Fault sand body identification method based on seismic attribute phase consistency

Abstract

The invention discloses a fault sand body identification method based on seismic attribute phase consistency, and the method comprises the steps: S1, extracting the root mean square attribute of a target horizon in a block, and obtaining basic input data; s2, performing normalization and transformation processing on the basic input data to obtain a radial component and an angle component of a phase consistency filter; s3, a multi-scale Log-Gabor filter is constructed; s4, circularly traversing different directions and different scales, calculating the filter response for the convolution of each direction and each scale, respectively accumulating the amplitude response of each scale as SumAn, accumulating the convolution result of an even number filter as SumE, accumulating the convolution result of an odd number filter as SumO, and then calculating a weighted mean response vector; s5, calculating phase consistency energy and phase consistency; s6, constructing covariance data of each point in the image, and calculating the maximum moment of the matrix according to the covariance data; and S7, on the basis of the weighted mean response vector obtained in the step S4, calculating a feature type, namely obtaining a lithologic feature.

Description

Technical Field

[0001] The present invention belongs to the technical fields of petroleum engineering and seismic exploration, and particularly relates to a method for identifying fault sand bodies based on seismic attribute phase consistency. Background Art

[0002] In the middle and late stages of offshore oilfield development, the complexity of oil and gas reservoirs has increased significantly. Especially for structural-lithologic oil and gas reservoirs in complex fault-block oil and gas fields, there are characteristics such as thin sand body thickness, rapid lateral change of reservoirs, complex fault boundaries, and diverse fault-sand combinations, which have become important reasons for the complex reservoir connectivity, ineffective injection and production in oilfield development, and enrichment of local remaining oil. Therefore, the rapid and accurate identification and characterization of faults and sand bodies can improve the accuracy of seismic interpretation in the study area, help solve the injection-production contradiction, and can largely realize the tapping of remaining oil and improve the recovery rate.

[0003] At present, seismic attributes such as coherence volume and variance volume in the industry play an important role in fault identification, and amplitude attributes play an important role in lithology identification. However, generally speaking, in seismic fine interpretation, it still depends on the experience of interpreters and the understanding of the geological conditions of the work area. At the same time, the workload is large and the subjectivity is strong, which is its major limitation. Especially in the in-depth exploration stage, the fault system is complex, the vertical and horizontal faults are severely interlaced, and the fault interpretation is more difficult. With the continuous increase in the complexity of oil and gas reservoirs and the continuous improvement of the exploration accuracy requirements, the requirements for identification accuracy have become increasingly challenging. Relying only on the current conventional attributes in the industry cannot meet all seismic data conditions. When the characteristics such as small fault throw of hidden faults, continuous seismic event axis, and weak contrast between adjacent traces lead to insufficient fracture identification accuracy of conventional technologies or even inability to identify, although amplitude attributes may reveal some geological characteristics under certain conditions, due to the influence of various factors, they cannot fully reflect the spatial distribution characteristics of sand bodies. In actual production, how to accurately identify and extract the spatial distribution of underground faults and sands using seismic data is an urgent problem to be solved in the comprehensive research of seismic reservoir fine description. Summary of the Invention

[0004] To solve the technical problems existing in the background art, the present invention aims to provide a phase consistency (PC) method based on seismic attributes for detecting the characteristics of geological bodies in seismic data. The results show that this method exhibits high precision in identifying faults. It can not only clearly display the distribution, extension characteristics, strike changes, and throw of faults, but also effectively identify the spatial distribution characteristics of sand bodies. This provides an effective interpretation tool for seismic fine interpretation. Especially in the exploration of complex fault-block oil and gas reservoirs, it has broad application prospects. Through this method, fine interpretation of faults and sand bodies is achieved, the accuracy of thin sand body reservoir prediction is improved, the reservoir geology research in the oilfield development stage is supported to shift from genesis to structure, the analysis of water flooding direction is guided, and a scientific basis is provided for the formulation of oil and gas field development plans, which helps to improve the final development effect and production efficiency of oil and gas fields.

[0005] To solve the technical problems, the technical solution of the present invention is as follows:

[0006] A method for identifying faults and sand bodies based on seismic attribute phase consistency, the method comprising:

[0007] S1: Extract the root mean square attribute of the target horizon in the block to obtain the basic input data;

[0008] S2: Normalize and transform the basic input data to obtain the radial component and angular component of the phase consistency filter;

[0009] S3: Based on the radial component and angular component obtained in step S2, construct a multi-scale Log-Gabor filter;

[0010] S4: Loop through different directions and different scales, calculate the filter response for each direction and each scale by convolution, and accumulate the amplitude responses of each scale as SumAn, accumulate the convolution results of even filters as SumE, accumulate the convolution results of odd filters as SumO, and then calculate the weighted mean response vector;

[0011] S5: Based on the weighted mean response vector obtained in step S4, calculate the phase consistency energy and phase consistency;

[0012] S6: According to the phase consistency calculation result, construct the covariance data of each point in the image, and calculate the maximum moment M of the matrix according to the covariance data, which is expressed as the fault significance result;

[0013] S7: Based on the weighted mean response vector obtained in step S4, calculate the feature type, that is, obtain the lithology feature.

[0014] Further, in the step S1, the root mean square attribute of the target horizon in the block is extracted by using the interlayer time window or the along-horizon time window method.

[0015] Further, in the step S2, first, calculate the normalized distance and polar angle of each point of the root mean square attribute to the image center, perform quadrant offset on the radius and polar angle so that the zero frequency of the spectrum is located at the center of the image, and remove the zero frequency point; then calculate the sine and cosine values of the polar angle, and construct the radial component and angular component for the filter.

[0016] Further, in the step S3, construct a multi-scale Log-Gabor filter, including:

[0017]

[0018] where r represents the radial frequency; f0 represents the center frequency of the filter; σ represents the standard deviation of the logarithmic frequency distribution, which is the bandwidth coefficient; θ represents the angular component; θ0 represents the center direction of the filter; σ θ represents the standard deviation of the direction distribution.

[0019] Further, in the step S4, the calculation of the weighted mean response vector specifically includes:

[0020]

[0021] MeanE and MeanO respectively represent the weighted means of the even and odd filter responses.

[0022] Further, in the step S5, the calculation of the phase consistency energy specifically includes:

[0023] Energy = Energy + E·MeanE + O·MeanO - abs(E·MeanO - O·MeanE)

[0024] where E and O respectively represent the real part and the imaginary part of the convolution result of the image and the filter;

[0025] The calculation of the phase consistency specifically includes:

[0026] Further, in the step S7, the calculation of the lithology feature specifically includes:

[0027]

[0028] Compared with the prior art, the advantages of the present invention are:

[0029] The phase consistency fault-sand identification method based on seismic attributes can identify fault features and sand body features simultaneously, comprehensively identify the planar distribution, extension length and throw of faults, accurately depict the spatial distribution characteristics of sand bodies, effectively separate fault and sandstone features, significantly improve the accuracy and reliability of seismic interpretation, reduce the multi-solution problem, reduce the workload of interpreters and human factors. Especially for the challenges of structural-lithologic oil and gas reservoirs in complex fault-block oil and gas fields, such as thin sand bodies, rapid lateral changes in reservoirs, complex fault boundaries and diverse fault-sand combinations, the present invention can stably provide structured information, enhance the reliability and stability of interpretation results, more accurately characterize the features of geological bodies, achieve fine interpretation of faults and sand bodies, improve the accuracy of seismic interpretation. The present invention provides support for reservoir geological research from genesis to structure, guides the analysis of water flooding direction and prediction of remaining oil distribution. Especially when calculating reserves, it can provide more accurate geological information, avoid problems with the calculation line of oil-bearing area, and contribute to the efficient development and management of oil fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 、Phase consistency fault detection results based on seismic attributes;

[0031] Figure 2 、Fault detection results of variance volume technique (conventional fault detection method);

[0032] Figure 3 、Root mean square attribute;

[0033] Figure 4 、Phase consistency featType attribute. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The following describes the specific embodiments of the present invention in conjunction with the embodiments:

[0035] It should be noted that the structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions under which the present invention can be implemented. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0036] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of clear narration, and are not used to limit the scope under which the present invention can be implemented. The change or adjustment of their relative relationship, without substantial change in the technical content, should also be regarded as the scope under which the present invention can be implemented.

[0037] Embodiment 1:

[0038] This embodiment provides the following technical solutions:

[0039] Step 1: Extract the root-mean-square attributes of the target horizons within the block. There are two ways to open the time window for attribute extraction. One is to select the horizon-horizon time window method. Generally, this method is used when the target horizon is a small layer or a sand layer. Taking the top and bottom interfaces of the target horizon as the reference planes, the window range is determined to extract the inter-layer attributes. The other is to open the time window along the layer. Taking the horizon trend surface as the center, the top and bottom surfaces of the time window are set. Generally, the time window is set not to exceed one wavelength length. The root-mean-square amplitude is very sensitive to extremely large amplitudes. The change in the root-mean-square amplitude can identify changes in formation lithology, such as the transition from sandstone to mudstone and the amplitude mutation shown by faults in seismic responses. At the same time, it is sensitive to the thickness of sand layers and hydrocarbon content, and is particularly suitable for complex fault-block oil and gas fields with thin sand body thickness and rapid lateral changes in reservoirs. Therefore, in order to enable the attributes to comprehensively reflect the characteristics of faults and sandstones, an appropriate time window is selected to extract the root-mean-square amplitude attributes as the basic input data.

[0040] Step 2: Initialize the phase consistency filter. First, calculate the normalized distance and polar angle from each point of the root-mean-square attribute to the center of the image, perform quadrant offset on the radius and polar angle so that the zero frequency of the spectrum is located at the center of the image, and remove the zero frequency points to avoid logarithmic calculation errors in subsequent steps. Then calculate the sine and cosine values of the polar angle, and construct the radial component (controlling the frequency band of the filter response, i.e., the bandwidth of the filter) and the angular component (controlling the directivity of the filter response) for the filter.

[0041] Step 3: Construct a multi-scale Log-Gabor filter

[0042]

[0043] Among them, r represents the radial frequency; f0 represents the center frequency of the filter; σ represents the standard deviation of the logarithmic frequency distribution, representing the bandwidth coefficient; θ represents the angular component; θ0 represents the center direction of the filter; σ θ represents the standard deviation of the direction distribution.

[0044] Step 4: Calculate the filter response

[0045] First, loop through different directions (orient) and different scales (scale). For each direction and each scale, calculate the filter response through convolution, and accumulate the amplitude responses of each scale as SumAn, accumulate the convolution results of even filters as SumE, and accumulate the convolution results of odd filters as SumO. Then calculate the weighted mean response vector.

[0046]

[0047] MeanE and MeanO represent the weighted means of the even and odd filter responses respectively, which reflect the phase information of the filter response.

[0048] Step Five: Calculate the phase consistency

[0049] First, calculate the phase consistency energy, Energy = Energy + E·MeanE + O·MeanO - abs(E·MeanO - O·MeanE); where E and O represent the real and imaginary parts of the convolution result of the image and the filter respectively, and then calculate the phase consistency

[0050] Step Six: Calculate the edge features, i.e., the fault features

[0051] First, construct the covariance data for each point in the image, and calculate the maximum moment M of the matrix based on the covariance data, which is expressed as the fault significance result.

[0052] Step Seven: Calculate the feature type, i.e., the lithology feature:

[0053]

[0054] It can be understood that the present invention enhances the ability to capture the planar distribution characteristics of faults. Figure 1 For the phase consistency fault detection result based on seismic attributes, it enhances the fault ductility and throw, eliminates the influence of lithology boundaries, and facilitates fault interpretation. Figure 2 The fault detection result of the conventional method (variance volume technique) cannot eliminate the influence of lithology boundaries, and the detection result has a high degree of fragmentation and cannot reflect the planar distribution characteristics of faults. The present invention enhances the ability to identify the distribution characteristics of sand bodies. Figure 3 The root mean square attribute can highlight the strong reflection characteristics in the seismic signal, but since different lithologies and fluids will affect the amplitude intensity, it cannot reflect the distribution characteristics of channel sands. Figure 4 For the phase consistency featType attribute, it can reflect the distribution characteristics of channel sands.

[0055] In an alternative embodiment, wavelet transform is used instead of the Log-Gabor filter to transform the image in multiple scales and multiple directions, so as to extract the multi-scale features of the image. However, the control of the frequency band is relatively weak, and it cannot accurately control the frequency band shape and bandwidth like the Log-Gabor filter, is sensitive to noise, and may require additional denoising steps.

[0056] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take 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.

[0057] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0058] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0060] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the above embodiments. Within the knowledge scope of those of ordinary skill in the art, various changes can be made without departing from the gist of the present invention.

[0061] Many other changes and modifications can be made without departing from the concept and scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A fault sand body identification method based on seismic attribute phase consistency, characterized in that The method includes: S1: Extract the root mean square attribute of the target horizon within the block to obtain the basic input data; S2: Normalize and transform the basic input data to obtain the radial component and angular component of the phase consistency filter; S3: Based on the radial component and angular component obtained in step S2, construct a multi-scale Log-Gabor filter; S4: Loop through different directions and different scales, calculate the filter response for each direction and each scale through convolution, and accumulate the amplitude responses of each scale as SumAn, accumulate the convolution results of the even filters as SumE, accumulate the convolution results of the odd filters as SumO, and then calculate the weighted mean response vector; S5: Based on the weighted mean response vector obtained in step S4, calculate the phase consistency energy and phase consistency; S6: According to the phase consistency calculation result, construct the covariance data for each point in the image, and calculate the maximum moment M of the matrix, which is expressed as the fault significance result; S7: Based on the weighted mean response vector obtained in step S4, calculate the feature type, that is, obtain the lithology feature.

2. The method for identifying fault sand bodies based on seismic attribute phase consistency according to claim 1, in step S1, the root mean square attribute of the target horizon within the block is extracted by using an interlayer time window or an along-layer open time window method.

3. The method for identifying fault sand bodies based on seismic attribute phase consistency according to claim 1, in step S2, first calculate the normalized distance and polar angle of each point of the root mean square attribute to the center of the image, perform quadrant offset on the radius and polar angle so that the zero frequency of the spectrum is located at the center of the image, and remove the zero frequency points; then calculate the sine and cosine values of the polar angle, and construct the radial component and angular component for the filter.

4. The method for identifying fault sand bodies based on seismic attribute phase consistency according to claim 1, in step S3, constructing a multi-scale Log-Gabor filter includes: where r represents the radial frequency; f0 represents the center frequency of the filter; σ represents the standard deviation of the logarithmic frequency distribution, representing the bandwidth coefficient; θ represents the angular component; θ0 represents the center direction of the filter; σ θ represents the standard deviation of the direction distribution.

5. The method for identifying fault sand bodies based on seismic attribute phase consistency according to claim 1, in step S4, the calculation of the weighted mean response vector specifically includes: MeanE and MeanO respectively represent the weighted means of the even and odd filter responses.

6. The method for identifying fault sand bodies based on seismic attribute phase consistency according to claim 1, in step S5, the calculation of the phase consistency energy specifically includes: Energy = Energy + E·MeanE + O·MeanO - abs(E·MeanO - O·MeanE) where E and O respectively represent the real part and the imaginary part of the convolution result of the image and the filter; The calculation of the phase consistency specifically includes:

7. The method for identifying fault sand bodies based on seismic attribute phase consistency according to claim 1, in step S7, the calculation of the lithology feature specifically includes:

Images