Fracture identification method and apparatus for weathered crust reservoirs

Abstract

The embodiment of the present application relates to the field of oil and gas geophysics, and discloses a weathered crust reservoir fracture identification method and device, comprising: obtaining prestack diffraction wave data volume of the weathered crust reservoir; performing seismic tensor volume calculation on the prestack diffraction wave data volume to obtain prestack diffraction wave data seismic tensor attribute volume; performing seismic ant calculation on the prestack diffraction wave data seismic tensor attribute volume to obtain tensor ant attribute based on the prestack diffraction wave data volume; and according to the tensor ant attribute based on the prestack diffraction wave data volume, describing the reservoir fracture in three-dimensional space to obtain fracture prediction data volume. The weathered crust reservoir fracture identification method disclosed in the present application solves the problem that the fracture-fissure detection result is affected by structural fluctuation, and improves the surface fissure network identification precision.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of oil and gas geophysics, and particularly to a method and apparatus for identifying fractures in weathered crust reservoirs. Background Technology

[0002] Weathered crust reservoirs are exposed for a long time and are affected by weathering and leaching and tectonic movement. The development of the reservoir is controlled by lithology, top surface structure and other factors. The weathered crust reservoirs are characterized by high porosity, high permeability and large thickness, and the reservoir properties change rapidly laterally.

[0003] The Ordovician carbonate rocks in the Tarim Oilfield exhibit typical weathering crust-composite karst development, with surface fracture networks in the shallow layers and karst conduits in the medium and deep layers, resulting in a well-developed overall fracture-cavity reservoir. As development progresses and the water content of the reservoirs increases, most of the remaining oil in the weathering crust reservoirs is concentrated within the surface weathering crust fracture networks, necessitating effective identification and description of these networks.

[0004] The top interface of the weathered crust of the Tarim River is an unconformity interface. It has been exposed to weathering and erosion for a long time, and the tectonic undulations are large. Conventional fracture-crack identification techniques, such as high-precision coherence, AFE, and ant tracking, are greatly affected by the tectonic undulations. They all show "two-sided" characteristics, that is, they indicate the tectonic boundary rather than the actual fracture network structure. Summary of the Invention

[0005] The purpose of this invention is to provide at least one method and apparatus for identifying fractures in weathered crust reservoirs, which can at least solve the problem that fracture identification results are affected by tectonic undulations, and at least improve the accuracy of fracture identification in areas with large tectonic undulations.

[0006] To address the aforementioned technical problems, at least one embodiment of this application provides a method for identifying fractures in weathered crust reservoirs, comprising:

[0007] Acquire pre-stack diffraction wave data volumes of weathered crust reservoirs;

[0008] Seismic tensor volume calculation is performed on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0009] Seismic ant calculations are performed on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume;

[0010] Based on the tensor ant properties of the pre-stack diffraction wave data volume, the reservoir fracture is characterized in three-dimensional space to obtain the fracture prediction data volume.

[0011] In one embodiment, the step of performing seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data includes:

[0012] The pre-stack diffraction wave data volume is subjected to filtering preprocessing to obtain the preprocessed pre-stack diffraction wave data volume; the filtering preprocessing includes at least one of diffusion filtering and guided filtering;

[0013] Seismic tensor volume calculation is performed on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0014] In one embodiment, the step of performing filtering preprocessing on the pre-stack diffraction data volume to obtain the preprocessed pre-stack diffraction data volume includes:

[0015] The pre-stack diffraction wave data volume is subjected to diffusion filtering to obtain the pre-stack diffraction wave data volume after diffusion filtering.

[0016] The pre-stack diffraction wave data volume after diffusion filtering is subjected to guided filtering to obtain the pre-processed pre-stack diffraction wave data volume.

[0017] In one embodiment, the step of performing seismic ant calculations on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume includes:

[0018] Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain the filtered seismic tensor attribute volume of the pre-stack diffraction wave data.

[0019] Seismic ant calculations are performed on the filtered pre-stack diffraction wave data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack diffraction wave data volume.

[0020] In one embodiment, the method further includes:

[0021] Acquire pre-stack depth migration seismic data volume;

[0022] Seismic tensor volume calculation is performed on the pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data.

[0023] Seismic ant calculations are performed on the seismic tensor attribute volume of the pre-stack depth migration seismic data to obtain tensor ant attributes based on the pre-stack depth migration seismic data volume.

[0024] The step of characterizing reservoir fractures in three-dimensional space based on the tensor ant properties of the pre-stack diffraction wave data volume to obtain fracture prediction data volume includes:

[0025] Tensor ant properties of the pre-stack depth migration seismic data volume are selected to characterize gentle regions in three-dimensional space, and tensor ant properties of the pre-stack diffraction wave data volume are selected to characterize steep regions in three-dimensional space, thus obtaining the fault prediction data volume.

[0026] In one embodiment, the step of performing seismic tensor volume calculation on the pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data includes:

[0027] The pre-stack depth migration seismic data volume is subjected to diffusion filtering to obtain the diffusion-filtered pre-stack depth migration seismic data volume.

[0028] The pre-stack depth migration seismic data volume after diffusion filtering is subjected to guided filtering to obtain the pre-processed pre-stack depth migration seismic data volume.

[0029] Seismic tensor volume calculation is performed on the pre-processed pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data.

[0030] The step of performing seismic ant calculations on the seismic tensor attribute volume of the pre-stack depth-migrated seismic data to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume includes:

[0031] Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack depth migration seismic data to obtain the filtered seismic tensor attribute volume of the pre-stack depth migration seismic data.

[0032] Seismic ant calculations are performed on the filtered pre-stack depth-migrated seismic data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume.

[0033] In one embodiment, the step of performing seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data includes:

[0034] Acquire pre-stack depth migration seismic data volume;

[0035] The tectonic undulation time difference of the in-phase axis of the target layer of the pre-stack depth migration seismic data volume is extracted by cross-correlation calculation;

[0036] The derivatives of the pre-stack depth migration seismic data volume are calculated along the main survey line, the connecting line, and time, respectively, to construct a seismic tensor field constrained by the tectonic undulation time difference.

[0037] The seismic tensor field is applied to the pre-stack depth migration seismic data volume and the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration data and the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0038] At least one embodiment of this application also provides a fracture identification device for weathered crust reservoirs, comprising:

[0039] The diffraction wave data acquisition module is used to acquire pre-stack diffraction wave data volumes of the weathered crust reservoir;

[0040] The diffraction tensor volume calculation module is used to perform seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0041] The diffraction ant calculation module is used to perform seismic ant calculation on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume.

[0042] The ant characterization module is used to characterize reservoir fractures in three-dimensional space based on the tensor ant properties of the pre-stack diffraction wave data volume, and obtain fracture prediction data volume.

[0043] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described fracture identification method for weathered crust reservoirs.

[0044] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying fractures in weathered crust reservoirs.

[0045] The embodiment of this application provides a fracture identification and apparatus for weathered crust reservoirs. It acquires pre-stack diffraction wave data volumes of the weathered crust reservoirs. These pre-stack diffraction wave data volumes reflect areas with significant structural undulations. Analysis based on this data can uncover the influence of structural undulations on fractures. Seismic tensor volume calculations are performed on the pre-stack diffraction wave data volumes to obtain seismic tensor attribute volumes. Seismic ant calculations are then performed on these seismic tensor attribute volumes to obtain tensor ant attributes based on the pre-stack diffraction wave data volumes. Based on these tensor ant attributes, reservoir fractures are characterized in three-dimensional space to obtain fracture prediction data volumes. This allows the fracture prediction data volumes to accurately characterize fracture morphology in areas with significant structural undulations. Therefore, the influence of structural undulations on fracture-fracture detection results is reduced, improving the identification accuracy of fractures in areas with significant structural undulations, resulting in more accurate surface fracture networks and clearer development patterns. This meets the needs of refined development and effectively guides well location adjustments and the construction of three-dimensional well networks. Attached Figure Description

[0046] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0047] Figure 1 This is a flowchart of a fracture identification method for weathered crust reservoirs provided in one embodiment of this application;

[0048] Figure 2 This is a cross-section comparison diagram of weathered crust reservoir fracture identification results based on pre-stack depth migration data and diffraction wave data.

[0049] Figure 3 This is a comparison chart of the early-stage fracture prediction results of the weathering crust of the target layer in a certain work area and the weathering crust fracture prediction results of this method. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0051] Weathered crust reservoirs are exposed for a long time and are affected by weathering and leaching and tectonic movement. The development of the reservoir is controlled by lithology, top surface structure and other factors. The weathered crust reservoirs are characterized by high porosity, high permeability and large thickness, and the reservoir properties change rapidly laterally.

[0052] The Ordovician carbonate rocks in the Tarim Oilfield exhibit typical weathering crust-composite karst development, with surface fracture networks in the shallow layers and karst conduits in the medium and deep layers, resulting in a well-developed overall fracture-cavity reservoir. As development progresses and the water content of the reservoirs increases, most of the remaining oil in the weathering crust reservoirs is concentrated within the surface weathering crust fracture networks, necessitating effective identification and description of these networks.

[0053] The weathered crust top interface of the Tarim River is an unconformity interface, exposed to weathering and erosion for a long time, with significant structural undulations. Conventional fracture-fracture identification techniques, such as high-precision coherence, AFE, and ant tracking, are greatly affected by structural undulations, exhibiting a "two-sided" characteristic, indicating structural boundaries rather than the actual fracture network structure. Therefore, the surface fracture network identification accuracy is low, the development pattern is unclear, making it difficult to meet the needs of refined development and unable to effectively guide well location adjustments and the construction of three-dimensional well networks.

[0054] Example 1:

[0055] The fracture identification method for weathered crust reservoirs in this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. The specific process can be as follows: Figure 1 As shown, it includes:

[0056] Step 110: Obtain pre-stack diffraction wave data volume of the weathered crust reservoir.

[0057] Specifically, during the propagation of seismic waves, some abrupt changes in lithology are encountered, such as fault edges, stratigraphic pinch-out points, and unconformity protrusions. These protrusions become new seismic sources, emitting spherical waves again, which propagate in all directions and form diffraction waves. For weathered crust reservoirs with large tectonic undulations, pre-stack diffraction wave data volumes are collected.

[0058] Step 120: Perform seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0059] Specifically, by extracting the time difference of the weathering crust reservoir phase axis along the main survey line and connecting lines, and calculating the derivatives in the three directions of the main survey line, connecting lines, and time, a seismic tensor field constrained by tectonic undulations is constructed. The seismic tensor attribute volume of the pre-stack diffraction wave data is then calculated, and the seismic tensor eigenvalue 2 can be selected as the preferred seismic tensor attribute volume for the pre-stack diffraction wave data. Using seismic tensor techniques, the seismic tensor attribute volume of the pre-stack diffraction wave data is calculated, where the seismic tensor field can be calculated using the following formula:

[0060]

[0061] In the formula, λ1, λ2, and λ3 are the derivatives of the seismic data volume in the x, y, and z directions. D is a diagonal matrix. Calculate the eigenvalues ​​and eigenvectors of D. The eigenvalues ​​are λ1, λ2, and λ3, respectively.

[0062] Step 130: Perform seismic ant calculation on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume.

[0063] Specifically, based on the seismic tensor attribute volume of pre-stack diffraction wave data, tensor ant attributes based on the pre-stack diffraction wave data volume are calculated using ant technology, and tensor ant attributes of different time windows of the target layer are extracted.

[0064] Step 140: Based on the tensor ant properties of the pre-stack diffraction wave data volume, the reservoir fracture is characterized in three-dimensional space to obtain the fracture prediction data volume.

[0065] In practical implementation, the weathered crust is prone to weathering and erosion in some high-lying areas. The undulating structure leads to many faults appearing on both sides of the bends, but these are not actually faults, but rather boundaries of structural undulations. Diffraction waves do not preserve continuous features, only the areas of variation, thus retaining regions with large structural undulations. Faults are identified based on diffraction wave data, highlighting faults in areas of energy anomalies. The reservoir fractures are then characterized in three-dimensional space to obtain a fracture prediction data volume.

[0066] This embodiment of the fracture identification method for weathered crust reservoirs acquires pre-stack diffraction wave data volumes of the weathered crust reservoirs. These pre-stack diffraction wave data volumes reflect areas with significant structural undulations. Analysis based on this data can uncover the influence of structural undulations on fractures. Seismic tensor volume calculations are performed on the pre-stack diffraction wave data volumes to obtain seismic tensor attribute volumes. Seismic ant calculations are then performed on these seismic tensor attribute volumes to obtain tensor ant attributes based on the pre-stack diffraction wave data volumes. Based on these tensor ant attributes, reservoir fractures are characterized in three-dimensional space to obtain fracture prediction data volumes. These fracture prediction data volumes accurately characterize fracture morphology in areas with significant structural undulations. This reduces the impact of structural undulations on fracture-fracture detection results, improves fracture identification accuracy in areas with significant structural undulations, makes surface fracture networks more accurate, and reveals development patterns more clearly, thus meeting the needs of refined development and effectively guiding well location adjustments and the construction of three-dimensional well networks.

[0067] Example 2

[0068] The fracture identification method for weathered crust reservoirs in this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. The specific process can be as follows: Figure 1 As shown, it includes:

[0069] Step 110: Obtain pre-stack diffraction wave data volume of the weathered crust reservoir.

[0070] Specifically, during the propagation of seismic waves, some abrupt changes in lithology are encountered, such as fault edges, stratigraphic pinch-out points, and unconformity protrusions. These protrusions become new seismic sources, emitting spherical waves again, which propagate in all directions and form diffraction waves. For weathered crust reservoirs with large tectonic undulations, pre-stack diffraction wave data volumes are collected.

[0071] Step 120: Perform seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0072] Specifically, by extracting the time difference of the weathering crust reservoir phase axis along the main survey line and connecting lines, and calculating the derivatives in the three directions of the main survey line, connecting lines, and time, a seismic tensor field constrained by tectonic undulations is constructed. The seismic tensor attribute volume of the pre-stack diffraction wave data is then calculated, and the seismic tensor eigenvalue 2 can be selected as the preferred seismic tensor attribute volume for the pre-stack diffraction wave data. Using seismic tensor techniques, the seismic tensor attribute volume of the pre-stack diffraction wave data is calculated, where the seismic tensor field can be calculated using the following formula:

[0073]

[0074] In the formula, λ1, λ2, and λ3 are the derivatives of the seismic data volume in the x, y, and z directions. D is a diagonal matrix. Calculate the eigenvalues ​​and eigenvectors of D. The eigenvalues ​​are λ1, λ2, and λ3, respectively.

[0075] Step 130: Perform seismic ant calculation on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume.

[0076] Specifically, based on the seismic tensor attribute volume of pre-stack diffraction wave data, tensor ant attributes based on the pre-stack diffraction wave data volume are calculated using ant technology, and tensor ant attributes of different time windows of the target layer are extracted.

[0077] Step 140: Based on the tensor ant properties of the pre-stack diffraction wave data volume, the reservoir fracture is characterized in three-dimensional space to obtain the fracture prediction data volume.

[0078] In practical implementation, the weathered crust is prone to weathering and erosion in some high-lying areas. The undulating structure leads to many faults appearing on both sides of the bends, but these are not actually faults, but rather boundaries of structural undulations. Diffraction waves do not preserve continuous features, only the areas of variation, thus retaining regions with large structural undulations. Faults are identified based on diffraction wave data, highlighting faults in areas of energy anomalies. The reservoir fractures are then characterized in three-dimensional space to obtain a fracture prediction data volume.

[0079] In one embodiment, the step of performing seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data includes:

[0080] The pre-stack diffraction wave data volume is subjected to filtering preprocessing to obtain the preprocessed pre-stack diffraction wave data volume; the filtering preprocessing includes at least one of diffusion filtering and guided filtering;

[0081] Seismic tensor volume calculation is performed on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0082] In this embodiment, before calculating the seismic tensor volume, the pre-stack diffraction wave data volume undergoes filtering preprocessing, mainly including diffusion filtering and structure-guided filtering. This ensures that the preprocessed pre-stack diffraction wave data volume retains fault information to the greatest extent possible. Based on this, the preprocessed pre-stack diffraction wave data volume is used to calculate the seismic tensor volume, resulting in a more accurate seismic tensor attribute volume. Structure-guided filtering is applied to the pre-stack diffraction wave data. Structure-guided filtering techniques include characterization of structural direction and non-stationary filtering methods. Two structure-guided strategies are employed: one utilizes the local dip characteristics of the seismic wave phase axis to construct a predictive data volume, which serves as the structural guide; the other directly determines the local seismic signal orientation through the local dip of the seismic wave phase axis. This embodiment constructs a guided filter. By automatically scanning and analyzing the waveform, amplitude, and azimuth information of seismic data, a structural guided volume representing the azimuth information of the seismic data is obtained. This data volume has three components, and the azimuth information of a point in space is represented by the combined intensity in the x, y, and z directions. Its advantage is that it preserves fault information to the greatest extent along the structural trend. For example, if the structural trend is high in the north and low in the south, or high in the east and low in the west, then the degree of fault preservation is better in this structural region. For conventional pre-stack depth migration data, waveform, amplitude, and azimuth variations are considered. For target imaging data such as diffracted waves, amplitude variations are the main consideration.

[0083] In one embodiment, the step of performing filtering preprocessing on the pre-stack diffraction data volume to obtain the preprocessed pre-stack diffraction data volume includes:

[0084] The pre-stack diffraction wave data volume is subjected to diffusion filtering to obtain the pre-stack diffraction wave data volume after diffusion filtering.

[0085] The pre-stack diffraction wave data volume after diffusion filtering is subjected to guided filtering to obtain the pre-processed pre-stack diffraction wave data volume.

[0086] In this embodiment, the preprocessing of the pre-stack diffraction wave data volume first involves diffusion filtering to remove noise and retain reflection information such as dip angle and stratigraphic contact relationship. Then, the diffused pre-stack diffraction wave data volume undergoes structure-guided filtering to maximize the retention of fault information along the structural trend by utilizing differences in seismic waveform, amplitude, and azimuth. The structure-guided filtering technique comprises two core components: characterization of structural direction and non-stationary filtering methods. Typically, two structure-guided strategies exist: one utilizes the local dip angle characteristics of the seismic wave phase axis to construct a predictive data volume, which serves as the structural guide; the other directly determines the local seismic signal orientation through the local dip angle of the seismic wave phase axis. In this embodiment, a guided filter is constructed. By automatically scanning and analyzing the waveform, amplitude, azimuth, and other information of the seismic data, a structural guided body representing the azimuth information of the seismic data is obtained. This data body has three components, which comprehensively represent the azimuth information of a point in space through the intensity of the x, y, and z directions. Its advantage is that it preserves fault information to the greatest extent along the structural trend. For example, if the structural trend is high in the north and low in the south, and high in the east and low in the west, then the degree of fault preservation is better in this structural region. For pre-stack depth migration data, waveform, amplitude, and azimuth changes are considered. For target imaging data such as diffraction waves, amplitude changes are the main consideration.

[0087] In one embodiment, the step of performing seismic ant calculations on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume includes:

[0088] Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain the filtered seismic tensor attribute volume of the pre-stack diffraction wave data.

[0089] Seismic ant calculations are performed on the filtered pre-stack diffraction wave data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack diffraction wave data volume.

[0090] In this embodiment, to address the issues of low signal-to-noise ratio and poor continuity in the seismic tensor attribute volume of pre-stack diffraction wave data, Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack diffraction wave data. Gaussian weighted filtering is performed on the x, y, and z directions (i.e., line, trace, and time directions). The x and y directions are assigned a first weight, and the z direction is assigned a second weight. The second weight is greater than the first weight, which enhances the continuity of the tensor in the longitudinal z direction, improves the signal-to-noise ratio of the attribute, enhances the longitudinal continuity, highlights the longitudinal continuity features of anomalies such as fractures, and maintains smoothness within a small range in the lateral direction.

[0091] In one embodiment, the method further includes:

[0092] Acquire pre-stack depth migration seismic data volume;

[0093] Seismic tensor volume calculation is performed on the pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data.

[0094] Seismic ant calculations are performed on the seismic tensor attribute volume of the pre-stack depth migration seismic data to obtain tensor ant attributes based on the pre-stack depth migration seismic data volume.

[0095] The step of characterizing reservoir fractures in three-dimensional space based on the tensor ant properties of the pre-stack diffraction wave data volume to obtain fracture prediction data volume includes:

[0096] Tensor ant properties of the pre-stack depth migration seismic data volume are selected to characterize gentle regions in three-dimensional space, and tensor ant properties of the pre-stack diffraction wave data volume are selected to characterize steep regions in three-dimensional space, thus obtaining the fault prediction data volume.

[0097] In this embodiment, pre-stack depth migration seismic data volume of the weathered crust reservoir is acquired. Seismic tensor volume calculations are then performed sequentially on the pre-stack depth migration seismic data volume. By extracting the time difference of the weathered crust reservoir's phase axis along the main survey line and connecting lines, the derivatives in the main survey line, connecting lines, and time directions are calculated to construct a seismic tensor field constrained by structural undulations. The seismic tensor attribute volume of the pre-stack depth migration seismic data is then calculated using seismic tensor techniques. The seismic tensor field can be calculated using... Calculate, where, This refers to the derivatives of the seismic data volume in the x, y, and z directions. D is a diagonal matrix. The eigenvalues ​​and eigenvectors of D are calculated, with eigenvalues ​​being λ1, λ2, and λ3, respectively. Seismic ant calculations are then performed on the seismic tensor attribute volume of the pre-stack depth-migrated seismic data to obtain the tensor ant attributes based on the pre-stack depth-migrated seismic data volume. Diffraction waves subtract reflected wave signals, retaining only spatial anomalies. Depth-domain data exhibits features such as continuous strata and wind tunnel-like spatial layers in its reflected wave data. Diffraction waves filter out continuous layers, retaining only individual, bead-like wind tunnel-like anomalies, eliminating continuous reflections. Weathering crusts are prone to weathering and erosion at higher elevations. The undulating structure causes many faults to appear on either side of these changes, but these are not actually faults, but rather boundaries of structural undulations. Diffraction waves do not retain continuous features, only retaining areas of change, preserving regions with significant structural undulations. Faults are then identified based on the diffraction wave data, identifying faults in areas of energy anomalies. For flat layers, fractures exist, but their energy levels do not reach the diffraction level, appearing as faults in the image layer. However, they lack energy characteristics in diffracted waves, and energy changes are crucial for extracting information. If the energy of the flat layer remains unchanged, it will not reach the level of strong reflection by diffracted waves, which will erase the smooth data. Flat areas will not produce diffraction, making it difficult to identify reservoir anomalies and weakening the ability to identify fractures. Therefore, for the flat areas of weathered crust reservoirs, tensor ant properties of pre-stack depth migration seismic data volumes are used for characterization, while for the steep areas of weathered crust reservoirs, tensor ant properties of pre-stack diffracted wave data volumes are used. In the characterization, different datasets represent different targets, and finally, they are spatially superimposed. The superimposed display after limiting the value range yields a fused image. The weathered crust reservoir fractures are characterized in three-dimensional space. Combined with actual drilling calibration and production dynamic data, the ant attribute threshold is optimized. The tensor ant attribute characterization of the pre-stack depth migration seismic data volume and the tensor ant attribute characterization of the pre-stack diffraction wave data volume are fused to obtain the weathered crust reservoir fracture prediction results, namely the fracture prediction data volume.

[0098] In one embodiment, the step of performing seismic tensor volume calculation on the pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data includes:

[0099] The pre-stack depth migration seismic data volume is subjected to filtering preprocessing to obtain the preprocessed pre-stack depth migration seismic data volume; the filtering preprocessing includes at least one of diffusion filtering and guided filtering.

[0100] Seismic tensor volume calculation is performed on the pre-processed pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data.

[0101] In this embodiment, pre-stack depth migration data is subjected to structure-guided filtering. Structure-guided filtering technology comprises two core components: characterization of structural direction and non-stationary filtering methods. Two structural guidance strategies are employed: one utilizes the local dip characteristics of the seismic wave phase axis to construct a predictive data volume, which serves as the structural guide; the other directly determines the local seismic signal orientation through the local dip of the seismic wave phase axis. In this embodiment, the structure-guided filtering automatically scans and analyzes the waveform, amplitude, and azimuth information of the seismic data to obtain a structural guide volume representing the azimuth information of the seismic data. This data volume has three components, using the intensity in the x, y, and z directions to comprehensively represent the azimuth information of a point in space. Its advantage lies in preserving fault information to the greatest extent along structural trends. For example, if a structural trend is high in the north and low in the south, or high in the east and low in the west, then fault preservation is better within this structural region. For conventional pre-stack depth migration data, waveform, amplitude, and azimuth variations are considered; for target imaging data volumes such as diffracted waves, amplitude variations are the primary consideration.

[0102] In one embodiment, the step of performing filtering preprocessing on the pre-stack depth migration seismic data volume to obtain the preprocessed pre-stack depth migration seismic data volume includes:

[0103] The pre-stack depth migration seismic data volume is subjected to diffusion filtering to obtain the diffusion-filtered pre-stack depth migration seismic data volume.

[0104] The pre-stack depth migration seismic data volume after diffusion filtering is subjected to guided filtering to obtain the pre-processed pre-stack depth migration seismic data volume.

[0105] In one embodiment, the step of performing seismic ant computation on the seismic tensor attribute volume of the pre-stack depth migration seismic data to obtain tensor ant attributes based on the pre-stack depth migration seismic data volume includes:

[0106] Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack depth migration seismic data to obtain the filtered seismic tensor attribute volume of the pre-stack depth migration seismic data.

[0107] Seismic ant calculations are performed on the filtered pre-stack depth-migrated seismic data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume.

[0108] In one embodiment, the step of performing seismic tensor volume calculation on the pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data includes:

[0109] The pre-stack depth migration seismic data volume is subjected to diffusion filtering to obtain the diffusion-filtered pre-stack depth migration seismic data volume.

[0110] The pre-stack depth migration seismic data volume after diffusion filtering is subjected to guided filtering to obtain the pre-processed pre-stack depth migration seismic data volume.

[0111] Seismic tensor volume calculation is performed on the pre-processed pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data.

[0112] The step of performing seismic ant calculations on the seismic tensor attribute volume of the pre-stack depth-migrated seismic data to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume includes:

[0113] Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack depth migration seismic data to obtain the filtered seismic tensor attribute volume of the pre-stack depth migration seismic data.

[0114] Seismic ant calculations are performed on the filtered pre-stack depth-migrated seismic data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume.

[0115] In this embodiment, before calculating the seismic tensor volume, the pre-stack depth migration seismic data volume undergoes filtering preprocessing, mainly including diffusion filtering and structure-guided filtering. This ensures that the preprocessed pre-stack depth migration seismic data volume retains fault information to the greatest extent possible. Based on this, the seismic tensor volume is calculated from the preprocessed pre-stack depth migration seismic data volume, resulting in a more accurate seismic tensor attribute volume. Before performing seismic ant calculation, Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack depth migration seismic data volume to address the issues of low signal-to-noise ratio and poor continuity, resulting in more accurate tensor ant attributes in the calculated pre-stack depth migration seismic data volume. In Gaussian weighted filtering, the longitudinal continuity of the tensor is enhanced, and the attribute signal-to-noise ratio is improved. Gaussian weighted filtering (x, y, z) is applied to the seismic tensor attribute volume of pre-stack depth migration seismic data. The x and y directions are assigned first weights, and the z direction is assigned a second weight, with the second weight being greater than the first. This enhances the longitudinal continuity of the tensor in the z-direction, highlighting the longitudinal continuity features of anomalies such as faults, while maintaining smoothness within a small range in the lateral direction. For pre-stack depth migration data, waveform, amplitude, and azimuth variations are considered. For target imaging data volumes such as diffraction waves, amplitude variations are the primary consideration.

[0116] In one embodiment, the step of performing seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data includes:

[0117] Acquire pre-stack depth migration seismic data volume;

[0118] The tectonic undulation time difference of the in-phase axis of the target layer of the pre-stack depth migration seismic data volume is extracted by cross-correlation calculation;

[0119] The derivatives of the pre-stack depth migration seismic data volume are calculated along the main survey line, the connecting line, and time, respectively, to construct a seismic tensor field constrained by the tectonic undulation time difference.

[0120] The seismic tensor field is applied to the pre-stack depth migration seismic data volume and the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration data and the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0121] In this embodiment, when calculating the seismic tensor volume, the time difference of structural undulations along the target layer's phase axis on the main survey line and crossline is extracted. The derivatives in the main survey line, crossline, and time directions are calculated to construct a seismic tensor field constrained by structural undulations. Seismic tensor attribute volumes of pre-stack depth migration seismic data and pre-stack diffraction data are calculated separately, and the seismic tensor eigenvalue 2 is selected as the final tensor attribute volume. The seismic phase axis orientation is calculated through cross-correlation, allowing the acquisition of structural undulation time differences on the inline (main survey line) and crossline (crossline). These time differences reflect the degree of structural change. The core of seismic tensor technology is detecting the rate of amplitude change between adjacent traces. Rapid changes in the weathering crust can cause amplitude anomalies, and these anomalies do not represent the true fault characteristics. Therefore, the structural undulation time difference is extracted and introduced into the tensor attribute calculation to obtain seismic tensor attributes that eliminate stratigraphic changes, thus reflecting the amplitude changes of adjacent traces as realistically as possible. Calculating the derivatives of pre-stack depth migration seismic data volumes in the linear, trace, and temporal directions yields their derivatives in the main survey line, connecting lines, and time directions. The constraint condition for structural undulations is to constrain the strike of the phase axis. Uneven phase axes, large strike time differences, and areas with relatively dipping strata will exhibit seismic tensor anomalies. Adding constraints primarily aims to eliminate these anomalous changes and highlight variations caused by anomalous energy. This approach is particularly effective for reservoirs exhibiting structural undulations on the weathered crust top surface and fracture-vuggy anomalies.

[0122] Example 3:

[0123] The fracture identification method for weathered crust reservoirs in this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. The specific process includes:

[0124] ① Data preparation: The acquired seismic data includes pre-stack depth migration seismic data volume, pre-stack diffraction wave data volume, stratigraphic data, and development dynamic data.

[0125] ② Seismic data preprocessing:

[0126] First, diffusion filtering is performed on the pre-stack depth migration seismic data volume and the pre-stack diffraction data volume to remove noise and retain reflection information such as dip angle and stratigraphic contact relationship.

[0127] Secondly, structural guidance filtering is carried out to preserve fault information to the greatest extent possible along the structural trend by taking into account differences in seismic waveforms, amplitudes, and azimuths.

[0128] Prestack depth migration data and prestack diffraction data were subjected to structural steering filtering. Structural steering filtering technology comprises two core components: characterization of structural direction and non-stationary filtering methods. Two common structural steering strategies are: one utilizes the local dip characteristics of seismic wave phase axes to construct a predictive data volume, which serves as the structural steering guide; the other directly determines the local seismic signal orientation through the local dip of the seismic wave phase axes. In this structural steering filtering, an automatic scanning analysis of the waveform, amplitude, and azimuth information of the seismic data yields a structural steering volume characterizing the azimuth information of the seismic data. This data volume has three components, representing the azimuth information of a point in space through the combined intensity in the x, y, and z directions. Its advantage lies in preserving fault information to the greatest extent possible along structural trends.

[0129] For example, if the tectonic trend is high in the north and low in the south, or high in the east and low in the west, then the faults will be better preserved in this tectonic region. For conventional pre-stack depth migration data, waveform, amplitude, and azimuth variations are considered. For target imaging data such as diffraction waves, the amplitude variation is the main consideration.

[0130] ③ Seismic tensor volume calculation: Based on the pre-stack depth migration seismic data volume and pre-stack diffraction wave data volume preprocessed in step ②, the seismic tensor data volume is calculated.

[0131] First, extract the time difference of structural undulations along the main survey line and connecting line directions of the target layer phase axis of the pre-stack depth migration seismic data volume.

[0132] Secondly, the derivatives of the pre-stack depth migration seismic data volume in the three directions of the main survey line, the connecting line, and time are calculated to construct a seismic tensor field based on tectonic undulation constraints. The purpose is to eliminate the problem of inaccurate seismic tensor extraction caused by stratigraphic tilt in the seismic tensor under tectonic constraints, highlight only amplitude anomalies, and improve the accuracy of fault and fracture-vuggy reservoir identification.

[0133] Finally, this seismic tensor field was applied to pre-stack depth migration data and pre-stack diffraction data to obtain the eigenvalues ​​and eigenvectors of different tensors, and the optimal tensor eigenvalue 2 was selected as the optimal seismic tensor attribute body.

[0134] Cross-correlation calculations of seismic phase axis orientation yield tectonic undulation time differences along both the inline (main survey line) and crossline (connecting lines), reflecting the degree of tectonic change. The core of seismic tensor technology lies in detecting the rate of amplitude change between adjacent traces. Rapid changes in the weathering crust cause amplitude anomalies, but these anomalies do not necessarily represent true fault characteristics. Therefore, this tectonic undulation time difference is extracted and incorporated into the tensor properties to obtain seismic tensor properties that eliminate stratigraphic variation, thus reflecting amplitude changes between adjacent traces as accurately as possible.

[0135] By calculating the derivatives of the pre-stack depth migration seismic data volume in the linear, trace, and time directions, we can obtain its derivatives in the main survey line, connecting line, and time directions.

[0136] The constraint condition for structural undulations is to constrain the strike of the phase axis. When the phase axis is uneven, the strike time difference is large, and the strata are relatively diagonal, seismic tensor anomalies will appear. The main purpose of adding constraints is to eliminate these anomalies and highlight the changes caused by anomalous energy. This is particularly effective for reservoirs with structural undulations on the weathered crust top surface and fracture-vuggy anomalies. Oil production seismic tensor technology calculates the seismic tensor properties of pre-stack depth migration and diffraction wave data separately. The seismic tensor field can be calculated using the following formula:

[0137]

[0138] These are the derivatives of the seismic data volume in the x, y, and z directions. D is a diagonal matrix. Calculate the eigenvalues ​​and eigenvectors of D. The eigenvalues ​​are λ1, λ2, and λ3.

[0139] ④ Gaussian weighted filtering of seismic tensor volume: Based on the seismic tensor attribute volumes of pre-stack depth migration data and pre-stack diffraction wave data obtained in step ③, a Gaussian kernel is first generated, and Gaussian weighted filtering is performed in the x, y, and z directions. A smaller standard deviation is given in the x and y directions, and a larger standard deviation is given in the z direction to generate a Gaussian kernel matrix. Next, the generated Gaussian kernel is convolved with the image, and the weighted average value at each position is calculated to achieve image blurring and smoothing. Finally, the seismic tensor attribute volume after Gaussian weighted processing is obtained.

[0140] Seismic tensor eigenvalues ​​are attribute data volumes. This data volume undergoes x, y, and z (line, trace, time) Gaussian weighted filtering, assigning smaller values ​​in the x and y directions and larger values ​​in the z direction to enhance vertical continuity and highlight the vertical continuity characteristics of anomalies such as faults. The purpose of Gaussian weighting is to enhance vertical continuity while maintaining smoothness within a small range in the lateral direction.

[0141] In the Gaussian filtering calculation, Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack diffraction data in three directions (x, y, and z) (i.e., line, trace, and time directions), resulting in a filtered pre-stack diffraction data seismic tensor attribute volume. The unfiltered pre-stack diffraction data seismic tensor attribute volume lacks continuity in the z-direction; Gaussian filtering enhances the z-direction attributes. In the Gaussian processing, a Gaussian kernel is generated, and this kernel is used to convolve the pre-stack diffraction data seismic tensor attribute volume, treating each trace as image data and enhancing the attributes in directions lacking numerical continuity.

[0142] ⑤ Seismic Ant Volume Calculation: Based on the Gaussian-weighted seismic tensor attribute volume obtained in step ④, reasonable ant tracking parameters are set individually, and ant attribute volumes are calculated based on the seismic tensor attributes of pre-stack depth migration data and the seismic tensor attributes of pre-stack diffracted waves, respectively. The ant calculation input requires attribute volumes of discontinuous classes. Ant tracking parameters can be tested according to actual conditions.

[0143] ⑥ Prediction of fractures in weathered crust reservoirs:

[0144] Based on the ant attribute data from step ⑤, ant attributes are extracted along the target layer at different time windows, and the weathering crust reservoir fractures are characterized in three-dimensional space. Combined with actual drilling calibration and production dynamic data, the ant attribute threshold is optimized.

[0145] For fracture prediction in gently sculpted areas, tensor ant properties of pre-stack depth-migrated seismic data are preferred. For fracture prediction in areas with large tectonic undulations, tensor ant properties of pre-stack diffraction wave data are preferred. The two are fused to obtain a fracture prediction data volume, and finally, fracture prediction results for weathered crust reservoirs that meet the actual exploration and development needs are obtained.

[0146] In this embodiment, the target layer attribute extraction can extract ant-like attributes within a certain time window of the target layer, which is a conventional attribute extraction method used in reservoir prediction. The ant-like attribute volume is essentially an ant-like volume based on tensor attributes. Production dynamic data represents the connectivity between wells; "dynamic" refers to the connectivity between wells A and B, while "static" refers to a clear fracture path between wells A and B, thus allowing for a reasonable selection of the ant-like attribute value range. The ant-like volume is calculated based on tensor attributes and is also called a tensor ant. Fusion involves characterizing the data in three-dimensional space, selecting different data for different regions, and then displaying the results.

[0147] In this embodiment, diffraction waves subtract the reflected wave signal, essentially retaining only spatial anomalies. Two types of data were used: conventional depth-domain data, whose reflected wave data includes continuous strata and wind tunnel-like layers. Diffraction waves filter out continuous layers, retaining only individual, bead-like wind tunnel-like anomalous reflections, eliminating continuous reflections. Weathering crusts are prone to weathering and erosion at higher elevations, and the undulating structure causes many faults to appear on either side of these changes. However, these are not actually faults, but rather boundaries of structural undulations. Diffraction waves do not retain continuous features, only the areas of change, preserving regions with significant structural undulations. Faults are then identified based on the diffraction wave data, pinpointing faults in areas of energy anomalies. For conventional flat layers, fractures exist, but their energy levels do not reach the diffraction level, appearing as faults in the image layer. However, they lack energy characteristics in diffraction waves, and energy variations are crucial for extracting information. If the energy of the flat layer does not change, it will not reach the level of strong reflection of diffracted waves. The diffracted waves will erase the flat data, and flat areas will not produce diffraction, making it difficult to identify reservoir anomalies and weakening the ability to identify fractures.

[0148] In the characterization process, different types of datasets represent different targets, and finally they are superimposed in space. The superimposed display after the value range is defined is used to obtain a fused image.

[0149] This embodiment first preprocesses the seismic data, mainly including diffusion filtering and tectonic-guided filtering, to preserve fault information to the greatest extent. Second, by extracting the time difference of the target layer's phase axis along the main survey line and connecting lines, the derivatives in the main survey line, connecting lines, and time directions are calculated to construct a seismic tensor field constrained by tectonic fluctuations. Seismic tensor attributes of pre-stack depth migration data and pre-stack diffraction wave data are calculated respectively, and the seismic tensor eigenvalue 2 is selected as the final tensor attribute volume. To address the low signal-to-noise ratio and poor continuity of the seismic tensor eigenvalues, Gaussian weighted filtering is performed to enhance the longitudinal continuity of the tensor and improve the attribute signal-to-noise ratio. This paper calculates tensor ant body attributes using ant technology based on tensor eigenvalue attribute volumes and extracts ant attributes from different time windows of the target layer. This allows for the characterization of weathered crust reservoir fractures in three-dimensional space. By combining actual drilling calibration and production dynamic data, the ant attribute thresholds are optimized. For fracture prediction in gently sloping areas, tensor ant attributes based on pre-stack depth migration seismic data are preferred; for fracture prediction in areas with significant structural undulations, tensor ant attributes based on pre-stack diffraction wave data are preferred. The fusion of these two methods yields the predicted fracture results for weathered crust reservoirs. This provides a sound technical method and approach for fracture identification in carbonate weathered crust reservoirs, demonstrating promising application prospects and widespread value. This embodiment improves the accuracy of carbonate weathered crust fracture identification, ensuring fractures better conform to geological patterns, reducing the influence of structural boundaries, and achieving identification results that meet the practical development needs of weathered crust reservoir connectivity path research.

[0150] In this embodiment, the appendix Figure 2 This image shows a well profile comparison of fracture identification results for weathered crust reservoirs in a certain work area based on pre-stack depth migration data and diffraction wave data. The upper part shows the fracture identification results based on pre-stack depth migration data, and the lower part shows the fracture identification results based on pre-stack diffraction wave data. It can be seen from the image that the fracture prediction results based on pre-stack depth migration data are better in areas with gentle structures, while the fracture prediction results based on pre-stack diffraction wave data are better in areas with large structural undulations. (Attached) Figure 3 The figure shows a comparison between the early weathering crust fault prediction results of the target layer in a certain work area and the weathering crust fault prediction results of this method. The left side shows the early weathering crust fault prediction results of the target layer in a certain work area, and the right side shows the weathering crust fault prediction results of this method. As can be seen from the figure, the weathering crust fault prediction effect of this method is significantly improved, the "two-sided" anomaly caused by the tectonic boundary is eliminated, and the overall distribution of the fault is more in line with the geological laws.

[0151] Example 4:

[0152] Another embodiment of this application relates to a fracture identification device for weathered crust reservoirs. The implementation details of the fracture identification device for weathered crust reservoirs in this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution. The fracture identification device for weathered crust reservoirs in this embodiment includes a diffraction wave data acquisition module, a diffraction tensor volume calculation module, a diffraction ant body calculation module, and an ant body characterization module.

[0153] The diffraction wave data acquisition module is used to acquire pre-stack diffraction wave data volumes of the weathered crust reservoir;

[0154] The diffraction tensor volume calculation module is used to perform seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0155] The diffraction ant calculation module is used to perform seismic ant calculation on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume.

[0156] The ant characterization module is used to characterize reservoir fractures in three-dimensional space based on the tensor ant properties of the pre-stack diffraction wave data volume, and obtain fracture prediction data volume.

[0157] It is worth mentioning that the fracture identification device for weathered crust reservoirs described in this embodiment can be used to perform any step of the embodiment of the fracture identification method for weathered crust reservoirs described above. All modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application, but this does not mean that other units do not exist in this embodiment.

[0158] Example 5:

[0159] Another embodiment of this application relates to a fracture identification device for weathered crust reservoirs. The implementation details of the fracture identification device for weathered crust reservoirs in this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution. The fracture identification device for weathered crust reservoirs in this embodiment includes a diffraction wave data acquisition module, a diffraction tensor volume calculation module, a diffraction ant body calculation module, and an ant body characterization module.

[0160] The diffraction wave data acquisition module is used to acquire pre-stack diffraction wave data volumes of the weathered crust reservoir;

[0161] The diffraction tensor volume calculation module is used to perform seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0162] The diffraction ant calculation module is used to perform seismic ant calculation on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume.

[0163] The ant characterization module is used to characterize reservoir fractures in three-dimensional space based on the tensor ant properties of the pre-stack diffraction wave data volume, and obtain fracture prediction data volume.

[0164] In one embodiment, the diffraction tensor volume calculation module includes a diffraction preprocessing submodule and a diffraction tensor calculation submodule;

[0165] The diffraction preprocessing submodule is used to perform filtering preprocessing on the pre-stack diffraction wave data volume to obtain the preprocessed pre-stack diffraction wave data volume; the filtering preprocessing includes at least one of diffusion filtering and guided filtering;

[0166] The diffraction tensor calculation submodule performs seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0167] In one embodiment, the diffraction preprocessing submodule includes a diffraction diffusion filtering module and a diffraction guiding filtering module.

[0168] The diffraction diffusion filtering module is used to perform diffusion filtering on the pre-stack diffraction wave data volume to obtain the pre-stack diffraction wave data volume after diffusion filtering.

[0169] The diffraction-guided filtering module is used to perform guided filtering on the pre-stack diffraction wave data volume after diffusion filtering to obtain the pre-processed pre-stack diffraction wave data volume.

[0170] In one embodiment, the diffracted ant body calculation module includes a diffracted Gaussian filter submodule and a diffracted ant body calculation submodule.

[0171] The diffraction Gaussian filtering submodule is used to perform Gaussian weighted filtering on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain the filtered seismic tensor attribute volume of the pre-stack diffraction wave data.

[0172] The diffraction ant calculation submodule is used to perform seismic ant calculation on the filtered pre-stack diffraction wave data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack diffraction wave data volume.

[0173] In one embodiment, the device further includes:

[0174] The migration data acquisition module is used to acquire pre-stack depth migration seismic data volumes;

[0175] The offset tensor volume calculation module is used to perform seismic tensor volume calculation on the pre-stack depth-migrated seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth-migrated seismic data.

[0176] The offset ant calculation module is used to perform seismic ant calculation on the seismic tensor attribute volume of the pre-stack depth-migrated seismic data to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume.

[0177] The ant characterization module is used to select the tensor ant properties of the pre-stack depth migration seismic data volume to characterize the gentle regions in three-dimensional space, and to select the tensor ant properties of the pre-stack diffraction wave data volume to characterize the steep regions in three-dimensional space, thereby obtaining the fault prediction data volume.

[0178] In one embodiment, the offset tensor volume calculation module includes an offset preprocessing submodule and an offset tensor calculation submodule:

[0179] The migration preprocessing submodule is used to perform filtering preprocessing on the pre-stack depth migration seismic data volume to obtain the preprocessed pre-stack depth migration seismic data volume; the filtering preprocessing includes at least one of diffusion filtering and guided filtering.

[0180] The offset tensor calculation submodule is used to perform seismic tensor volume calculation on the pre-processed pre-stack depth-migrated seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth-migrated seismic data.

[0181] In one embodiment, the offset preprocessing submodule includes an offset diffusion filtering module and an offset steering filtering module:

[0182] The migration diffusion filtering module is used to perform diffusion filtering on the pre-stack depth migration seismic data volume to obtain the diffusion-filtered pre-stack depth migration seismic data volume.

[0183] The migration-guided filtering module is used to perform guided filtering on the pre-stack depth migration seismic data volume after diffusion filtering to obtain the pre-processed pre-stack depth migration seismic data volume.

[0184] In one embodiment, the offset ant body calculation module includes an offset Gaussian filtering submodule and an offset ant body calculation submodule:

[0185] The offset Gaussian filtering submodule is used to perform Gaussian weighted filtering on the seismic tensor attribute volume of the pre-stack depth migration seismic data to obtain the filtered seismic tensor attribute volume of the pre-stack depth migration seismic data.

[0186] The offset ant volume calculation submodule is used to perform seismic ant calculation on the filtered pre-stack depth-migrated seismic data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume.

[0187] In one embodiment, the offset tensor volume calculation module includes an offset diffusion filter module, an offset guiding filter module, and an offset tensor calculation submodule; the offset ant volume calculation module includes an offset Gaussian filter submodule and an offset ant volume calculation submodule.

[0188] The migration diffusion filtering module is used to perform diffusion filtering on the pre-stack depth migration seismic data volume to obtain the diffusion-filtered pre-stack depth migration seismic data volume.

[0189] The migration-guided filtering module is used to perform guided filtering on the pre-stack depth migration seismic data volume after diffusion filtering to obtain the pre-processed pre-stack depth migration seismic data volume.

[0190] The offset tensor calculation submodule is used to perform seismic tensor volume calculation on the pre-processed pre-stack depth-migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth-migration seismic data.

[0191] The offset Gaussian filtering submodule performs Gaussian weighted filtering on the seismic tensor attribute volume of the pre-stack depth-migrated seismic data to obtain the filtered seismic tensor attribute volume of the pre-stack depth-migrated seismic data.

[0192] The offset ant calculation submodule performs seismic ant calculations on the filtered pre-stack depth-migrated seismic data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume.

[0193] In one embodiment, the diffraction tensor volume calculation module includes:

[0194] The pre-stack migration acquisition module is used to acquire pre-stack depth migration seismic data volumes;

[0195] The undulation time difference extraction module is used to extract the structural undulation time difference of the in-phase axis of the target layer of the pre-stack depth migration seismic data volume through cross-correlation calculation;

[0196] The tensor field construction module is used to calculate the derivatives of the pre-stack depth migration seismic data volume on the main survey line, the connecting line, and time, respectively, so as to construct a seismic tensor field under the constraint of the tectonic fluctuation time difference.

[0197] The tensor field application module is used to apply the seismic tensor field to the pre-stack depth migration seismic data volume and the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration data and the seismic tensor attribute volume of the pre-stack diffraction wave data.

[0198] It is worth mentioning that the fracture identification device for weathered crust reservoirs described in this embodiment can be used to perform any step of the embodiment of the fracture identification method for weathered crust reservoirs described above. All modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application, but this does not mean that other units do not exist in this embodiment.

[0199] Example 6:

[0200] Another embodiment of this application relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the fracture identification method for weathering crust reservoirs in the above embodiments.

[0201] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0202] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0203] Example 7:

[0204] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0205] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0206] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for identifying fractures in weathered crust reservoirs, characterized in that, include: Acquire pre-stack diffraction wave data volumes of weathered crust reservoirs; Seismic tensor volume calculation is performed on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data. Seismic ant calculations are performed on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume; Based on the tensor ant properties of the pre-stack diffraction wave data volume, the reservoir fracture is characterized in three-dimensional space to obtain the fracture prediction data volume.

2. The fracture identification method according to claim 1, characterized in that, The step of performing seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data includes: The pre-stack diffraction wave data volume is subjected to filtering preprocessing to obtain the preprocessed pre-stack diffraction wave data volume; the filtering preprocessing includes at least one of diffusion filtering and guided filtering; Seismic tensor volume calculation is performed on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data.

3. The fracture identification method according to claim 2, characterized in that, The step of performing filtering preprocessing on the pre-stack diffraction wave data volume to obtain the preprocessed pre-stack diffraction wave data volume includes: The pre-stack diffraction wave data volume is subjected to diffusion filtering to obtain the pre-stack diffraction wave data volume after diffusion filtering. The pre-stack diffraction wave data volume after diffusion filtering is subjected to guided filtering to obtain the pre-processed pre-stack diffraction wave data volume.

4. The fracture identification method according to claim 1, characterized in that, The step of performing seismic ant calculations on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume includes: Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain the filtered seismic tensor attribute volume of the pre-stack diffraction wave data. Seismic ant calculations are performed on the filtered pre-stack diffraction wave data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack diffraction wave data volume.

5. The fracture identification method according to claim 1, characterized in that, The method further includes: Acquire pre-stack depth migration seismic data volume; Seismic tensor volume calculation is performed on the pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data. Seismic ant calculations are performed on the seismic tensor attribute volume of the pre-stack depth migration seismic data to obtain tensor ant attributes based on the pre-stack depth migration seismic data volume. The step of characterizing reservoir fractures in three-dimensional space based on the tensor ant properties of the pre-stack diffraction wave data volume to obtain fracture prediction data volume includes: Tensor ant properties of the pre-stack depth migration seismic data volume are selected to characterize gentle regions in three-dimensional space, and tensor ant properties of the pre-stack diffraction wave data volume are selected to characterize steep regions in three-dimensional space, thus obtaining the fault prediction data volume.

6. The fracture identification method according to claim 5, characterized in that, The step of performing seismic tensor volume calculation on the pre-stack depth-migrated seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth-migrated seismic data includes: The pre-stack depth migration seismic data volume is subjected to diffusion filtering to obtain the diffusion-filtered pre-stack depth migration seismic data volume. The pre-stack depth migration seismic data volume after diffusion filtering is subjected to guided filtering to obtain the pre-processed pre-stack depth migration seismic data volume. Seismic tensor volume calculation is performed on the pre-processed pre-stack depth migration seismic data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration seismic data. The step of performing seismic ant calculations on the seismic tensor attribute volume of the pre-stack depth-migrated seismic data to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume includes: Gaussian weighted filtering is applied to the seismic tensor attribute volume of the pre-stack depth migration seismic data to obtain the filtered seismic tensor attribute volume of the pre-stack depth migration seismic data. Seismic ant calculations are performed on the filtered pre-stack depth-migrated seismic data seismic tensor attribute volume to obtain tensor ant attributes based on the pre-stack depth-migrated seismic data volume.

7. The fracture identification method according to claim 1, characterized in that, The step of performing seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data includes: Acquire pre-stack depth migration seismic data volume; The tectonic undulation time difference of the in-phase axis of the target layer of the pre-stack depth migration seismic data volume is extracted by cross-correlation calculation; The derivatives of the pre-stack depth migration seismic data volume are calculated along the main survey line, the connecting line, and time, respectively, to construct a seismic tensor field constrained by the tectonic undulation time difference. The seismic tensor field is applied to the pre-stack depth migration seismic data volume and the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack depth migration data and the seismic tensor attribute volume of the pre-stack diffraction wave data.

8. A fracture identification device for weathered crust reservoirs, characterized in that, include: The diffraction wave data acquisition module is used to acquire pre-stack diffraction wave data volumes of the weathered crust reservoir; The diffraction tensor volume calculation module is used to perform seismic tensor volume calculation on the pre-stack diffraction wave data volume to obtain the seismic tensor attribute volume of the pre-stack diffraction wave data. The diffraction ant calculation module is used to perform seismic ant calculation on the seismic tensor attribute volume of the pre-stack diffraction wave data to obtain tensor ant attributes based on the pre-stack diffraction wave data volume. The ant characterization module is used to characterize reservoir fractures in three-dimensional space based on the tensor ant properties of the pre-stack diffraction wave data volume, and obtain fracture prediction data volume.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the fracture identification method for weathered crust reservoirs as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the fracture identification method for weathered crust reservoirs as described in any one of claims 1 to 7.

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