Methods, systems, equipment, and media for effective earthquake identification of small-scale underground fractures and caverns

Abstract

The present invention belongs to the technical field of oil and gas seismic exploration and development, and relates to a method for effectively identifying small-scale underground fractures and caves. The method primarily employs a method based on tectonic interpretation of horizon results to construct formation reflection energy background data and remove the background from the original seismic data, thereby enhancing seismic signals associated with small-scale fractures and caves in the formation. The method also tracks the commonality of effective spatial eigenvalues ​​by combining geological laws and drilling data to achieve feature spatial carving of geological bodies. This method achieves carving of the spatial features of geological bodies hidden beneath the formation background energy. By combining understanding of geological laws and drilling data, the accuracy of descriptions of underground geological bodies and their understanding of their distribution patterns are improved, providing a powerful basis for well site optimization and deployment for oil reservoir development, as well as for the management of old wells.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas seismic exploration and development, and specifically relates to a method, system, equipment and medium for effectively identifying seismic events of small-scale underground fractures and caves. Background Art

[0002] Exploration and development practices have demonstrated that fracture networks and high-quality reservoirs associated with strike-slip fault zones are key factors in oil and gas migration and accumulation. With the advancement of oil and gas exploration, smaller-scale fracture-pore systems have become the primary focus of exploration and development for deep fracture-vuggy reservoirs, representing a key area for concentrated and efficient oil and gas exploration. However, previous research has shown that the geophysical characteristics of small-scale fracture-pore systems are extremely weak, making them virtually indistinguishable in the seismic response of formations with strong impedance differences. Sensitive attributes such as conventional coherence, structural curvature, uniform root amplitude, and instantaneous amplitude have very low prediction accuracy for micro-scale fracture-pore systems and cannot meet the current needs of oil and gas exploration and development.

[0003] Therefore, there is an urgent need to develop an effective method for extracting and enhancing seismic signals related to small-scale fracture networks and pore systems, so as to realize the identification and spatial carving of weak signals hidden under strong background energy, so as to meet the actual needs of further exploration and development. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for effectively identifying small-scale underground fractures and caves by seismic analysis, which solves the problem of effectively characterizing small-scale heterogeneous fractures and caves in deep to ultra-deep strata by seismic analysis.

[0005] The present invention is achieved through the following technical solutions:

[0006] An effective method for identifying small-scale underground fractures and caverns by earthquakes, comprising the following steps:

[0007] 1) Obtain seismic data and drilling data from a certain work area, perform fine calibration of the seismic data and drilling data, and obtain calibration results;

[0008] Based on the calibration results, continuous tracking and interpretation of the top and bottom events of the target layer are carried out to obtain the structural positions of the top and bottom surfaces of the target layer;

[0009] According to the structural layers of the top and bottom surfaces of the target layer, the original seismic data between the top and bottom surfaces of the target layer are obtained;

[0010] 2) Filter the raw seismic data between the top and bottom layers of the target layer, complete the vertical hour window and horizontal small-scale filtering processing, obtain the target data, and determine the seismic reflection characteristics of the target layer based on the target data;

[0011] 3) Based on the seismic reflection characteristics, the target data is considered to consist of two parts, including the formation background energy field data and the formation heterogeneity data;

[0012] 4) Remove the formation background energy field data from the target data to obtain formation heterogeneity data;

[0013] 5) Based on the calibration results and the formation heterogeneity data, obtain the energy anomaly phenomenon of the formation heterogeneity data; determine whether the energy anomaly phenomenon of the formation heterogeneity data matches one or more phenomena indicative of the reservoir;

[0014] If not, return to step 2) to adjust the vertical hour window and the horizontal small range, and repeat steps 2) to 5) until the energy anomaly phenomenon of the formation heterogeneity data matches at least one of the phenomena indicating the reservoir.

[0015] Furthermore, in step 2), if the vertical hour window is less than 5 sampling points and the sampling rate is greater than 4ms or 5ms, resampling is required, and the resampling rate is 1ms or 2ms;

[0016] The lateral small range is less than 9 adjacent seismic traces. If the lateral structural changes are drastic, the analysis should be conducted along the survey line with the smallest structural change.

[0017] Furthermore, in step 2), the seismic reflection characteristics include background reflection characteristics and heterogeneous reflection characteristics.

[0018] Furthermore, background reflection features include continuous peak reflections and continuous trough reflections;

[0019] The inhomogeneous reflection characteristics include strong energy reflection and chaotic reflection with multiple peaks and troughs interwoven.

[0020] Furthermore, in step 3), the target data is regarded as the sum of two matrices. The target data is called matrix D, and the two matrices are named A and E respectively. The A matrix corresponds to the formation background energy field data, and the E matrix corresponds to the formation heterogeneity data.

[0021] The A matrix is ​​reconstructed using the maximum cross-correlation analysis method, specifically:

[0022] First, the target data is flattened based on a certain structural layer to obtain the layer-flattened data;

[0023] When reconstructing each trace, the cross-correlation value between the trace and several adjacent traces in the layer-leveled data is calculated, and the trace with the maximum cross-correlation value and the minimum seismic trace energy is taken as the layer-leveled reconstructed data of the trace;

[0024] All seismic traces in the layer-flattened data are reconstructed in sequence to obtain the layer-flattened stratum background energy field data;

[0025] The reverse flattening of the original flattened structural layer is completed for the flattened stratum background energy field data, and the original structural form is restored to obtain the stratum background energy field data of the target data;

[0026] In order to deal with the smoothness between the reconstructed traces, the average of the adjacent traces is taken before deflating.

[0027] Furthermore, step 4) is specifically as follows: for the target data and the formation background energy field data, perform E=DA operation to remove the formation background energy field data from the target data, and obtain formation heterogeneity data.

[0028] Furthermore, in step 5), the phenomena indicating the reservoir include drilling blowdown, leakage, abnormal well logging gas values, or abnormal drilling time.

[0029] The present invention also discloses an effective earthquake identification system for underground small-scale fractures and caves, comprising:

[0030] Data acquisition module, used to obtain seismic data and drilling data of a certain work area;

[0031] The calibration module is used to carry out fine calibration of seismic data and drilling data and obtain calibration results;

[0032] The continuous tracking interpretation module is used to carry out continuous tracking interpretation of the phase axis of the top and bottom surfaces of the target layer based on the calibration results to obtain the structural layers of the top and bottom surfaces of the target layer; and obtain the original seismic data between the top and bottom surfaces of the target layer based on the structural layers of the top and bottom surfaces of the target layer;

[0033] The pre-processing module is used to filter the original seismic data between the top and bottom layers of the target layer, complete the vertical hour window and horizontal small range filtering processing, obtain the target data, and determine the seismic reflection characteristics of the target layer based on the target data;

[0034] The formation background energy volume building module is used to regard the target data as consisting of two parts of data based on the seismic reflection characteristics, including the formation background energy field data and the formation heterogeneity data;

[0035] The formation background energy field removal module is used to remove the formation background energy field data from the target data to obtain formation heterogeneity data;

[0036] An analysis module is used to obtain an energy anomaly phenomenon of the formation heterogeneity data based on the calibration results and the formation heterogeneity data; and to determine whether the energy anomaly phenomenon of the formation heterogeneity data matches one or more phenomena indicative of a reservoir;

[0037] If there is no match, the vertical small window and the horizontal small range in the pre-processing module are adjusted until at least one of the reservoir phenomena indicated by the energy anomaly of the formation heterogeneity data matches.

[0038] The present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for effectively identifying small-scale underground fractures and caves are implemented.

[0039] The present invention also discloses a computer-readable storage medium, which stores a computer program, and is characterized in that when the computer program is executed by a processor, the steps of the method for effectively identifying earthquakes of small-scale underground fractures and caves are implemented.

[0040] Compared with the prior art, the present invention has the following beneficial technical effects:

[0041] The present invention establishes an effective seismic identification method for small-scale underground fractures and caves. This method identifies formation heterogeneity by removing formation background energy. It primarily uses a method based on tectonic interpretation of horizon results to construct formation reflection energy background data and remove the background from the original seismic data, thereby enhancing seismic signals associated with small-scale fractures and caves in the formation. Finally, by combining geological laws and drilling data to track the commonality of effective spatial eigenvalues, the characteristic spatial carving of geological bodies is achieved. This method achieves the carving of spatial features of geological bodies hidden under the formation background energy. By combining understanding of geological laws and drilling data, the accuracy of descriptions of underground geological bodies and the understanding of their distribution patterns are improved, providing a strong basis for well site optimization and deployment in oil reservoir development, as well as treatment measures for old wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of each scanning method for reconstructing data A;

[0043] Figure 2 It is a superimposed display of the original seismic data of a certain area and the data obtained by different modeling parameters;

[0044] Figure a is the original seismic profile; Figure b is the profile of 49 modeled intensities; Figure c is the profile of 25 modeled intensities; Figure d is the profile of 9 modeled intensities;

[0045] Figure 3 It is a superposition display of the original seismic data of a certain area and the heterogeneous characteristic data results of different modeling parameters;

[0046] Figure a is the original seismic profile; Figure b is the profile of 49 modeled intensities; Figure c is the profile of 25 modeled intensities; Figure d is the profile of 9 modeled intensities;

[0047] Figure 4 The plane map of the RMS attributes of the target layer obtained from the original seismic data of a certain area;

[0048] Figure 5 It is a plane diagram of the root mean square properties of the target layer obtained based on the heterogeneous characteristic data of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following is a further detailed description with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. That is, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments.

[0050] The components described and illustrated in the drawings and embodiments of the present invention may be arranged and designed in a variety of different configurations. Therefore, the detailed description of the embodiments of the present invention provided in the following drawings is not intended to limit the scope of the claimed invention, but merely represents a selected embodiment of the present invention. All other embodiments derived by those skilled in the art based on the drawings and embodiments of the present invention without inventive effort shall fall within the scope of protection of the present invention.

[0051] It should be noted that the terms "comprises", "includes" or any other variations are intended to cover non-exclusive inclusion, so that a process, element, method, article or apparatus that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to the process, element, method, article or apparatus.

[0052] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0053] The specific contents are as follows:

[0054] 1) Conduct detailed interpretation of structural horizons based on seismic data

[0055] First, the seismic data and drilling data of a certain work area are obtained, and the seismic data and drilling data are finely calibrated to obtain the calibration results; based on the calibration results, continuous tracking and interpretation of the phase axes of the top and bottom surfaces of the target layer are carried out to obtain the structural horizons of the top and bottom surfaces of the target layer; based on the structural horizons of the top and bottom surfaces of the target layer, the original seismic data between the top and bottom surface layers of the target layer are obtained.

[0056] Based on the calibration results, continuous tracking and interpretation of seismic events are performed to achieve layer closure between the main and tie lines. The final interpreted structural horizons must achieve a 1x1 density. Once the structural horizon interpretation is complete, interpolation of the horizon samples is performed to ensure that no gaps are missed at any sample point within the analysis area. Furthermore, when stratum thickness varies significantly, the top and bottom interpretations of the target stratum must be completed.

[0057] 2) Target layer residual noise suppression preprocessing

[0058] The original seismic data between the top and bottom layers of the target layer are filtered, wherein the filter can be a median filter, a mean filter, etc., to complete the filtering processing of the vertical hour window and the horizontal small range to obtain the target data.

[0059] The seismic reflection characteristics of the target layer are determined based on the target data, such as continuous peak reflection, continuous trough reflection, chaotic reflection, and strong energy reflection interwoven with multiple peaks and troughs.

[0060] 3) Construction of the formation background energy body based on principal component analysis

[0061] The key to this invention is the construction of a background energy field. The target data is constructed as a data matrix D, which is considered the sum of two matrices (A + E). One matrix, A, is low-rank (due to internal background structural information, resulting in linear correlations between rows and columns), while the other, E, is sparse (containing information related to geological bodies such as heterogeneous fractured-vuggy reservoirs; individual geological bodies are uncorrelated and therefore considered sparse). The A matrix is ​​then considered the formation background energy field, while the E matrix is ​​considered the energy field related to geological bodies such as heterogeneous fractured-vuggy reservoirs, i.e., heterogeneous data.

[0062] The A matrix is ​​reconstructed using the maximum cross-correlation analysis method. Taking a 3D seismic data as an example (e.g. Figure 1 As shown in the figure, when reconstructing each data trace, the data is first flattened based on the interpreted structural horizon. The cross-correlation value between the trace and the eight adjacent traces is then calculated. The trace with the maximum cross-correlation value and the lowest seismic trace energy is selected as the reconstructed data for that trace. Finally, the data is reverse-flattened to restore the original structural configuration. All seismic traces in the data are reconstructed sequentially. To account for the smoothness between the reconstructed traces (normal strata with no structural changes or sudden structural changes), the adjacent traces are averaged before reverse-flattening to obtain the stratigraphic background energy field data.

[0063] 4) Remove the formation background energy field based on the target data

[0064] For the target data and formation background energy field data, the E=DA operation is implemented to remove the formation background energy field from the original seismic data and obtain the formation heterogeneity data.

[0065] 5) Reliability analysis of well-seismic data for heterogeneous fracture-cavity identification results

[0066] Based on the original calibration results, analyze reservoir-indicating phenomena such as drilling blowdown, lost circulation, abnormal mud logging gas readings, and drilling time anomalies with the heterogeneity data E. Determine whether the energy anomaly in the formation heterogeneity data matches one or more of the reservoir-indicating phenomena. If not, return to step 2) to adjust the vertical small window and horizontal small range and repeat steps 2) through 5) until at least one of the reservoir-indicating phenomena in the energy anomaly in the formation heterogeneity data matches.

[0067] The specific implementation steps are as follows:

[0068] 1) Conduct detailed interpretation of structural layers based on seismic data. First, conduct detailed calibration of seismic data and drilling data to determine the seismic reflection characteristics corresponding to the strata, and conduct continuous tracking interpretation of seismic events to achieve the corresponding interpretation density.

[0069] 2) Filter the raw seismic data between the top and bottom layers of the target layer. The filter can be a median filter, a mean filter, or other filter. This filter is used to filter the vertical hour window and a small horizontal range to obtain the target data. The vertical hour window is generally less than five sampling points. If the sampling rate is greater than 4ms or 5ms, consider resampling to 1ms or 2ms. The small horizontal range is generally less than nine adjacent seismic traces. If the horizontal structure varies significantly, analyze along the survey line with the smallest structural changes.

[0070] 3) Based on principal component analysis, the stratigraphic background energy volume is constructed. The target data matrix D is considered the sum of two matrices (A + E). Matrix A is considered the reconstructed data. To reconstruct each trace, the data is first flattened based on the interpreted structural horizon. The cross-correlation value between the trace and the N adjacent traces is calculated. The trace with the maximum cross-correlation value and the lowest energy is selected as the reconstructed data for that trace. Finally, the data is deflattened to restore the original structural form. Reconstruction is completed for all seismic traces in the data.

[0071] 4) Based on the original seismic data, the formation background energy field is removed. For the original seismic data D and the formation background energy field data A, the E=DA operation is performed to remove the formation background energy field from the original seismic data and obtain the formation heterogeneity data E.

[0072] 5) Based on the original calibration results, complete the matching analysis of reservoir phenomena such as drilling blowdown, leakage, abnormal mud logging gas values, and drilling time anomalies with the heterogeneity data E.

[0073] The application of this invention in a certain three-dimensional work area in the Tarim Basin has a significant effect on the characteristics of heterogeneous fractures and caves and reservoirs formed by strike-slip faulting. The fault-reservoir space matching characteristics match the drilling results, improving the seismic characterization accuracy of heterogeneous geological bodies. Figure 2 The background energy fields reconstructed for different modeling intensities show that the greater the modeling intensity, the more continuous the separated background energy field is, and the less heterogeneous fracture-cavity information it contains;

[0074] Figure 3 The heterogeneous energy fields corresponding to different modeling intensities can be seen. It can be seen that the greater the modeling intensity, the clearer the fracture and cave information in the heterogeneous data, which is more conducive to identification.

[0075] Figure 4 and Figure 5 are respectively the target stratum root mean square attribute plane maps obtained based on the original seismic data of a certain area and the heterogeneous characteristic data of the present invention, and Figure 5 After the target layer slices are processed by this system, it can be seen that the characteristics of heterogeneous fractures and caves and reservoirs formed by strike-slip faulting are significantly enhanced, which improves the recognition accuracy of small-scale fractures and caves.

[0076] The present invention also discloses an effective earthquake identification system for underground small-scale fractures and caves, comprising:

[0077] Data acquisition module, used to obtain seismic data and drilling data of a certain work area;

[0078] The calibration module is used to carry out fine calibration of seismic data and drilling data and obtain calibration results;

[0079] The continuous tracking interpretation module is used to carry out continuous tracking interpretation of the phase axis of the top and bottom surfaces of the target layer based on the calibration results to obtain the structural layers of the top and bottom surfaces of the target layer; and obtain the original seismic data between the top and bottom surfaces of the target layer based on the structural layers of the top and bottom surfaces of the target layer;

[0080] The pre-processing module is used to filter the original seismic data between the top and bottom layers of the target layer, complete the vertical hour window and horizontal small range filtering processing, obtain the target data, and determine the seismic reflection characteristics of the target layer based on the target data;

[0081] The formation background energy volume building module is used to regard the target data as consisting of two parts of data based on the seismic reflection characteristics, including the formation background energy field data and the formation heterogeneity data;

[0082] The formation background energy field removal module is used to remove the formation background energy field data from the target data to obtain formation heterogeneity data;

[0083] An analysis module is used to obtain an energy anomaly phenomenon of the formation heterogeneity data based on the calibration results and the formation heterogeneity data; and to determine whether the energy anomaly phenomenon of the formation heterogeneity data matches one or more phenomena indicative of a reservoir;

[0084] If there is no match, the vertical small window and the horizontal small range in the pre-processing module are adjusted until at least one of the reservoir phenomena indicated by the energy anomaly of the formation heterogeneity data matches.

[0085] The present invention also discloses a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for effectively identifying small-scale underground fractures and caverns are implemented. The memory may include internal memory, such as a high-speed random access memory, or may also include non-volatile memory, such as at least one disk drive. The processor, network interface, and memory are interconnected via an internal bus, which may be an industrial standard architecture bus, a peripheral component interconnect standard bus, an extended industrial standard architecture bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, or the like. The memory is used to store programs. Specifically, the programs may include program code, which includes computer operating instructions. The memory may include both internal memory and non-volatile memory, and provides instructions and data to the processor.

[0086] The present invention also discloses a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of the method for effectively identifying small-scale underground fractures and caves. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory. The non-volatile memory may include read-only memory (ROM), a hard disk, flash memory, an optical disk, a magnetic disk, etc.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. An effective method for identifying small-scale underground fractures and caverns, characterized by: The following steps are involved: 1) Obtain seismic data and drilling data from a certain work area, perform fine calibration of the seismic data and drilling data, and obtain calibration results; Based on the calibration results, continuous tracking and interpretation of the top and bottom events of the target layer are carried out to obtain the structural positions of the top and bottom surfaces of the target layer; According to the structural layers of the top and bottom surfaces of the target layer, the original seismic data between the top and bottom surfaces of the target layer are obtained; 2) Filter the raw seismic data between the top and bottom layers of the target layer, complete the vertical hour window and horizontal small-scale filtering processing, obtain the target data, and determine the seismic reflection characteristics of the target layer based on the target data; 3) Based on the seismic reflection characteristics, the target data is considered to consist of two parts, including the formation background energy field data and the formation heterogeneity data; 4) Remove the formation background energy field data from the target data to obtain formation heterogeneity data; 5) Based on the calibration results and formation heterogeneity data, the energy anomaly of the formation heterogeneity data is obtained; Determine whether the energy anomaly phenomenon of the formation heterogeneity data matches one or more phenomena indicative of the reservoir; If not, return to step 2) to adjust the vertical hour window and the horizontal small range, and repeat steps 2) to 5) until the energy anomaly phenomenon of the formation heterogeneity data matches at least one of the phenomena indicating the reservoir; In step 3), the target data is regarded as the sum of two matrices. The target data is called matrix D, and the two matrices are named A and E respectively. The A matrix corresponds to the formation background energy field data, and the E matrix corresponds to the formation heterogeneity data. The A matrix is ​​reconstructed using the maximum cross-correlation analysis method, specifically: First, the target data is flattened based on a certain structural layer to obtain the layer-flattened data; When reconstructing each trace, the cross-correlation value between the trace and several adjacent traces in the layer-leveled data is calculated, and the trace with the maximum cross-correlation value and the minimum seismic trace energy is taken as the layer-leveled reconstructed data of the trace; All seismic traces in the layer-flattened data are reconstructed in sequence to obtain the layer-flattened stratum background energy field data; The reverse flattening of the original flattened structural layer is completed for the flattened stratum background energy field data, and the original structural form is restored to obtain the stratum background energy field data of the target data; In order to deal with the smoothness between the reconstructed traces, the average of the adjacent traces is taken before deflating.

2. The method for effectively identifying small-scale underground fractures and caves according to claim 1, characterized in that: In step 2), if the vertical hour window is less than 5 sampling points and the sampling rate is greater than 4ms or 5ms, resampling is required with a resampling rate of 1ms or 2ms. The lateral small range is less than 9 adjacent seismic traces. If the lateral structural changes are drastic, the analysis should be conducted along the survey line with the smallest structural change.

3. The effective seismic identification method for small-scale underground fractures and caves according to claim 1 is characterized in that: In step 2), the seismic reflection characteristics include background reflection characteristics and heterogeneous reflection characteristics.

4. The effective seismic identification method for small-scale underground fractures and caves according to claim 3 is characterized in that: Background reflection features include continuous wave crest reflection and continuous wave trough reflection; The inhomogeneous reflection characteristics include strong energy reflection and chaotic reflection with multiple peaks and troughs interwoven.

5. The effective seismic identification method for small-scale underground fractures and caves according to claim 1 is characterized in that: Step 4) is specifically as follows: for the target data and the formation background energy field data, perform the E=DA operation to remove the formation background energy field data from the target data and obtain the formation heterogeneity data.

6. The effective seismic identification method for underground small-scale fractures and caves according to claim 1 is characterized in that: In step 5), phenomena indicating reservoir formation include drilling blowdown, leakage, abnormal well logging gas values, or abnormal drilling time.

7. A system for effectively identifying underground small-scale fractures and caves by seismic analysis, which implements the method for effectively identifying underground small-scale fractures and caves by seismic analysis according to any one of claims 1 to 6, characterized in that: include: Data acquisition module, used to obtain seismic data and drilling data of a certain work area; The calibration module is used to carry out fine calibration of seismic data and drilling data and obtain calibration results; The continuous tracking interpretation module is used to carry out continuous tracking interpretation of the phase axis of the top and bottom surfaces of the target layer based on the calibration results to obtain the structural horizon of the top and bottom surfaces of the target layer; According to the structural layers of the top and bottom surfaces of the target layer, the original seismic data between the top and bottom surfaces of the target layer are obtained; The pre-processing module is used to filter the original seismic data between the top and bottom layers of the target layer, complete the vertical hour window and horizontal small range filtering processing, obtain the target data, and determine the seismic reflection characteristics of the target layer based on the target data; The formation background energy volume building module is used to regard the target data as consisting of two parts of data based on the seismic reflection characteristics, including the formation background energy field data and the formation heterogeneity data; The formation background energy field removal module is used to remove the formation background energy field data from the target data to obtain formation heterogeneity data; An analysis module is used to obtain energy anomalies of formation heterogeneity data based on the calibration results and formation heterogeneity data; Determine whether the energy anomaly phenomenon of the formation heterogeneity data matches one or more phenomena indicative of the reservoir; If there is no match, the vertical small window and the horizontal small range in the pre-processing module are adjusted until at least one of the reservoir phenomena indicated by the energy anomaly of the formation heterogeneity data matches.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for effectively identifying underground small-scale fractures and caves according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for effectively identifying underground small-scale fractures and caves according to any one of claims 1 to 6 are implemented.

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