Carbonate fractured-vug reservoir prediction method and device based on fracture-vug intensity factor

Abstract

The application discloses a kind of carbonate rock fracture-vug reservoir prediction method and device based on fracture-vug intensity factor, can be used in geophysical exploration field, the method includes: according to the plane range and the depth range of purpose layer of poststack seismic data body, determine the original seismic data body in the range of poststack seismic data body, carry out multi-window dip scanning to original seismic data body, according to the dip angle body and azimuth body obtained, original seismic data body is filtered, and the original seismic data body after filtering is carried out secondary multi-window dip scanning;Determine first scanning window and the relevant seismic data body corresponding to first scanning window, obtain the coherent calculation result by eigenvalue coherence calculation, calculate fracture-vug intensity factor, and further determine carbonate rock fracture-vug reservoir, and the fracture-vug intensity factor is used to describe the fracture-vug development degree in fracture-vug reservoir and the influence of fracture-vug development degree on reservoir performance.The application can effectively improve carbonate reservoir prediction accuracy.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration, and in particular to a method and apparatus for predicting carbonate fracture-vuggy reservoirs based on fracture-vuggy strength factors. Background Technology

[0002] This section is intended to provide background or context for embodiments of the present invention. The description herein is not intended to imply that it is prior art simply because it is included in this section.

[0003] Reservoir prediction plays a crucial role in the petroleum exploration and development industry, which begins with reservoir discovery. The main components of reservoir prediction include the planar location, subsurface depth, spatial distribution range, spatial distribution morphology, velocity, density, porosity, and permeability of the reservoir. Reservoir prediction is a technique that uses well logging and seismic information, guided by geological theory, to macroscopically describe the spatial distribution and geometric morphology of oil and gas reservoirs and laterally predict their microscopic characteristics. Typically, it varies with the different stages of petroleum exploration. Different stages of exploration have different objectives and tasks, different focuses, and varying levels of difficulty in reservoir prediction. In the early stages of oil exploration, structural exploration was the main focus, as reservoirs were thick and easy to identify. However, as exploration progressed, easier targets (such as shallow, simple structural oil and gas reservoirs) became increasingly scarce. Today, we face more and more complex exploration targets that are "difficult, hidden, and deep," such as highly heterogeneous carbonate reservoirs that are difficult to explore due to their strong concealment. The key issue is that these highly heterogeneous reservoirs, such as highly heterogeneous carbonate reservoirs, are difficult to predict and characterize.

[0004] Currently, in actual exploration, especially in the exploration of carbonate reservoirs, the understanding of the sedimentary development model of carbonate reservoirs is basically based on the old clastic sandstone sedimentary model, which is not in line with reality. Carbonate reservoirs are greatly affected by tectonic fault dissolution, and their spatial distribution is often characterized by closely interdependent fractures and cavities, vertically varying elevations, and horizontally unstable thickness variations, resulting in irregular shapes. Using conventional seismic reservoir prediction methods to invert continuous layered carbonate reservoirs based on sandstone sedimentary models is not consistent with geological laws, and the reliability of the prediction results is low. Summary of the Invention

[0005] This invention provides a method for predicting fractured-vuggy carbonate reservoirs based on fracture-vuggy strength factors, to improve the accuracy of carbonate reservoir prediction and increase the success rate of exploration and development of fractured carbonate oil and gas reservoirs. The method includes:

[0006] Determine the scope of the work area to be measured, determine the planar scope of the post-stack seismic data volume based on the scope of the work area, and determine the target layer depth range of the post-stack seismic data volume based on the planar scope of the post-stack seismic data volume.

[0007] Based on the planar extent and target layer depth range of the post-stack seismic data volume, the original seismic data volume within the post-stack seismic data volume range is determined, and a multi-window dip scan is performed on the original seismic data volume to obtain the dip volume and azimuth volume.

[0008] The original seismic data volume is filtered based on the dip volume and azimuth volume, and a second multi-window dip scan is performed on the filtered original seismic data volume.

[0009] Based on the results of the second-stage multi-window dip scanning of the original seismic data volume, the first scanning window and the corresponding seismic data volume are determined. Based on the first scanning window and the corresponding seismic data volume, intrinsic coherence calculation is performed to obtain the coherence calculation results. The first scanning window is the scanning window used for image filtering in the second-stage multi-window dip scanning.

[0010] Based on the coherent calculation results, the fracture-vuggy strength factor is calculated to obtain the fracture-vuggy strength factor data volume. Based on the fracture-vuggy strength factor data volume, carbonate fracture-vuggy reservoirs are identified. The fracture-vuggy strength factor is used to describe the degree of fracture-vuggy development in fracture-vuggy reservoirs and the influence of the degree of fracture-vuggy development on reservoir performance.

[0011] This invention also provides a carbonate rock fractured-vuggy reservoir prediction device based on fracture-vuggy strength factor, to improve the prediction accuracy of carbonate rock reservoirs and increase the success rate of exploration and development of fractured oil and gas reservoirs in carbonate rocks. The device includes:

[0012] The data volume determination module is used to determine the work area range of the work area to be measured, determine the planar range of the post-stack seismic data volume based on the work area range, and determine the target layer depth range of the post-stack seismic data volume based on the planar range of the post-stack seismic data volume.

[0013] The first dip scanning module is used to determine the original seismic data volume within the scope of the post-stack seismic data volume based on the planar range and the depth range of the target layer, and to perform multi-window dip scanning on the original seismic data volume to obtain the dip volume and azimuth volume.

[0014] The second dip angle scanning module is used to filter the original seismic data volume based on the dip angle volume and azimuth angle volume, and to perform a second multi-window dip angle scan on the filtered original seismic data volume.

[0015] The coherence calculation module is used to determine the first scanning window and the corresponding seismic data volume based on the secondary multi-window dip scan results of the original seismic data volume, and to perform intrinsic coherence calculation based on the first scanning window and the corresponding seismic data volume to obtain the coherence calculation result. The first scanning window is the scanning window used for image filtering in the secondary multi-window dip scan.

[0016] The prediction module is used to calculate the fracture-vuggy strength factor based on the coherence calculation results, obtain the fracture-vuggy strength factor data volume, and determine the carbonate fracture-vuggy reservoir based on the fracture-vuggy strength factor data volume. The fracture-vuggy strength factor is used to describe the degree of fracture-vuggy development in the fracture-vuggy reservoir and the influence of the degree of fracture-vuggy development on the reservoir performance.

[0017] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for predicting carbonate fractured-vuggy reservoirs based on fracture-vuggy strength factors.

[0018] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting carbonate fractured-vuggy reservoirs based on fracture-vuggy strength factors.

[0019] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for predicting carbonate fractured-vuggy reservoirs based on fracture-vuggy strength factors.

[0020] In this embodiment of the invention, the scope of the work area to be measured is determined; the planar scope of the post-stack seismic data volume is determined based on the scope of the work area; the target layer depth range of the post-stack seismic data volume is determined based on the planar scope of the post-stack seismic data volume; the original seismic data volume within the scope of the post-stack seismic data volume is determined based on the planar scope and the target layer depth range of the post-stack seismic data volume; a multi-window dip scan is performed on the original seismic data volume to obtain dip volume and azimuth volume; the original seismic data volume is filtered based on the dip volume and azimuth volume; a second multi-window dip scan is performed on the filtered original seismic data volume; based on the original seismic data volume... The results of the secondary multi-window dip scan are used to determine the first scan window and the corresponding seismic data volume. Based on the first scan window and the corresponding seismic data volume, intrinsic coherence calculation is performed to obtain the coherence calculation results. The first scan window is the scan window used for image filtering in the secondary multi-window dip scan. Based on the coherence calculation results, the fracture-vuggy strength factor is calculated to obtain the fracture-vuggy strength factor data volume. Based on the fracture-vuggy strength factor data volume, carbonate fracture-vuggy reservoirs are determined. The fracture-vuggy strength factor is used to describe the degree of fracture-vuggy development in fracture-vuggy reservoirs and the influence of the degree of fracture-vuggy development on reservoir performance. In this way, by determining the planar range and target layer depth range of the post-stack seismic data volume, the original seismic data volume is determined. The original seismic data volume is then filtered based on the multi-window dip scanning results. A second multi-window dip scanning is performed on the filtered original seismic data volume, and coherence calculations are performed based on the scanning results to determine the fracture-vuggy strength factor, thereby identifying carbonate fracture-vuggy reservoirs. This enables three-dimensional prediction of the spatial distribution of carbonate reservoirs, effectively improving the prediction accuracy of carbonate reservoirs and increasing the success rate of exploration and development of fractured oil and gas reservoirs in carbonate reservoirs. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0022] Figure 1 This is a flowchart of the carbonate rock fractured-vuggy reservoir prediction method based on fracture-vuggy strength factor provided in the embodiments of the present invention;

[0023] Figure 2 This is a schematic diagram of discrete scanning of the reflective layer tilt angle provided in an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram illustrating the search process for the maximum coherence value window provided in an embodiment of the present invention;

[0025] Figure 4 This is a plan view showing the size of the target working area provided in this embodiment of the invention;

[0026] Figure 5a This is a structural diagram of the working target area lamp top provided in an embodiment of the present invention;

[0027] Figure 5b This is a diagram showing the structure of the lamp base in the target working area provided in this embodiment of the invention;

[0028] Figure 6a This is the original seismic profile before denoising provided in this embodiment of the invention;

[0029] Figure 6b This is a denoised seismic profile provided in an embodiment of the present invention;

[0030] Figure 7 This is a coherent cross-sectional view of the working target area through the main survey line 5802 provided in this embodiment of the invention;

[0031] Figure 8a This is a cross-sectional view of the carbonate rock fracture-cavity reservoir through the main survey line 5802 in the target area provided in this embodiment of the invention;

[0032] Figure 8b-1 , Figure 8b-2 , Figure 8b-3 This is an interpretation diagram of the working target area through the main survey line 5802PSDM seismic profile and reservoir calibration provided in this embodiment of the invention;

[0033] Figure 9 This is a plan view of the reservoir distribution in the target working area, Lamp 2, with two fractures and holes, provided in this embodiment of the invention.

[0034] Figure 10 This is a schematic diagram of a carbonate rock fractured-vuggy reservoir prediction device based on fracture-vuggy strength factor provided in an embodiment of the present invention.

[0035] Figure 11 This is a structural block diagram of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0037] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0038] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0039] Underground strata are mostly distributed in layers, formed by sediments from different geological periods through a long process of deposition, burial, compaction, diagenesis, and subsequent tectonic deformation, weathering, and erosion. Simultaneously, the remains of dead animals and plants, embedded in soil and sand, are buried underground. Under certain underground temperature conditions, the buried organic matter undergoes a series of biochemical and catalytic degradation processes, transforming into natural gas and oil. This generated oil and natural gas then migrates to strata with porous, fractured, and karst cavernous storage spaces, where, under suitable capping conditions, it is preserved. This forms oil fields and oil and gas reservoirs awaiting development. Strata containing oil and natural gas are called oil and gas-bearing reservoirs, while strata without such reserves are called non-oil and gas-bearing reservoirs, which are often strata with a certain degree of porosity and permeability, such as carbonate rock strata.

[0040] The purpose of oil exploration is to comprehensively utilize geophysical exploration methods, gravity / magnetic exploration methods, geophysical logging exploration methods, geological exploration methods, and geochemical exploration methods to find underground reservoirs containing oil and natural gas; this is reservoir prediction in a broad sense. Among these, the most widely used method in practice is to comprehensively utilize various seismic exploration methods to qualitatively or quantitatively predict reservoirs; this is reservoir prediction in the usual sense.

[0041] Reservoir prediction plays a crucial role in the petroleum exploration and development industry, which begins with reservoir discovery. The main components of reservoir prediction include the planar location, subsurface depth, spatial distribution range, spatial distribution morphology, velocity, density, porosity, and permeability of the reservoir. Reservoir prediction is a technique that uses well logging and seismic information, guided by geological theory, to macroscopically describe the spatial distribution and geometric morphology of oil and gas reservoirs and laterally predict their microscopic characteristics. Typically, it varies with the different stages of petroleum exploration. Different stages of exploration have different objectives and tasks, different focuses, and varying levels of difficulty in reservoir prediction. In the early stages of oil exploration, structural exploration was the main focus, as reservoirs were thick and easy to identify. However, as exploration progressed, easier targets (such as shallow, simple structural oil and gas reservoirs) became increasingly scarce. Today, we face more and more complex exploration targets that are "difficult, hidden, and deep," such as highly heterogeneous carbonate reservoirs that are difficult to explore due to their strong concealment. The key issue is that these highly heterogeneous reservoirs, such as highly heterogeneous carbonate reservoirs, are difficult to predict and characterize.

[0042] Geophysical seismic technology is a key technology for oil and gas exploration. Through the inversion and analysis of noisy and incomplete geophysical data, it ultimately provides a qualitative or quantitative description of geological targets. The main components include: geological structure imaging, lithological and physical property parameter identification, and reservoir fluid type prediction. Around these three objectives, some geophysical theories and technologies have developed rapidly and achieved significant results in oil and gas exploration and development. However, research on some geophysical advanced reserve technologies is relatively weak. Major breakthroughs in oil and gas exploration depend on advancements in geophysical technology, making the development and research of seismic frontier technologies of great value.

[0043] With frequent and complex changes in sedimentary age, sedimentary environment, and tectonic movement, as well as the complex and varied genesis of reservoirs, various types of reservoirs can be formed, ranging from homogeneous and simple to relatively complex or strongly heterogeneous. Their seismic responses may be characterized by a single and obvious feature, or by a complex response due to the combined influence of multiple factors, or by variations in direction and orientation, making the combined influence of multiple factors extremely complex. Therefore, for reservoirs under different conditions, their corresponding seismic response information can be used, and certain parameters can be solved through appropriate mathematical methods to characterize and describe the reservoirs.

[0044] Generally, conventional seismic reservoir prediction techniques typically include the following:

[0045] The first type is the conventional post-stack seismic attribute reservoir prediction technique.

[0046] This technology can be broadly categorized into three types: The first type is the direct method or seismic profile method, which is what you see is what you get. It utilizes the visual reflection characteristics of the reservoir (obvious and easily identifiable seismic reflection characteristics on seismic profiles, such as obvious reservoir seismic reflection amplitudes and waveform structures with specific characteristics) to directly identify the planar and cross-sectional distribution of the reservoir through visual observation of the seismic profile. The second type is the indirect method or seismic attribute analysis method, which uses signal analysis or image processing of the seismic response of the reservoir to solve for physical parameters with certain physical meanings, such as smooth reflection intensity, instantaneous real amplitude, instantaneous amplitude, average absorption coefficient, frequency, sweet spot, average vibration energy, target layer peak width, power spectrum integral, information entropy, texture, etc., to indirectly predict the planar distribution of the reservoir. The third type is the characteristic amplitude response analysis method, which is a special type of method that uses the characteristic seismic reflection amplitudes with obvious seismic reflection characteristics such as "bright spots," "dark spots," and "flat spots" caused by the saturation of oil and gas reservoirs to predict saturated or undersaturated oil and gas reservoirs.

[0047] The second method is seismic impedance reservoir inversion technology.

[0048] This technology can be broadly divided into two categories: The first category is post-stack impedance reservoir inversion technology, which basically uses well logging to establish an initial wave impedance model, and then obtains an initial reflection coefficient model. The wavelet and the initial reflection coefficient model are convolved to obtain the initial model synthetic seismic record. The residual between the initial model synthetic seismic record and the actual seismic record is calculated. The model is iteratively modified according to certain rules so that the residual between the model synthetic record and the actual seismic data reaches the allowable range. The modified wave impedance model is the inversion result. The second category is pre-stack elastic impedance reservoir inversion technology, which uses partially stacked data with different incident angle ranges to invert elastic impedance volumes at different angles. Then, the Lamé coefficient, shear modulus and density are estimated based on the elastic impedance volume. Furthermore, elastic parameters such as P-wave velocity and S-wave velocity can be calculated. First, the sparse pulse method is used to invert the elastic impedance volume corresponding to each incident angle by using partially superimposed data for different incident angle ranges. Then, based on the relationship between the well's density, shear modulus, Lamé coefficient curves and the inverted elastic impedance well bypass, the attribute volumes such as shear modulus, density, Lamé coefficient, P-wave velocity, S-wave velocity, λρ attribute, μρ attribute, and P-wave / S-wave velocity ratio are extracted.

[0049] The third method is geostatistical inversion.

[0050] In the petroleum industry, the application of geostatistical methods began with the use of Kriging techniques to create geological maps and distribution maps of rock physical parameters. The Kriging method is a deterministic geostatistical method that uses well point information to obtain a variation field for well interpolation.

[0051] The fourth type is neural network inversion.

[0052] This method establishes a nonlinear relationship between reservoir seismic response and well logging electrical parameters, calculates corresponding electrical parameter data volumes from seismic data, and characterizes the spatial distribution of the reservoir. It is an artificial intelligence algorithm that learns from large amounts of data to summarize the relationship between reservoir physical parameters and data characteristics; this is a data-driven prediction method.

[0053] The fifth method is to use forward modeling and speculative inversion of the actual seismic response of the reservoir.

[0054] Multiple well locations within the work area were selected to obtain well-side seismic traces as sample traces. The original well data of the sample traces were used to build a model, and the model parameters were continuously perturbed to obtain a sufficient number of synthetic seismic records. Correlation analysis and matching were performed with the seismic traces to be inverted. The inversion parameters of the corresponding model traces were assigned proportional weighting coefficients according to the similarity from large to small, and the weighting process was performed to obtain the parameter inversion results of the seismic traces to be inverted.

[0055] Currently, conventional methods mostly conduct reservoir prediction work in the time domain, and rarely in the depth domain.

[0056] This invention provides a method for predicting fractured-vuggy carbonate reservoirs based on fracture-vuggy strength factors. Figure 1 This is a schematic diagram of a method for predicting fractured-vuggy reservoirs in carbonate rocks based on fracture-vuggy strength factors, as shown below. Figure 1 As shown, it includes:

[0057] Step 101: Determine the scope of the work area to be measured, determine the planar scope of the post-stack seismic data volume based on the scope of the work area, and determine the target layer depth range of the post-stack seismic data volume based on the planar scope of the post-stack seismic data volume.

[0058] Step 102: Based on the planar range and target layer depth range of the post-stack seismic data volume, determine the original seismic data volume within the range of the post-stack seismic data volume, and perform multi-window dip scanning on the original seismic data volume to obtain the dip volume and azimuth volume;

[0059] Step 103: Filter the original seismic data volume based on the dip volume and azimuth volume, and perform a second multi-window dip scan on the filtered original seismic data volume;

[0060] Step 104: Based on the results of the second-stage multi-window dip scanning of the original seismic data volume, determine the first scanning window and the corresponding seismic data volume. Based on the first scanning window and the corresponding seismic data volume, perform intrinsic coherence calculation to obtain the coherence calculation result. The first scanning window is the scanning window used for image filtering in the second-stage multi-window dip scanning.

[0061] Step 105: Calculate the fracture-vuggy strength factor based on the coherence calculation results to obtain the fracture-vuggy strength factor data volume. Determine the carbonate fracture-vuggy reservoir based on the fracture-vuggy strength factor data volume. The fracture-vuggy strength factor is used to describe the degree of fracture-vuggy development in the fracture-vuggy reservoir and the influence of the degree of fracture-vuggy development on the reservoir performance.

[0062] The fracture-vuggy reservoir prediction method for carbonate rocks based on fracture-vuggy strength factor proposed in this invention addresses the limitations of conventional seismic reservoir prediction technology. It studies a new method and technology for predicting fracture-vuggy reservoirs in carbonate rocks in the depth domain based on fracture-vuggy strength factor, and predicts the development and distribution of strongly heterogeneous carbonate rock reservoirs that are more consistent with reality, thus providing support for the exploration and development of strongly heterogeneous carbonate rock oil and gas reservoirs.

[0063] The technical problem this invention aims to solve is to provide a method for predicting fractured-vuggy carbonate reservoirs based on fracture-vuggy strength factors. This method enables three-dimensional prediction of the spatial distribution of carbonate reservoirs, improving the accuracy of carbonate reservoir prediction and increasing the success rate of exploration and development of fractured carbonate oil and gas reservoirs.

[0064] Carbonate rocks are brittle strata. Under the influence of tectonic forces such as compression, tension, shear, and thrust, they are prone to slip displacement, deformation, faulting, and fracturing, forming deformation variation zones. These zones include fracture zones with certain angles, low-angle faults, and bedding-parallel slip-torsional swelling deformation zones. When the strata are exposed, atmospheric precipitation most easily erodes and dissolves along these deformation variation zones, altering the reservoir permeability of carbonate rocks and promoting the further formation of high-quality carbonate reservoirs. Simultaneously, the dissolution process increases the pore space of the reservoir and facilitates the filling of gas, water, and oil, further exacerbating the strong heterogeneity of carbonate reservoirs. This change will inevitably be reflected in the waveform response of seismic signals. By employing appropriate correlation analysis seismic methods and techniques, these changes in seismic signals can be detected. Analyzing these changes allows us to characterize the development and distribution trends and patterns of strongly heterogeneous carbonate reservoirs.

[0065] A 360-degree all-round scan of the underground strata is performed, and a three-dimensional window is taken along the longitudinal axis of the scan azimuth to capture seismic data to analyze the dip angle of the strata. Finally, the azimuth of the true dip angle, the dip angle value of the strata, and the corresponding optimal scan window are obtained. Correlation analysis is performed on the seismic reflection information in the optimal longitudinal scan window corresponding to the true dip angle to detect the small lateral changes in post-stack seismic data caused by carbonate fracture-vuggy reservoirs. The fracture-vuggy strength factor is calculated to predict carbonate fracture-vuggy reservoirs.

[0066] Generally, correlation analysis is based on waveform similarity calculations. Multichannel similarity is used to transform 3D seismic data volumes into correlation coefficient data volumes, emphasizing uncorrelated anomalies and highlighting discontinuities in the display. Its premise is that the strata are continuous, and any changes in seismic waves are gradual; therefore, adjacent traces and lines are similar. When the continuity of the strata is disrupted, such as through fracture-cavity dissolution or lithological changes, the waveform characteristics between seismic traces change abnormally, leading to abnormal changes in the similarity between local traces. These abnormal changes can be used to characterize the development and distribution of fracture-cavity reservoirs in carbonate rocks.

[0067] In one embodiment, determining the survey area of ​​the target seismic zone, and determining the planar extent of the post-stack seismic data volume based on the survey area, includes:

[0068] Determine the work area, data lines, and track range of the work area to be tested;

[0069] Based on the work area, data lines, and trace range of the work area to be measured, the planar range of the post-stack seismic data volume is determined.

[0070] In practice, firstly, the size of the target area for multi-scale fault prediction work is clearly defined on the plane, and the plane range of the post-stack seismic data volume is defined accordingly.

[0071] In one embodiment, determining the target layer depth range of the post-stack seismic data volume based on its planar extent includes:

[0072] Based on the planar extent of the post-stack seismic data volume, edit and extract the seismic interpretation horizons within the planar extent;

[0073] Based on the seismic interpretation horizon, determine the target layer depth range of the post-stack seismic data volume.

[0074] In practice, based on the plane range of the seismic data volume determined in the previous step, the seismic interpretation horizons within that range are manually edited and extracted. This then determines the time range of the seismic data volume for the target work area. This significantly reduces the amount of data, thereby reducing the amount of computer processing, saving machine time and costs, and improving the efficiency of scientific research and production. This is well-suited to the fast pace of oilfield production and meets the schedule requirements of oilfield production.

[0075] In one embodiment, a multi-window dip scan is performed on the original seismic data volume to obtain a dip volume and an azimuth volume, including:

[0076] Using the original seismic data volume as input, a multi-window dip scan is performed using a vertical window to obtain the dip volume and azimuth volume.

[0077] In practice, using a vertical window to estimate dip and azimuth angles typically provides more stable results than estimating at the picked layer. Dip and azimuth angles are fundamental to constructing steering filters, volume curvature, and coherent amplitude energy gradients.

[0078] The multi-window tilt scanning mechanism is as follows: Figure 2 This is a schematic diagram of discrete scanning of the reflector dip angle, where the instantaneous dip angle with the maximum coherence value is taken as the formation dip angle; Figure 3 This is a schematic diagram of the search process for the maximum coherence window. The optimal coherence window is obtained by comparing the coherence values ​​of each window. Here, the inline is the main survey line, and the crossline is the connecting line. It can be seen that it performs a 360-degree all-round scan of the underground strata and obtains the optimal coherence window, which is the scan window along the true dip angle of the strata.

[0079] In one embodiment, filtering the raw seismic data volume based on dip and azimuth volumes includes:

[0080] A guide body is constructed based on the tilt body and the azimuth body, and the guide body is used for guiding filtering;

[0081] The original seismic data volume is filtered based on the guide body.

[0082] In practice, the raw seismic data is filtered to remove noise and increase the signal-to-noise ratio, providing high-quality seismic data for subsequent calculations.

[0083] Using the result of the previous step (the result after constructing a guided filter on the original seismic data volume) as input, a multi-window dip scan is performed, which serves as the input data volume for the next step.

[0084] In one embodiment, the first scanning window is the kuwahara scanning window in the secondary multi-window dip scanning result, and the related seismic data volume corresponding to the first scanning window is the related seismic data volume corresponding to the kuwahara scanning window in the secondary multi-window dip scanning result.

[0085] Based on the first scanning window and the corresponding seismic data volume, intrinsic coherence calculation is performed to obtain the coherence calculation results, including:

[0086] Using the Kuwahara scan window and the corresponding seismic data volume as input data, intrinsic coherence calculation is performed to obtain the coherence calculation results.

[0087] In practice, the kuwahara scan window (one of the multi-window dip angle scan results calculated in the previous step) and the corresponding related seismic data (the result after constructing a directional filter from the original seismic data) are used as inputs to perform intrinsic coherence calculations and obtain the coherence calculation results.

[0088] In one embodiment, based on the coherent calculation results, the cavity strength factor is calculated to obtain a cavity strength factor data volume, including:

[0089] Based on the coherent calculation results, the third-generation coherent algorithm is used to calculate the fracture strength factor, and the fracture strength factor data volume is obtained.

[0090] Many application software programs, such as Landmark and GeoFrame, use algorithms based on classic normalization-based cross-correlation calculations (Bahorich and Farmer, 1995, 1996), which are referred to as the first-generation algorithm C1.

[0091] The C2 algorithm introduces a covariance matrix, enabling it to perform similarity analysis on any number of traces and estimate their coherence. In addition to measuring coherence more robustly in noisy environments, the C2 coherence algorithm can limit the vertical analysis window to only a few time samples, allowing for the accurate creation of thin and small stratigraphic feature maps.

[0092] The C3 coherent algorithm utilizes the covariance matrix introduced in the C2 coherent algorithm, and its resolution is higher than that of the C1 and C2 algorithms.

[0093] (1) First generation coherent algorithm

[0094] The first-generation coherence algorithm was proposed in 1995 by MS Bahorich and SLF Farmer. It is a coherence volume algorithm based on cross-correlation. Its basic principle is to perform a simple cross-correlation between the data of each trace of the transverse and longitudinal survey lines and the central trace to obtain the correlation coefficient ρ.

[0095] First, define the position of the track at time t on the longitudinal survey line as (x... i ,y j ) and (x i+1 ,y j The cross-correlation function ρ between point () and seismic trace (u) with a delay of l. x

[0096]

[0097] Here, 2ω is the time length of the relevant time window.

[0098] Next, define the position of the track at time t on the transverse survey line (x... i ,y j ) and (x i ,y j+1 The cross-correlation function ρ between the seismic trace u and the seismic trace u with a delay of m. y

[0099]

[0100] Combining the correlation coefficients of the horizontal and vertical survey lines above yields the correlation coefficient ρ. xy Three-dimensional estimation:

[0101]

[0102] In the formula and Let ρ represent the values ​​corresponding to time shifts of l and m, respectively. x and ρ y The maximum value.

[0103] For high-quality data, l and m are approximately equal to the apparent time tilt angles in the x and y directions. For data with coherent noise, determining the apparent tilt angle using only two data points will result in a large error, which is a drawback of the mutual coherence algorithm.

[0104] (2) Second-generation coherent algorithm

[0105] The second-generation coherent algorithm was proposed in 1998 by KJ Marfurt, RL Kirlin, and SL Farmer. It mainly utilizes the similarity of seismic traces to obtain coherence properties. The second-generation coherent algorithm significantly improves the signal-to-noise ratio and stability.

[0106] First, define an elliptical or rectangular time window centered at the analysis point and containing channel J. Then, the similarity coefficient σ(τ,p,q) is the ratio of the energy of the average channel to the energy of all channels within the analysis time window, i.e.

[0107]

[0108] In the formula, the subscript j indicates that it falls within the j-th channel of the analysis window; x j and y j The distances in the x and y directions between trace j and the center point τ within the analysis window are represented by , and p and q represent the apparent tilt angles in the x and y directions of the local reflection interface where the center point τ is located within the analysis window, respectively. The superscript H indicates the orthogonal component of the Hilbert transform or the actual seismic trace u. By calculating the similarity of the seismic traces, we obtain robust coherence calculations for seismic reflection phase axes, even those crossing zero points.

[0109] However, when calculating the similarity of strongly coherent seismic phase axes past zero, the given similarity estimate is unstable for some seismic phase axes with small coherence values. To avoid this problem, the same technique as in similarity velocity analysis is used to calculate the average of 2K+1 samples in the vertical direction, i.e.

[0110]

[0111] In the formula, Δt is the sampling rate. Since the analysis window is always the middle window, i.e., x = 0, y = 0, the intercept time τ can be replaced with t.

[0112] The second-generation coherence algorithm can calculate coherence for any seismic data, based on horizontal slices or within a specific time window at each layer. Its advantages include strict quality control over the seismic data and strong noise resistance. Utilizing a variable time window, i.e., an analysis window of appropriate size, it effectively resolves the trade-off between improving resolution and signal-to-noise ratio. Therefore, this algorithm has good applicability and resolution, and a relatively fast computation speed. However, it cannot accurately reflect changes in stratigraphic dip.

[0113] (3) Third-generation coherent algorithm

[0114] The third-generation coherence algorithm was proposed in 1999 by A. Gersztenkorn and KJ Marfurt. It is a method to obtain coherence properties by calculating the eigenvalues ​​of the covariance matrix.

[0115] Define the following formula as the coherence value of the third-generation coherent algorithm:

[0116]

[0117] Where, the denominator T r (C) is the trace of the matrix, representing the energy of the covariance matrix. The numerator λ1 is the largest eigenvalue, representing the dominant energy. The trace of the matrix can be calculated using... C is the covariance matrix of the given J-channel seismic data:

[0118]

[0119] In the formula, u jm =u j (mΔt-px j -qy j ), p, q are the apparent tilt angles.

[0120] Let C0, C1, C2, C3, and C4 be constants, then

[0121] y = C4-{C3-[C2 -(x-C0)*C1]}, (8)

[0122] In the formula: y is the crack strength factor, and x is the coherent data of the micro-signal based on the optimal orientation.

[0123] Equation (8) is the formula for calculating the strength factor of the crack.

[0124] In practice, the fracture-cavity strength factor data volume is calculated according to formula (8), which can comprehensively depict the development and distribution of underground carbonate reservoirs and promote the exploration and development of fractured carbonate oil and gas reservoirs.

[0125] Here is an example of the application of this invention in a certain well area:

[0126] 1. Define the work area, data line (inline), and crossline boundaries:

[0127] Define the size of the target area for multi-scale fault prediction work on a plane, such as... Figure 4 This is a planar map showing the size of the target area. Based on this, the planar range of the post-stack seismic data volume can be defined as Inline3058~Inline6870 and Crossline4734~Crossline9079.

[0128] 2. Seismic interpretation layer processing to determine the depth range of the target layer in the seismic data volume:

[0129] Based on the seismic data volume plane range determined in step 1, manually edit and extract the seismic interpretation horizons within this range. Figure 5a Construction diagram of the two lamp tops for the target work area. Figure 5b This is a diagram showing the construction of the light fixture in the target work area. Based on... Figure 5a , Figure 5b The depth range of the seismic data volume can be determined to be 5000 meters to 9500 meters (D5000 to D9500). Thus, based on steps 1 and 2, the seismic data volume range of the target area can be determined to be Inline3058 to Inline6870, Crossline4734 to Crossline9079, and D5000 to D9500.

[0130] 3. Multi-window dip scanning of raw seismic data:

[0131] Using the original seismic data volume within the target seismic data volume area determined in Step 1 and Step 2 as input data, multi-window dip angle scanning calculation is performed to obtain the dip volume and azimuth volume, which are then used as the input data volume for the next calculation.

[0132] 4. Constructing guided filtering from raw seismic data:

[0133] Input the raw seismic data and the calculation results from step 3. Filter the raw seismic data to remove noise and increase the signal-to-noise ratio, providing high-quality seismic data for subsequent calculations. For example... Figure 6a The original seismic profile before noise reduction. Figure 6b The denoised seismic profile shows that the energy is enhanced and the noodle-like noise is suppressed, providing better quality seismic data for the next step.

[0134] 5. Constructing multi-window dip scans based on the results of steering filter analysis using raw seismic data:

[0135] Using the results calculated in step 4 (the results after constructing a directional filter from the original seismic data) as input, a multi-window dip scan is performed as the input data for the next step.

[0136] 6. Eigenvalue coherence calculation based on Kuwahara scanning window:

[0137] Using the Kuwahara scan window (one of the multi-window dip scan results calculated in step 5) and its corresponding related seismic data (the result after constructing a directional filter from the original seismic data) as input, intrinsic coherence calculation is performed to obtain the coherence calculation results. For example... Figure 7 The diagram shows the coherence profile of the target area along the main survey line 5802. Red and yellow indicate well-developed carbonate reservoirs, green indicates well-developed carbonate reservoirs, dark blue to light blue indicates moderately developed carbonate reservoirs, and blue-white indicates poorly developed carbonate reservoirs. The carbonate reservoirs are characterized by undulating terrain and rapid vertical and horizontal variations. The calculation results generally conform to geological laws and roughly reflect the morphological distribution of carbonate reservoirs. However, the dynamic range of the calculated values ​​is relatively small (105–127), limiting the information representation and failing to fully represent the complete development of fractures and cavities. Furthermore, the results do not conform to conventional thinking: generally, larger values ​​indicate stronger fracture-cavity reservoir development, and smaller values ​​indicate weaker development.

[0138] 7. Calculation of fracture-vuggy strength factor data and prediction of fracture-vuggy reservoirs in carbonate rocks:

[0139] Using the coherence data calculated in step 6 as input data, the fracture strength factor is calculated according to formula (8), resulting in the fracture strength factor data volume. For example... Figure 8a This is a profile of the carbonate fracture-vuggy reservoir (fracture-vuggy strength factor) along the main survey line 5802 in the target area. Figure 8b-1 , Figure 8b-2 , Figure 8b-3 This is an interpretation map of the 5802PSDM seismic profile and reservoir calibration along the main survey line in the target area. Red and yellow values ​​represent large values ​​indicating well-developed carbonate reservoirs; green represents medium to small values ​​indicating well-developed carbonate reservoirs; dark blue to light blue values ​​represent relatively small values ​​indicating moderately developed carbonate reservoirs; and blue-white values, the smallest and almost zero, indicate poorly developed carbonate reservoirs. It can be seen that the result has a wide value range (0-200), making the prediction results more intuitive and providing stronger information. Furthermore, from... Figure 8b-1 , Figure 8b-2 , Figure 8b-3 It can be seen that the fractured reservoir prediction describes the spatial distribution of the main gas layers in the first and second sub-layers of the upper segment of Deng 2 quite well, indicating that the method is accurate and reliable.

[0140] Figure 9This is a planar distribution map of the fractured-vuggy reservoir in the target area, Deng 2. Red and yellow values ​​represent large values, indicating well-developed carbonate reservoirs; green values ​​represent medium to small values, indicating well-developed carbonate reservoirs; dark blue to light blue values ​​represent relatively small values, indicating moderately developed carbonate reservoirs; and blue-white values, the smallest and almost zero, indicate poorly developed carbonate reservoirs. It can be seen that the prediction results effectively describe the planar distribution trend and pattern of fractured-vuggy reservoir development.

[0141] This invention also provides a device for predicting carbonate fractured-vuggy reservoirs based on fracture-vuggy strength factors, as described in the following embodiments. Since the principle behind this device is similar to the method for predicting carbonate fractured-vuggy reservoirs based on fracture-vuggy strength factors, the implementation of this device can refer to the implementation of the method for predicting carbonate fractured-vuggy reservoirs based on fracture-vuggy strength factors; repeated details will not be elaborated further.

[0142] Figure 10 This is a schematic diagram of a carbonate rock fractured-vuggy reservoir prediction device based on fracture-vuggy strength factor provided in an embodiment of the present invention, as shown below. Figure 10 As shown, the device includes:

[0143] The data volume determination module 1001 is used to determine the work area range of the work area to be measured, determine the planar range of the post-stack seismic data volume based on the work area range, and determine the target layer depth range of the post-stack seismic data volume based on the planar range of the post-stack seismic data volume.

[0144] The first dip angle scanning module 1002 is used to determine the original seismic data volume within the scope of the post-stack seismic data volume based on the planar range and the depth range of the target layer, and to perform multi-window dip angle scanning on the original seismic data volume to obtain the dip angle volume and azimuth angle volume.

[0145] The second dip angle scanning module 1003 is used to filter the original seismic data volume according to the dip angle volume and azimuth angle volume, and to perform a second multi-window dip angle scan on the filtered original seismic data volume.

[0146] The coherence calculation module 1004 is used to determine the first scanning window and the relevant seismic data volume corresponding to the first scanning window based on the secondary multi-window dip scan results of the original seismic data volume, and to perform intrinsic coherence calculation based on the first scanning window and the relevant seismic data volume corresponding to the first scanning window to obtain the coherence calculation result. The first scanning window is the scanning window used for image filtering in the secondary multi-window dip scan.

[0147] The prediction module 1005 is used to calculate the fracture-vuggy strength factor based on the coherence calculation results, obtain the fracture-vuggy strength factor data volume, and determine the carbonate fracture-vuggy reservoir based on the fracture-vuggy strength factor data volume. The fracture-vuggy strength factor is used to describe the degree of fracture-vuggy development in the fracture-vuggy reservoir and the influence of the degree of fracture-vuggy development on the reservoir performance.

[0148] In one embodiment, the data volume determination module 1001 is specifically used for:

[0149] Determine the work area, data lines, and track range of the work area to be tested;

[0150] Based on the work area, data lines, and trace range of the work area to be measured, the planar range of the post-stack seismic data volume is determined.

[0151] In one embodiment, the data volume determination module 1001 is specifically used for:

[0152] Based on the planar extent of the post-stack seismic data volume, edit and extract the seismic interpretation horizons within the planar extent;

[0153] Based on the seismic interpretation horizon, determine the target layer depth range of the post-stack seismic data volume.

[0154] In one embodiment, the first tilt angle scanning module 1002 is specifically used for:

[0155] Using the original seismic data volume as input, a multi-window dip scan is performed using a vertical window to obtain the dip volume and azimuth volume.

[0156] In one embodiment, the second tilt angle scanning module 1003 is specifically used for:

[0157] A guide body is constructed based on the tilt body and the azimuth body, and the guide body is used for guiding filtering;

[0158] The original seismic data volume is filtered based on the guide body.

[0159] In one embodiment, the first scanning window is the kuwahara scanning window in the secondary multi-window dip scanning result, and the related seismic data volume corresponding to the first scanning window is the related seismic data volume corresponding to the kuwahara scanning window in the secondary multi-window dip scanning result.

[0160] The coherent computing module 1004 is specifically used for:

[0161] Using the Kuwahara scan window and the corresponding seismic data volume as input data, intrinsic coherence calculation is performed to obtain the coherence calculation results.

[0162] In one embodiment, the prediction module 1005 is specifically used for:

[0163] Based on the coherent calculation results, the third-generation coherent algorithm is used to calculate the fracture strength factor, and the fracture strength factor data volume is obtained.

[0164] Based on the aforementioned inventive concept, such as Figure 11As shown, the present invention also proposes a computer device 1100, including a memory 1110, a processor 1120, and a computer program 1130 stored in the memory 1110 and executable on the processor 1120. When the processor 1120 executes the computer program 1130, it implements the aforementioned method for predicting carbonate fractured-vuggy reservoirs based on fracture-vuggy strength factors.

[0165] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting carbonate fractured-vuggy reservoirs based on fracture-vuggy strength factors.

[0166] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for predicting carbonate fractured-vuggy reservoirs based on fracture-vuggy strength factors.

[0167] In summary, in this embodiment of the invention, the scope of the work area to be measured is determined; the planar scope of the post-stack seismic data volume is determined based on the scope of the work area; the target layer depth range of the post-stack seismic data volume is determined based on the planar scope of the post-stack seismic data volume; the original seismic data volume within the scope of the post-stack seismic data volume is determined based on the planar scope and the target layer depth range of the post-stack seismic data volume; a multi-window dip scan is performed on the original seismic data volume to obtain dip and azimuth volumes; the original seismic data volume is filtered based on the dip and azimuth volumes; a second multi-window dip scan is performed on the filtered original seismic data volume; and the original seismic data volume is then analyzed based on the dip and azimuth volumes. Based on the results of the secondary multi-window dip scanning, the first scanning window and the corresponding seismic data volume are determined. Intrinsic coherence calculation is performed based on the first scanning window and the corresponding seismic data volume to obtain the coherence calculation results. The first scanning window is the scanning window used for image filtering in the secondary multi-window dip scanning. Based on the coherence calculation results, the fracture-vuggy strength factor is calculated to obtain the fracture-vuggy strength factor data volume. The fracture-vuggy strength factor data volume is used to determine carbonate fracture-vuggy reservoirs. The fracture-vuggy strength factor is used to describe the degree of fracture-vuggy development in fracture-vuggy reservoirs and the influence of fracture-vuggy development on reservoir performance. In this way, by determining the planar range and target layer depth range of the post-stack seismic data volume, the original seismic data volume is determined. The original seismic data volume is then filtered based on the multi-window dip scanning results. A second multi-window dip scanning is performed on the filtered original seismic data volume, and coherence calculations are performed based on the scanning results to determine the fracture-vuggy strength factor, thereby identifying carbonate fracture-vuggy reservoirs. This enables three-dimensional prediction of the spatial distribution of carbonate reservoirs, effectively improving the prediction accuracy of carbonate reservoirs and increasing the success rate of exploration and development of fractured oil and gas reservoirs in carbonate reservoirs.

[0168] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0169] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0170] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0171] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0172] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting fractured-vuggy reservoirs in carbonate rocks based on fracture-vuggy strength factor, characterized in that, include: Determine the scope of the work area to be measured, determine the planar scope of the post-stack seismic data volume based on the scope of the work area, and determine the target layer depth range of the post-stack seismic data volume based on the planar scope of the post-stack seismic data volume. Based on the planar extent and target layer depth range of the post-stack seismic data volume, the original seismic data volume within the post-stack seismic data volume range is determined, and a multi-window dip scan is performed on the original seismic data volume to obtain the dip volume and azimuth volume. The original seismic data volume is filtered based on the dip volume and azimuth volume, and a second multi-window dip scan is performed on the filtered original seismic data volume. Based on the results of the second-stage multi-window dip scanning of the original seismic data volume, the first scanning window and the corresponding seismic data volume are determined. Based on the first scanning window and the corresponding seismic data volume, intrinsic coherence calculation is performed to obtain the coherence calculation results. The first scanning window is the scanning window used for image filtering in the second-stage multi-window dip scanning. Based on the coherent calculation results, the fracture-vuggy strength factor is calculated to obtain the fracture-vuggy strength factor data volume. Based on the fracture-vuggy strength factor data volume, carbonate fracture-vuggy reservoirs are identified. The fracture-vuggy strength factor is used to describe the degree of fracture-vuggy development in fracture-vuggy reservoirs and the influence of the degree of fracture-vuggy development on reservoir performance.

2. The method as described in claim 1, characterized in that, Determine the scope of the seismic zone to be measured, and based on the scope of the seismic zone, determine the planar extent of the post-stack seismic data volume, including: Determine the work area, data lines, and track range of the work area to be tested; Based on the work area, data lines, and trace range of the work area to be measured, the planar range of the post-stack seismic data volume is determined.

3. The method as described in claim 1, characterized in that, Based on the planar extent of the post-stack seismic data volume, determine the target layer depth range of the post-stack seismic data volume, including: Based on the planar extent of the post-stack seismic data volume, edit and extract the seismic interpretation horizons within the planar extent; Based on the seismic interpretation horizon, determine the target layer depth range of the post-stack seismic data volume.

4. The method as described in claim 1, characterized in that, Multi-window dip scanning is performed on the original seismic data volume to obtain dip and azimuth volumes, including: Using the original seismic data volume as input, a multi-window dip scan is performed using a vertical window to obtain the dip volume and azimuth volume.

5. The method as described in claim 1, characterized in that, The raw seismic data volume is filtered based on dip and azimuth volumes, including: A guide body is constructed based on the tilt body and the azimuth body, and the guide body is used for guiding filtering; The original seismic data volume is filtered based on the guide body.

6. The method as described in claim 1, characterized in that, The first scanning window is the kuwahara scanning window in the secondary multi-window dip scanning result, and the related seismic data volume corresponding to the first scanning window is the related seismic data volume corresponding to the kuwahara scanning window in the secondary multi-window dip scanning result; Based on the first scanning window and the corresponding seismic data volume, intrinsic coherence calculation is performed to obtain the coherence calculation results, including: Using the Kuwahara scan window and the corresponding seismic data volume as input data, intrinsic coherence calculation is performed to obtain the coherence calculation results.

7. The method as described in claim 1, characterized in that, Based on the coherent calculation results, the fracture strength factor is calculated, resulting in a fracture strength factor data volume, including: Based on the coherent calculation results, the third-generation coherent algorithm is used to calculate the fracture strength factor, and the fracture strength factor data volume is obtained.

8. A device for predicting fractured-vuggy carbonate reservoirs based on fracture-vuggy strength factor, characterized in that, include: The data volume determination module is used to determine the work area range of the work area to be measured, determine the planar range of the post-stack seismic data volume based on the work area range, and determine the target layer depth range of the post-stack seismic data volume based on the planar range of the post-stack seismic data volume. The first dip scanning module is used to determine the original seismic data volume within the scope of the post-stack seismic data volume based on the planar range and the depth range of the target layer, and to perform multi-window dip scanning on the original seismic data volume to obtain the dip volume and azimuth volume. The second dip angle scanning module is used to filter the original seismic data volume based on the dip angle volume and azimuth angle volume, and to perform a second multi-window dip angle scan on the filtered original seismic data volume. The coherence calculation module is used to determine the first scanning window and the corresponding seismic data volume based on the secondary multi-window dip scan results of the original seismic data volume, and to perform intrinsic coherence calculation based on the first scanning window and the corresponding seismic data volume to obtain the coherence calculation result. The first scanning window is the scanning window used for image filtering in the secondary multi-window dip scan. The prediction module is used to calculate the fracture-vuggy strength factor based on the coherence calculation results, obtain the fracture-vuggy strength factor data volume, and determine the carbonate fracture-vuggy reservoir based on the fracture-vuggy strength factor data volume. The fracture-vuggy strength factor is used to describe the degree of fracture-vuggy development in the fracture-vuggy reservoir and the influence of the degree of fracture-vuggy development on the reservoir performance.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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