Micro-amplitude identification method based on amplitude curvature

Abstract

The invention relates to the technical field of oil field reservoir prediction, in particular to a micro-amplitude identification method based on amplitude curvature. The method specifically comprises the following steps: S1, evaluating the quality of basic data; s2, carrying out amplitude curvature volume algorithm analysis based on a three-dimensional seismic volume; s3, preferably selecting a seismic low-frequency structure of wavelet transform to determine a structure trend background; and S4, the micro-amplitude structure is identified through fusion of the construction trend large background and the three-dimensional curvature body inflection points. According to the micro-amplitude identification method based on the amplitude curvature, on one hand, based on the amplitude curvature body of the three-dimensional seismic body, the corresponding curvature is calculated by using the change of the amplitude value in the seismic data; and on the other hand, fusion of the objective construction large background and the three-dimensional curvature body inflection point is realized, so that large-scale continuity of the low-amplitude construction is ensured, and change of imaging details of the low-amplitude construction is also ensured.

Description

Technical Field

[0001] This invention relates to the field of oilfield reservoir prediction technology, and in particular to a micro-amplitude identification method based on amplitude curvature. Background Technology

[0002] As oilfield exploration progresses, low-amplitude structural oil and gas reservoirs are receiving increasing attention. Currently, traditional low-amplitude structural identification techniques cannot effectively eliminate low-frequency structural background information contained in seismic data, and fail to consider the origins of low-amplitude structures in areas near faults characterized by weak tectonic activity and differential compaction, particularly in the central parts of depressions and far from major faults. Therefore, traditional methods are difficult to use and cannot accurately identify low-amplitude structures.

[0003] The three-dimensional curvature volume property, as the spatial second derivative of the seismic phase axis, can finely characterize new seismic properties such as the curvature of strata and the distribution characteristics of stress fields, making it suitable for fine structural interpretation, fracture prediction, and small-scale fault identification. However, this valuable information has previously been used only for reservoir structural description in interpretation work.

[0004] However, in practical applications, it has been found that existing technologies have at least the following problems:

[0005] In the first category of methods, traditional methods such as mean filtering and variable-rate mapping have difficulty controlling the scale of microstructural amplitude. In the second category of methods, the interpretation schemes for small-scale faults have a significant impact on the regional trend of microstructural amplitude. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] This invention provides a micro-amplitude identification method based on amplitude curvature to overcome the problems of inaccurate identification of structural size scale and difficulty in identifying small faults in the prior art, or the occurrence of false low-amplitude structures due to over-reliance on structural background data.

[0008] (II) Technical Solution

[0009] To address the above problems, this invention provides a micro-amplitude recognition method based on amplitude curvature, comprising:

[0010] Step S1: Basic data quality assessment;

[0011] Step S2: Amplitude curvature body algorithm analysis based on three-dimensional seismic bodies;

[0012] Step S3: Select the low-frequency seismic structure using wavelet transform to determine the tectonic trend background;

[0013] Step S4: Use the fusion of the overall trend background and the inflection points of the three-dimensional curvature body to identify micro-amplitude structures.

[0014] Furthermore, step S1 specifically includes: firstly, performing appropriate seismic filtering preprocessing on the seismic data; and when applying the filtering, selecting appropriate filtering window parameters, choosing a smaller aperture and calculation time window.

[0015] Further, step S2 specifically includes:

[0016] A slice of the layer is generated using the earthquake amplitude A, root mean square amplitude A, or impedance data volume, and then calculations are performed. and The first derivative;

[0017] Seismic data is processed from two-dimensional to three-dimensional space using formulas (1) and (2), and the changes in the amplitude values ​​of the anomalies are used to identify the boundary information changes of reservoir bright spots, channels and other geological anomalies in different orientations θ.

[0018] z(x, y) = ax 2 +by 2 +cxy+dx+ey+f (1)

[0019]

[0020] In the formula:

[0021] z(x,y) is the signal for constructing the surface; x is the horizontal coordinate; y is the vertical coordinate; It is the first-order derivative in the transverse direction; The first-order differential is the longitudinal differential; the usual boundary sharpening detection method can obtain its second-order differential by the Laplacian operator, and equation (2) is the average amplitude curvature of the second-order differential.

[0022] Furthermore, step S3 specifically includes:

[0023] By utilizing the improved Morlet wavelet, which exhibits good continuity in the large-scale time-frequency domain of seismic signals, wavelet transform and inverse transform were performed on the structural data to obtain a low-amplitude structural interpretation map that eliminates the low-frequency background of the structure. The low-frequency volume results after wavelet transform were comprehensively analyzed, and single-frequency volumes were extracted every 5Hz within the effective frequency band to identify the data volume of the dominant frequency band reflecting the structural and reservoir characteristics.

[0024] Furthermore, the dominant frequency is 20–45 Hz.

[0025] Furthermore, step S4 specifically includes:

[0026] Low-frequency models determine structural trends, curvature controls structural inflection points and trends, the difference between the two is a micro-amplitude feature, and the layer and trend surface are superimposed to find structural inflection points.

[0027] (III) Beneficial Effects

[0028] This invention provides a method for identifying micro-amplitude structures based on amplitude curvature. On one hand, it utilizes the amplitude curvature volume of a three-dimensional seismic body, calculating the corresponding curvature by analyzing changes in amplitude values ​​within seismic data. On the other hand, it integrates the objective structural background with the inflection points of the three-dimensional curvature volume, ensuring both the large-scale continuity of low-amplitude structures and the detailed variations in their mapping. Furthermore, this invention can accurately describe the location and distribution of micro-amplitude structures, guaranteeing the accuracy of actual measurement data from underground prototype geological models and providing technical support for quantitative prediction. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the micro-amplitude recognition method based on amplitude curvature according to an embodiment of the present invention;

[0030] Figure 2 For three-dimensional amplitude curvature space distribution;

[0031] Figure 3 This is a three-dimensional low-frequency seismic body model. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] like Figure 1-3 As shown, the present invention provides a micro-amplitude recognition method based on amplitude curvature, comprising:

[0034] (1) Three-dimensional amplitude curvature seismic data volume highlights the amplitude differences of geological origin. The amplitude volume curvature is calculated by using the transverse second derivative of the amplitude coherence component, which can highlight the characteristics such as changes, twists and abrupt amplitude changes that appear on conventional seismic profiles.

[0035] (2) Application of the improved Morlet wavelet transform in seismic tectonic background trend. The wavelet transform is an advanced short-time Fourier transform localized multi-scale transform analysis method that optimizes the contradiction between seismic resolution and time window, achieving high-frequency subdivision and low-frequency coarse subdivision, and can automatically adapt to the requirements of time-frequency signal analysis.

[0036] (3) Based on the three-dimensional curvature surface, control the morphology and structural inflection point of the low-frequency trend surface. In order to improve the detail variation of the structural mapping, the curvature surface is introduced to control the change of the structural inflection point, eliminate the influence of the large structural background trend surface, improve the low-amplitude structural details, and achieve the best structural inflection point search.

[0037] (4) Searching for tectonic inflection points based on the fusion of low-frequency trend surfaces and stratigraphic horizons. Micro-amplitude studies must ensure both a reasonable low-frequency background and an appropriate inflection point search size. Curvature surfaces are introduced to control the variation of tectonic inflection points, achieving optimal tectonic inflection point search.

[0038] Specifically, it includes the following steps:

[0039] Step S1: Basic data quality assessment;

[0040] This step specifically includes: first, performing appropriate seismic filtering preprocessing on the seismic data; and then, when applying the filtering, selecting suitable filtering window parameters, a smaller aperture, and a calculation time window.

[0041] Step S2: Amplitude curvature body algorithm analysis based on three-dimensional seismic bodies;

[0042] This step specifically includes:

[0043] A slice of the layer is generated using the earthquake amplitude A, root mean square amplitude A, or impedance data volume, and then calculations are performed. and The first derivative;

[0044] Seismic data is processed from two-dimensional to three-dimensional space using formulas (1) and (2), and the changes in the amplitude values ​​of the anomalies are used to identify the boundary information changes of reservoir bright spots, channels and other geological anomalies in different orientations θ.

[0045] z(x,y)=ax 2 +by 2 +cxy+dx+ey+f (1)

[0046]

[0047] In the formula:

[0048] z(x,y) is the signal for constructing the surface; x is the horizontal coordinate; y is the vertical coordinate; It is the first-order derivative in the transverse direction; The first-order differential is the longitudinal differential; the usual boundary sharpening detection method can obtain its second-order differential by the Laplacian operator, and equation (2) is the average amplitude curvature of the second-order differential.

[0049] Step S3: Optimize the low-frequency seismic tectonics using wavelet transform to determine the tectonic trend background; this specifically includes:

[0050] Utilizing the improved Morlet wavelet's excellent continuity in the large-scale time-frequency domain of seismic signals, wavelet transform and inverse transform were performed on the structural data to obtain a low-amplitude structural interpretation map eliminating the low-frequency background of the structure. A comprehensive analysis of the low-frequency volume results after the wavelet transform was conducted, and single-frequency volumes were extracted every 5 Hz within the effective frequency band to identify the dominant frequency bands reflecting structural and reservoir characteristics. These dominant frequencies range from 20 to 45 Hz.

[0051] Step S4: Use the fusion of the overall trend background and the inflection points of the three-dimensional curvature body to identify micro-amplitude structures.

[0052] Furthermore, step S4 specifically includes:

[0053] Low-frequency models determine structural trends, curvature controls structural inflection points and trends, the difference between the two is a micro-amplitude feature, and the layer and trend surface are superimposed to find structural inflection points.

[0054] The low-frequency trend surface should not be too large, as excessive smoothness leads to numerous and chaotic micro-amplitudes; nor should it be too small, as being too close to the original structural trend makes it difficult to identify micro-amplitude structures.

[0055] In summary, this invention first preprocesses basic data to eliminate differences in amplitude curvature caused by seismic factors, highlighting the structural characteristics of amplitude curvature. Secondly, based on the amplitude curvature of dip amplitude, local undulations on the stratigraphic level can be amplified, providing a basis for searching structural inflection points. Thirdly, low-frequency background based on wavelet transform is used to determine large-scale structural trends, ensuring more accurate large-scale structural morphology and more accurate identification of structural inflection points. Finally, high-precision structural mapping using small grids is achieved. This invention, on the one hand, is based on the amplitude curvature volume of a three-dimensional seismic body, using changes in amplitude values ​​in seismic data to calculate the corresponding curvature; on the other hand, it achieves the fusion of the objective structural background with the inflection points of the three-dimensional curvature volume, ensuring both the large-scale continuity of low-amplitude structures and the detailed variations in low-amplitude structural mapping. Simultaneously, this invention applies three-dimensional amplitude curvature technology, solving the problem of inaccurate structural inflection points in conventional structural mapping, and accurately identifying micro-amplitude structures, guiding horizontal well trajectory design with significant results. Furthermore, this invention can accurately describe the location and distribution of micro-amplitude structures, ensuring the accuracy of actual measurement data from underground prototype geological models, providing technical support for quantitative prediction.

[0056] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the present invention, and the patent protection scope of the present invention should be defined by the claims.

Claims

1. A micro-amplitude recognition method based on amplitude curvature, characterized in that, The application relates to a method for identifying micro-amplitude structure of reservoirs. The method comprises the following steps: S1, basic data quality evaluation; S2, amplitude curvature body algorithm analysis based on three-dimensional seismic body; S3, low-frequency structure determination of preferred wavelet transformation to determine structure trend background; 2. The micro-amplitude recognition method based on amplitude curvature according to claim 1, wherein, S4, fusion of structure trend background and three-dimensional curvature body inflection point to identify micro-amplitude structure.

3. The amplitude curvature based microamplitude identification method of claim 1, wherein, The step S1 specifically comprises the following steps: firstly, performing appropriate seismic filtering pretreatment on seismic data; when the filtering is applied, appropriate filtering window parameters are selected, and a smaller aperture and a calculation time window are selected. A slice of a horizon is generated using seismic amplitude A, root mean square amplitude A or impedance data volume, and then the first derivative of and is calculated. The step S2 specifically comprises the following steps: z(x,y) = ax 2 + by 2 + cxy + dx + ey + f (1) Seismic data is converted from two-dimensional to three-dimensional space through formulas (1) and (2), and the boundary information change of highlights, river channels and other geological anomalies of reservoirs in different directions theta is identified by using the change abnormal amplitude value; In the formulas: is a transverse first-order differential; is a longitudinal first-order differential; the usual edge-sharpening detection method takes its second-order differential by a Laplacian operator, and the equation (2) is an average amplitude curvature of the second-order differential.

4. The amplitude curvature based microamplitude identification method of claim 1, wherein, z(x, y) is a structure surface signal; x is a horizontal coordinate; and y is a vertical coordinate. The step S3 specifically comprises the following steps:

5. The micro-amplitude recognition method based on amplitude curvature according to claim 4, wherein, The structure data is subjected to wavelet transformation and inverse transformation by using the improved Morlet wavelet which has good continuity in the large-scale time-frequency domain of the seismic signal, a low-amplitude structure interpretation diagram eliminating the low-frequency background of the structure is obtained, the low-frequency body result after the wavelet transformation is comprehensively analyzed, a single-frequency body is extracted in every 5Hz in the effective frequency band, and a data body reflecting the advantage frequency band of the structure and reservoir characteristics is found out.

6. The amplitude curvature based microamplitude identification method of claim 1, wherein, The advantage frequency is 20-45Hz. The step S4 specifically comprises the following steps: The low-frequency model determines the structure trend, the curvature controls the structure inflection point and the trend, the difference between the former two is the micro-amplitude feature, the horizon is superimposed with the trend surface, and the structure inflection point is searched.

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