carried out. Using the short-time Fourier transform or wavelet transform, the seismic signal was decomposed into low-frequency tectonic trends and high-frequency sedimentary microfacies components. Then, high-frequency extraction was carried out. For example, a sub-band with a central frequency was selected, and the corresponding wavelength was a microtopography with a specific length. Then, curvature analysis was carried out to identify fault cliffs and gully boundaries. Finally, multi-attribute fusion was carried out. For example, the low-frequency tectonic trend, medium-high frequency sedimentary details, and curvature morphological features were superimposed according to weights to generate a comprehensive geomorphology map.

[0004] The following defects exist in the prior art: Firstly, single-layer elevation data is difficult to characterize the multi-scale geometric characteristics of the geomorphology, and the high-frequency topographic undulations representing the microtopography are difficult to clearly display after mapping with single-layer elevation data; Secondly, the conventional filtering process destroys structural features such as breakpoints and faults, resulting in abnormal geomorphology; Thirdly, the time-frequency domain coupling of low-frequency tectonic trends and high-frequency sedimentary microfacies causes the high-frequency components reflecting microtopography features to overlap with the low-frequency band due to energy attenuation and cannot be effectively separated.

[0005] With the popularization of artificial intelligence and high-precision geophysical data, the depiction of paleo-microtopography is developing towards high resolution, dynamic visualization, and multi-disciplinary collaboration. The innovation of the method for depicting the paleo-microtopography map of strata can not only promote the transformation of geology from qualitative description to quantitative analysis, but also provide scientific support for resource exploration and disaster prevention.

[0006] In view of this, there is an urgent need to provide a technical solution for quickly depicting the paleo-microtopography map of strata, so that the breakpoint and fault structures are retained in the paleo-microtopography map of strata, and the multi-scale micro-amplitude structures are clearly depicted. Summary of the Invention

[0007] In order to solve at least one or more of the above-mentioned technical problems, the present invention provides a method for quickly depicting the paleo-microtopography map of a formation, including: the first step, picking up the time or depth of the target layer in the three-dimensional seismic data containing the target layer to obtain the total data of the layer elevation; the second step, performing spectral analysis on the total data of the layer elevation to determine the cut-off frequency, and performing low-pass filtering on the total data of the layer elevation with the cut-off frequency to obtain low-pass filtered data; the third step, performing median filtering on the total data of the layer elevation, adjusting the filtering window size until the similarity between the obtained median filtered data and the low-pass filtered data reaches more than 90%, as the low-frequency data of the layer elevation; the fourth step, obtaining the difference between the total data of the layer elevation and the low-frequency data of the layer elevation to obtain the medium-high frequency data of the layer elevation; the fifth step, obtaining the curvature data of the total data of the layer elevation; the sixth step, performing RGB pixel fusion on the low-frequency data of the layer elevation, the medium-high frequency data of the layer elevation and the curvature data of the layer elevation to form a map, and obtaining the paleo-microtopography map of the formation.

[0008] According to an embodiment of the present invention, in the second step, the cut-off frequency is determined by the following method: performing spectral analysis on the total data of the layer elevation to obtain the main frequency, and increasing the main frequency by 0.2 to 0.3 octaves as the cut-off frequency.

[0009] According to an embodiment of the present invention, in the second step, the cut-off frequency is determined by the following method: performing spectral analysis on the total data of the layer elevation to obtain the peak frequency, and taking 32% of the peak frequency as the cut-off frequency.

[0010] According to an embodiment of the present invention, in the third step, the initial size of the filtering window is set to be twice or more of the spatial size of the paleo-microtopography of the formation.

[0011] According to an embodiment of the present invention, in the third step, the filtering window is rectangular, and the initial side length is 0.5 to 1 times the trace interval of the three-dimensional seismic data.

[0012] According to an embodiment of the present invention, the adjustment step size of the filtering window size is an integer multiple of the trace interval of the three-dimensional seismic data.

[0013] According to an embodiment of the present invention, the adjustment step size adopts a dynamic multi-scale form and is dynamically adjusted according to the curvature or fault distribution of the center point of the filtering window.

[0014] According to an embodiment of the present invention, the similarity between the smoothed filtered data and the low-pass filtered data is compared by calculating the correlation coefficient or the mean square error.

[0015] According to an embodiment of the present invention, the curvature data takes any one of the maximum slope, average slope or weighted slope of each point of the total data of the layer elevation.

[0016] According to an embodiment of the present invention, in the sixth step, the low-frequency data of the horizon elevation, the medium-high frequency data of the horizon elevation, and the curvature data of the horizon elevation are fused based on the coordinates of each point of the total data of the horizon elevation.

[0017] According to an embodiment of the present invention, the cut-off frequency is obtained in the following manner: performing spectral analysis on the total data of the horizon elevation of the target layer to determine the peak frequency; taking a% of the peak frequency as the cut-off frequency of the low-pass filter, where a ranges from 30 to 40.

[0018] The method for quickly depicting the paleo-microtopography map of the formation according to the present invention retains the characteristics of breakpoints and faults in the paleo-topography of the formation through median filtering. By separating the low-frequency data and the medium-high frequency data, the tectonic trend and sedimentary details are effectively presented. By dynamically adjusting the adjustment step size of the filtering window at multiple scales, the details of the microtopography can be specifically presented. By calculating the curvature data to quantify the geometric bending degree of the target horizon, the morphological characteristics in the paleo-topography of the formation are further presented. After separately performing monochromatic imaging on the low-frequency data, the medium-high frequency data, and the curvature data of the horizon elevation and then performing RGB pixel fusion, the paleo-microtopography map of the formation is obtained, which can clearly present the paleo-microtopography. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0020] Figure 1 A schematic diagram of a three-dimensional seismic exploration system is shown;

[0021] Figure 2 A schematic diagram of the steps of the method for quickly depicting the paleo-microtopography map of the formation is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] It should be understood that the terms "including" and "comprising" used in the specification and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0024] It should also be understood that the terms used in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. As used in the specification and claims of the present invention, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms. It should be further understood that the term "and / or" used in the specification and claims of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] As used in this specification and the claims, the term "if" may be construed, depending on the context, as "when", "once", "in response to determining", or "in response to detecting". Similarly, the phrases "if determined" or "if [the described condition or event] is detected" may be construed, depending on the context, as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]".

[0026] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] Figure 1 A schematic diagram of a three-dimensional seismic exploration system is shown.

[0028] As Figure 1 shown, in the system 100, a plurality of mutually spaced geophones 110 for detecting seismic waves are arranged on the surface 101 of the exploration target area, forming a geophone array covering the target area on a plane. These geophones 110 are connected to the seismic information processing device by wired or wireless means, and a plurality of seismic sources 120 are also provided. The seismic information processing device can perform preliminary processing on seismic data. The working process of the three-dimensional seismic exploration system is as follows: Seismic sources 120 located at multiple positions are artificially excited to generate seismic waves. The seismic waves are reflected from the boundary of the formation 102 and received by the geophone array, forming seismic information collected on a plane and varying with time. The seismic information received by the geophone array represents certain measures of the energy of the seismic wave as a function of time, such as displacement, velocity, wave impedance, pressure, etc. These information can be grouped in different ways, such as traces, gathers, etc., and then processed or format-converted according to the corresponding relationship of time and space to form a three-dimensional seismic data volume in the form of a three-dimensional array, that is, high-quality three-dimensional seismic fine imaging data is obtained. This three-dimensional seismic data volume is formed by stacking interface points in space. The interpretation of the three-dimensional seismic data volume can observe the morphology of the geological interface from different directions and study the changes of geological bodies in three-dimensional space by cutting transverse sections, longitudinal sections and horizontal slices.

[0029] Figure 2The figure shows a schematic diagram of the steps of a method for quickly depicting the paleo-microtopography map of a formation.

[0030] As Figure 2 shown, a method 200 for quickly depicting the paleo-microtopography map of a formation includes a first step S201 of picking up the time or depth of a target layer in 3D seismic data containing the target layer to obtain the total data of the horizon elevation; a second step S202 of performing spectral analysis on the total data of the horizon elevation to determine the cut-off frequency, and performing low-pass filtering on the total data of the horizon elevation with the cut-off frequency to obtain low-pass filtered data; a third step S203 of performing median filtering on the total data of the horizon elevation, and adjusting the filtering window size until the similarity between the obtained median filtered data and the low-pass filtered data reaches more than 90%, as the low-frequency data of the horizon elevation; a fourth step S204 of obtaining the difference between the total data of the horizon elevation and the low-frequency data of the horizon elevation to obtain the medium-high frequency data of the horizon elevation; a fifth step S205 of obtaining the curvature data of the total data of the horizon elevation; and a sixth step S206 of performing RGB pixel fusion on the low-frequency data of the horizon elevation, the medium-high frequency data of the horizon elevation, and the curvature data of the horizon elevation to form a map, thereby obtaining the paleo-microtopography map of the formation.

[0031] In the first step S201, the total data of the horizon elevation refers to the continuous elevation distribution information of a specific formation obtained through seismic interpretation in three-dimensional space, including the spatial morphology data after time or depth conversion of the top and bottom interfaces of the formation relative to the reference datum. Identify the continuous reflection features such as the in-phase axis and amplitude anomaly of the target layer in the 3D seismic data, draw a continuous reflection in-phase axis along the target layer, and mark its time position to obtain the total data of the horizon elevation.

[0032] The total data of the horizon elevation is a form that presents the mixture of low-frequency data and medium-high frequency data. The low-frequency data reflects the geological structure caused by later changes, and the medium-high frequency reflects the paleo-microtopography. In order to quickly depict the paleo-microtopography map of the formation, it is necessary to effectively separate them and present them separately and prominently.

[0033] Low-pass filtering is used to remove the high-frequency components in the signal and only retain the low-frequency components. In image processing, low-pass filtering can be used to achieve the smoothing effect of the image, reduce the noise and details in the image, and quickly process the image. However, during the low-pass filtering process, features such as breakpoints and faults will be eliminated, resulting in the distortion of the details of the paleo-microtopography.

[0034] Based on this, the purpose of performing low-pass filtering in the second step S202 of the present invention is to provide a reasonable standard for separating low-frequency data. That is, in order to determine the end point of the median filtering, the total data of the horizon elevation is subjected to low-pass filtering with the cut-off frequency, and the obtained result is used as the standard for the end point of the median filtering.

[0035] Specifically, perform spectral analysis on the total data of the horizon elevation to determine the cut-off frequency, and perform low-pass filtering on the total data of the horizon elevation with the cut-off frequency to obtain low-pass filtered data.

[0036] Among them, any of the following methods can be adopted to determine the cut-off frequency of the low-pass filter: First, perform spectral analysis on the total data of the horizon elevation to obtain the main frequency, and increase the main frequency by 0.2 - 0.3 octaves as the cut-off frequency. That is, 1.2 - 1.3 times the main frequency. Second, perform spectral analysis on the total data of the horizon elevation of the target layer to determine its peak frequency, and obtain a% of the peak frequency as the cut-off frequency of the low-pass filter, where a is taken as 30 - 40%. Preferably, the cut-off frequency is 32% of the peak frequency.

[0037] The cut-off frequency is the boundary between the passband and the stopband of the filter, while the peak frequency corresponds to the main energy band or resonance frequency of the signal. In the present invention, the selection of the cut-off frequency can effectively suppress high frequencies or interference while retaining the energy of the main frequency band.

[0038] In the third step S203, in order to separate the low-frequency data, perform median filtering on the total data of the horizon elevation, and set the end point of the filtering process when the similarity between the median-filtered data and the low-pass filtered data reaches 90%.

[0039] In the present invention, in order to avoid the detail distortion of the paleo-microtopography caused by low-pass filtering, median filtering is used instead of low-pass filtering. Median filtering replaces the central value with the median value of the data within the sliding window, and has a significant effect on suppressing isolated noise points. At the same time, it is beneficial to retain edge information, and has a lower smoothing degree for step-type mutations such as fault boundaries, and can partially retain mutation characteristics.

[0040] The initial filter window size is set according to the spatial size of the microtopography to be identified. The finer the scale of the microtopography to be identified, the smaller the filter window is set.

[0041] Preferably, the initial filter window is set to be twice or more the spatial size of the microtopography.

[0042] More preferably, the median filter window is rectangular, with an initial side length of 0.5 - 1 times the trace interval of the 3D seismic data, and the adjustment step size is an integer multiple of the trace interval. The adjustment step size adopts a dynamic multi-scale form and is dynamically adjusted according to the curvature of the window center point or the fault distribution.

[0043] A rectangular window is adopted, and the side length of the initial window is set to be 0.5 to 1 times the seismic data trace interval. For example, if the distance between adjacent receivers in the seismic data is 20 meters, the initial window can be a rectangle with a side length of 10 to 20 meters. This setting can cover a certain range of geological information and avoid the loss of details caused by an overly large window. As the processing range changes, the window size will be gradually adjusted in integer multiples of the trace interval, such as expanding from 10 meters to 20 meters, 40 meters, or shrinking to 5 meters, 2.5 meters.

[0044] The core of the dynamic adjustment lies in intelligently changing the window size according to the curvature or fault distribution in the central area of the window. When the strata are flat and the curvature is small, it indicates a stable sedimentation area, and at this time, the window can be enlarged to enhance the smoothing effect. If a sudden change in curvature or signs of faults are detected, the window is reduced to retain the structural details. This adaptive mechanism can present features with different precisions and avoid the distortion of geological features caused by over-smoothing.

[0045] When comparing the similarity between the median-filtered data and the low-pass filtered data, the low-pass filtered data forms a three-dimensional low-pass filtered surface. The initial median-filtered data forms a three-dimensional smoothed filtered surface. Then, the similarity between the three-dimensional low-pass filtered surface and the three-dimensional smoothed filtered surface is compared. When the similarity between the two is less than 90%, the size of the filter window for smoothed filtering is adjusted, and a three-dimensional smoothed filtered surface is generated again for comparison until the similarity between the two reaches more than 90%.

[0046] When comparing the similarity between the three-dimensional low-pass filtered surface and the three-dimensional smoothed filtered surface, methods such as curvature analysis or morphological matching can be used. For example, in curvature analysis, geometric attributes such as planar curvature and profile curvature are calculated to quantify the local bending degree of the surface, and the mean, variance, and extreme value regions of the curvature distribution are compared. Another example is that in morphological matching, the gradient direction distribution of the surface is compared, linear structures are matched, and the boundary similarity is compared. The similarity between the smoothed filtered data and the low-pass filtered data is compared by calculating the correlation coefficient or mean square error. Preferably, the correlation coefficient uses the Pearson or Spearman correlation coefficient.

[0047] In the fourth step S204, after obtaining the low-frequency data of the horizon elevation, the difference between the total data of the horizon elevation and the low-frequency data of the horizon elevation is obtained to get the medium-high frequency data of the horizon elevation. After processing, the medium-high frequency data will reveal the detailed morphology of the microtopography.

[0048] In the fifth step S205, on the basis of separating the low-frequency data and the medium-high frequency data of the horizon elevation, the present invention also obtains the curvature data of the total data of the horizon elevation. The curvature data takes any one of the maximum slope, average slope, or weighted slope of each point of the total data of the horizon elevation. When obtaining the curvature, it can also be obtained in units of the adjusted window size in the second step.

[0049] Obtaining the curvature data of the horizon elevation data is the core step to quantitatively describe the curvature of the formation surface by mathematical means. Based on the structure tensor method, the local gradient direction is calculated, a structure tensor containing slope changes is constructed, and the noise interference is optimized by Gaussian smoothing. Finally, the tensor eigenvalues are decomposed to obtain key parameters such as planar curvature and profile curvature.

[0050] In the sixth step S206, the low-frequency data of the horizon elevation, the medium-high frequency data of the horizon elevation, and the curvature data of the horizon elevation are fused based on the coordinates of each point of the total data of the horizon elevation. The curvature data features the microtopography undulations presented by the curvature. The low-frequency data of the horizon elevation, the medium-high frequency data of the horizon elevation, and the curvature data of the horizon elevation are respectively subjected to monochromatic imaging, and a fused paleo-microtopography map of the formation is obtained. The monochromatic imaging is to present the low-frequency data, the medium-high frequency data, and the curvature data in different colors.

[0051] Fusion means presenting the three types of data in the same coordinate system after alignment. RGB pixel fusion means respectively mapping the three types of data of low frequency, medium-high frequency, and curvature in red, green, and blue, and then aligning and fusing them in units of pixels.

[0052] By fusing the three types of data of low frequency, medium-high frequency, and curvature into a comprehensive geomorphology map, the structural trend, sedimentary details, and morphological features of the target horizon can be presented simultaneously, highlighting the spatial heterogeneity of the paleo-microtopography and significantly improving the characterization accuracy.

[0053] The method for quickly characterizing the paleo-microtopography map of the formation in the present invention retains the fault break features in the paleo-geomorphology of the formation through median filtering. By separating the low-frequency data and the medium-high frequency data, the structural trend and sedimentary details are effectively presented. By dynamically adjusting the adjustment step length of the filtering window at multiple scales, the details of the microtopography can be specifically presented. By obtaining the curvature data to quantitatively describe the geometric curvature of the target horizon, the morphological features in the paleo-geomorphology of the formation are further presented. By respectively performing monochromatic imaging on the low-frequency data, medium-high frequency data, and curvature data of the horizon elevation and then fusing them, a paleo-microtopography map of the formation is obtained, which can clearly present the paleo-microtopography.

[0054] Although multiple embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Many changes, variations, and alternative methods may occur to those skilled in the art without departing from the spirit and scope of the present invention. It should be understood that various alternative embodiments of the present invention described herein may be employed in practicing the present invention. The appended claims are intended to define the scope of the present invention and thus cover equivalents or alternative embodiments within the scope of these claims.

Claims

1. A method for quickly depicting the paleo-microtopography map of a formation, characterized in that, Including: The first step: picking the time or depth of the target layer in the 3D seismic data including the target layer to obtain the total data of the horizon elevation; The second step: performing spectral analysis on the total data of the horizon elevation to determine the cut-off frequency, and performing low-pass filtering on the total data of the horizon elevation with the cut-off frequency to obtain low-pass filtered data; The third step: performing median filtering on the total data of the horizon elevation, adjusting the filtering window size until the similarity between the obtained median filtered data and the low-pass filtered data reaches more than 90%, which is used as the low-frequency data of the horizon elevation; The fourth step: obtaining the difference between the total data of the horizon elevation and the low-frequency data of the horizon elevation to obtain the medium-high frequency data of the horizon elevation; The fifth step: obtaining the curvature data of the total data of the horizon elevation; The sixth step: performing RGB pixel fusion on the low-frequency data of the horizon elevation, the medium-high frequency data of the horizon elevation, and the curvature data of the horizon elevation to form a map, and obtaining the paleo-microtopography map of the formation.

2. The method according to claim 1, characterized in that In the second step, the cut-off frequency is determined in the following manner: performing spectral analysis on the total data of the horizon elevation to obtain the main frequency, and increasing the main frequency by 0.2 - 0.3 octaves as the cut-off frequency.

3. The method according to claim 1, characterized in that In the second step, the cut-off frequency is determined in the following manner: performing spectral analysis on the total data of the horizon elevation to obtain the peak frequency, and taking 32% of the peak frequency as the cut-off frequency.

4. The method according to claim 1, characterized in that In the third step, the initial size of the filtering window is set to be twice or more of the spatial size of the paleo-microtopography of the formation.

5. The method according to claim 1, characterized in that In the third step, the filtering window is rectangular, and the initial side length is 0.5 - 1 times the trace interval of the 3D seismic data.

6. The method according to claim 1, characterized in that The adjustment step size of the filtering window size is an integer multiple of the trace interval of the 3D seismic data.

7. The method according to claim 6, characterized in that The adjustment step size adopts a dynamic multi-scale form and is dynamically adjusted according to the curvature or fault distribution of the center point of the filtering window.

8. The method according to claim 1, characterized in that The similarity between the smoothed filtered data and the low-pass filtered data is compared by calculating the correlation coefficient or the mean square error.

9. The method according to claim 1, characterized in that The curvature data takes any one of the maximum slope, average slope or weighted slope of each point of the total data of the horizon elevation.

10. The method according to claim 1, characterized in that In the sixth step, the low-frequency data of the horizon elevation, the medium-high frequency data of the horizon elevation, and the curvature data of the horizon elevation are fused based on the coordinates of each point of the total data of the horizon elevation.

Images