Quantitative characterization method for micro-crack spatial distribution of tight oil reservoir based on sound wave far detection

Abstract

The invention discloses a quantitative characterization method for micro-crack spatial distribution of a tight oil reservoir based on acoustic remote exploration, and relates to the technical field of tight oil reservoir exploration, and the quantitative characterization method comprises the following steps: acquiring inter-well and in-well original acoustic logging data and fracturing response data, and preprocessing; based on the preprocessed original acoustic logging data, detecting a harmonic component in a reflection signal through a multi-frequency superposition excitation signal, and calculating a harmonic energy ratio of a depth horizon by using a harmonic component result; based on the harmonic energy ratio of the depth horizon, a crack sensitivity index is extracted, and a micro-crack development area is identified; and based on the micro-fracture development area and the processed original acoustic logging data, constructing a fracture network model to obtain spatial distribution information of the micro-fractures. According to the method, on the basis of the preprocessed original acoustic logging data, the harmonic components in the reflected signals are detected through the multi-frequency superposition excitation signals, and high-precision detection of the formation micro-cracks is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of tight oil reservoir exploration, in particular to a method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic wave remote detection. Background Art

[0002] In the exploration and development of tight oil reservoirs, the spatial distribution characteristics of microfractures are of great significance for reservoir evaluation and fracturing design. Fracture identification technology based on acoustic logging data has been widely used. It mainly achieves a preliminary characterization of fracture distribution by analyzing the relationship between acoustic wave propagation characteristics and formation response. Conventional methods usually use linear acoustic models to process raw acoustic logging data, and combine them with geological statistics or seismic attribute analysis methods to construct a spatial distribution map of the fracture development area. This can reflect the macro trend of the fracture system to a certain extent and provide basic support for reservoir modeling.

[0003] However, existing technologies still have certain limitations in terms of micro-scale fracture identification accuracy and spatial consistency modeling. Traditional methods mostly rely on single-frequency band signals, which makes it difficult to effectively capture the nonlinear acoustic response caused by micro-cracks. When constructing fracture network models, there is often a lack of effective mechanisms for fusing inter-well and in-well data, resulting in insufficient spatial resolution and difficulty in meeting the needs of high-precision quantitative characterization. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection to solve the problems of poor spatial modeling consistency caused by low microscale fracture identification accuracy and insufficient inter-well and intra-well data fusion in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection, which includes collecting raw acoustic logging data and fracturing response data between wells and within wells, and performing preprocessing; Based on the pre-processed original acoustic logging data, the harmonic components in the reflection signal are detected by multi-frequency superposition excitation signals, and the harmonic energy ratio of the depth layer is calculated using the harmonic component results. Based on the harmonic energy ratio of the depth layer, the fracture sensitivity index is extracted and the micro-fracture development area is identified; Based on the micro-fracture development area and the processed original acoustic logging data, a fracture network model is constructed to obtain the spatial distribution information of micro-fractures. The image pyramid algorithm is used for weighted fusion to generate a fracture distribution image. The fracture distribution image is compared and verified with the fracturing response data to obtain a comprehensive result of the spatial distribution characteristics.

[0007] As a preferred solution of the method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection of the present invention, the original acoustic logging data and fracturing response data between wells and within wells are collected and pre-processed, including the following steps: Acoustic logging instruments and interwell acoustic detection equipment are used to collect raw acoustic logging data between wells and within the wells, and fracturing response data is collected during the operation; The collected original acoustic logging data and fracturing response data between wells and within wells are preprocessed.

[0008] As a preferred solution of the method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection according to the present invention, the method comprises the following steps: based on the pre-processed original acoustic logging data, detecting the harmonic components in the reflected signal by using a multi-frequency superposition excitation signal, Perform time-frequency analysis on the pre-processed raw acoustic logging data to identify the main frequency band information; According to the main frequency band information, the three-frequency excitation signal is superimposed and transmitted to the formation to obtain the formation reflection signal; Perform harmonic separation on the formation reflection signal to obtain the harmonic components.

[0009] As a preferred solution of the method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection according to the present invention, the harmonic energy ratio of the depth layer is calculated using the harmonic component results, including the following steps: Perform frequency domain transformation on the harmonic component results to extract the energy of the main frequency and its harmonics; Based on the energy of the main frequency and its harmonics, the harmonic energy ratio of the depth layer is calculated.

[0010] As a preferred solution of the method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection according to the present invention, the method comprises the following steps: extracting the fracture sensitivity index and identifying the microfracture development area based on the harmonic energy ratio of the depth layer, Based on the harmonic energy ratio of the depth layer, a depth-harmonic energy ratio profile is constructed; Based on the depth-harmonic energy ratio profile, a sliding window statistical method was used to extract the crack sensitivity index and identify the microcrack development area.

[0011] As a preferred solution of the method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection according to the present invention, a fracture network model is constructed based on the original acoustic logging data pre-processed in the microfracture development area to obtain the spatial distribution information of microfractures, including the following steps: The microfracture development area and the processed original acoustic logging data are spatially registered to obtain a comprehensive dataset after spatial registration. Based on the microcrack development area, a preliminary fracture network model was formed using graph theory methods; The spatially registered comprehensive dataset is input into the fracture network model to obtain the spatial distribution information of microcracks.

[0012] As a preferred solution of the method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection according to the present invention, the method comprises the following steps: using an image pyramid algorithm for weighted fusion to generate a fracture distribution image, Construct a multi-scale Gaussian pyramid based on microcrack distribution information to generate image sets at different resolution levels; Use the weighted fusion formula to fuse image sets at different resolution levels to generate a fused pyramid image set; The detail information is restored by the Laplacian pyramid method, and the fused image pyramid set is converted into a complete crack distribution image.

[0013] As a preferred solution of the method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection according to the present invention, the fracture distribution image is compared and verified with the fracturing response data to obtain a comprehensive result of the spatial distribution characteristics, including the following steps: The fracture distribution image and the fracturing response data were unified, and the consistency between the fracture distribution and the fracturing response was evaluated using the squared error. Compare the fracture distribution of each layer with the fracturing response data point by point and record the deviation map; Based on the deviation map, a comprehensive result of the spatial distribution characteristics is generated.

[0014] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for quantitative characterization of the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection as described in the first aspect of the present invention is implemented.

[0015] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: by detecting the harmonic components in the reflected signal through multi-frequency superposition of excitation signals based on the pre-processed original acoustic logging data, high-precision detection of formation microcracks is achieved. The function is to accurately capture the nonlinear response caused by microcracks by identifying different frequency components, thereby achieving the purpose of constructing a detailed fracture sensitivity index profile, which not only improves the accuracy of microcrack identification, but also provides a high-quality data foundation for subsequent model construction. Based on the microcrack development area and the processed original acoustic logging data, a fracture network model is constructed to obtain the spatial distribution information of microcracks, thereby realizing the fracture connectivity analysis from point to surface. The function is to optimize the fracture network structure by using graph theory methods, and finally achieve the effect of comprehensively characterizing the spatial distribution characteristics of microcracks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 Flowchart of the quantitative characterization method for spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection.

[0019] Figure 2 Schematic diagram of the harmonic energy ratio of depth layers.

[0020] Figure 3 Flowchart for identifying microfracture development areas and constructing fracture network models.

[0021] Figure 4 Flowchart of weighted fusion and comparative verification of image pyramid algorithm. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0025] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic wave remote detection, comprising the following steps: S1. Collect raw acoustic logging data and fracturing response data between wells and within wells, and perform preprocessing.

[0026] S1.1. Collect raw acoustic logging data between wells and within wells using acoustic logging instruments and interwell acoustic detection equipment, and collect fracturing response data during the operation.

[0027] Furthermore, by deploying downhole acoustic logging instruments and inter-well acoustic detection equipment, high-resolution raw acoustic logging data in the well and large-scale fracture distribution information between wells are collected in multiple frequency ranges (such as low-frequency band 5kHz and high-frequency band 20kHz) at different depth levels in the target tight oil reservoir area. At the same time, during the on-site hydraulic fracturing operation, the fracturing response data is monitored and recorded in real time.

[0028] S1.2. Preprocess the collected original acoustic logging data and fracturing response data between wells and within wells.

[0029] Furthermore, all the collected raw acoustic logging data and fracturing response data are pre-processed, including denoising, correction and filtering operations, to ensure the quality and consistency of the data, providing reliable basic data for subsequent multi-frequency superposition excitation signal detection and harmonic energy ratio calculation.

[0030] S2. Based on the pre-processed original acoustic logging data, the harmonic components in the reflection signal are detected by multi-frequency superposition excitation signals.

[0031] S2.1. Perform time-frequency analysis on the pre-processed raw acoustic logging data to identify the main frequency band information.

[0032] Furthermore, short-time Fourier transform (STFT) is used to perform time-frequency analysis on the original acoustic logging data that has been denoised and filtered to obtain its spectral distribution characteristics at different depth levels and identify the main frequency band information.

[0033] S2.2. Based on the main frequency band information, the three-frequency excitation signal is superimposed and transmitted to the formation to obtain the formation reflection signal.

[0034] Furthermore, based on the main frequency band information, the main frequency band range and the noise interference frequency list are identified, and three non-overlapping center frequencies that avoid interference frequencies are selected within the main frequency range. The multi-frequency excitation signal is sent to the formation through the downhole acoustic wave transmitter to stimulate the formation to produce a nonlinear response, and the receiver is used to collect the reflected signal returned by the formation.

[0035] S2.3. Perform harmonic separation on the formation reflection signal to obtain harmonic components.

[0036] Furthermore, the collected formation reflection signals are subjected to adaptive filtering and noise reduction processing to obtain the denoised reflection signals. A narrowband bandpass filter group is used to finely separate the frequency domain, extract the second and third frequency components corresponding to the main frequency, and obtain the harmonic components through the energy value of each frequency point through fast Fourier transform (FFT).

[0037] S3. Calculate the harmonic energy ratio of the depth layer using the harmonic component results.

[0038] S3.1. Perform frequency domain transformation on the harmonic component results to extract the energy of the main frequency and its harmonics.

[0039] Furthermore, the harmonic component results are subjected to fast Fourier transform, converting the time domain signal into the frequency domain signal, so as to clearly identify the main frequency and the corresponding double and triple frequency components, and extract the energy values ​​of these three frequency points in the frequency domain.

[0040] S3.2. Calculate the harmonic energy ratio of the depth layer based on the energy of the main frequency and its harmonics.

[0041] Furthermore, based on the extracted main frequency energy, second frequency energy and third frequency energy, the harmonic energy ratio is calculated at each depth sampling point; Specifically, the expression is, ; in, is the harmonic energy ratio, is the main frequency energy, is the double frequency energy, It is the tripled energy.

[0042] S4. Based on the harmonic energy ratio of the depth layer, the fracture sensitivity index is extracted and the microcrack development area is identified.

[0043] S4.1. Construct a depth-harmonic energy ratio profile based on the harmonic energy ratio of the depth layer.

[0044] Furthermore, all sampling depth points are arranged in order of depth, and the interpolation algorithm is used to smooth the non-uniform sampling points to construct a continuous depth-harmonic energy ratio profile to intuitively reflect the changing trend of the degree of microcrack development in the formation along the depth direction.

[0045] S4.2. Based on the depth-harmonic energy ratio profile, a sliding window statistical method is used to extract the crack sensitivity index and identify the microcrack development area.

[0046] Furthermore, the constructed depth-harmonic energy ratio profile uses a sliding window statistical method to perform local feature analysis. A sliding window of fixed length is set at each depth point, the mean and standard deviation of the crack sensitivity index within the window are calculated, and a dynamic threshold is set. If the crack sensitivity index of the current depth point is greater than the dynamic threshold, it is preliminarily determined that there is a microcrack development area at this depth layer.

[0047] S5. Based on the pre-processed original acoustic logging data in the microfracture development area, a fracture network model is constructed to obtain the spatial distribution information of microfractures.

[0048] S5.1. Use the microfracture development area and the processed original sonic logging data to perform spatial registration to obtain a spatially registered comprehensive data set.

[0049] Furthermore, using the initially identified microfracture development areas and preprocessed raw sonic logging data, coordinate system normalization is performed to ensure that all data are represented in the same reference frame. Specifically, the depth coordinates of the microfracture development areas are matched with the corresponding sonic logging data time series. The coordinates are then converted to a unified depth coordinate system based on the sonic propagation velocity. Interpolation algorithms (such as kriging or spline interpolation) are used to fill gaps in the data, ensuring spatial continuity and consistency. This results in a comprehensive, spatially registered dataset containing microfracture locations, intensities, and related sonic logging parameters.

[0050] S5.2. Based on the microcrack development area, a preliminary fracture network model is formed using graph theory methods.

[0051] Furthermore, based on the preliminary identification of microcrack development areas, the discretely distributed fracture points in space are used as node sets in the graph theory model and are connected according to the set spatial adjacency rules (such as establishing a connection relationship between nodes when the Euclidean distance is less than a threshold to form an edge set, and topologically optimizing the nodes and edges through Delaunay triangulation or minimum spanning tree algorithm to construct a preliminary fracture network model that reflects the connectivity and distribution characteristics of the fracture system).

[0052] S5.3. Input the spatially registered comprehensive dataset into the fracture network model to obtain the spatial distribution information of microcracks.

[0053] Furthermore, the spatially registered comprehensive data set, including microfracture development area identifiers, original acoustic logging parameters (such as P-wave velocity and acoustic amplitude), and depth-time correspondence, is input into a preliminary fracture network model constructed based on graph theory. By mapping the physical properties and spatial position information of each fracture point to the corresponding nodes and edges, and combining the anisotropic characteristics of the formation to perform weighted correction on the fracture connectivity, geologically significant microfracture distribution information is finally generated in three-dimensional space.

[0054] S6. Use the image pyramid algorithm to perform weighted fusion to generate a crack distribution image.

[0055] S6.1. Construct a multi-scale Gaussian pyramid based on the microcrack distribution information to generate image sets at different resolution levels.

[0056] Furthermore, the process of constructing a multi-scale Gaussian pyramid based on microcrack distribution information begins with obtaining an accurate microcrack distribution image, which describes the fracture network in the underground rock formation in detail. The original microcrack distribution image is smoothed using a Gaussian filter to eliminate noise and reduce the influence of high-frequency components. A series of images with gradually decreasing resolution are generated through continuous downsampling operations. Each downsampling step halves the size of the previous layer of image while keeping the proportion and structural characteristics of the image content unchanged, thus forming a set of images that progress from coarse to fine and layer by layer.

[0057] S6.2. Use a weighted fusion formula to fuse image sets at different resolution levels to generate a fused pyramid image set.

[0058] Furthermore, after generating a multi-scale Gaussian pyramid image set, we can fuse these images at different resolution levels through a weighted fusion formula to create an optimized pyramid image set. First, a corresponding weight is assigned to each layer of the image. The weight is determined based on the importance, clarity, or specific analysis requirements of the image content. The weighted average method is used to calculate between the pixel values ​​at the same position but different resolution levels to form a new pixel value. For a pixel at a given position, its new value in the fused image is the weight corresponding to the layer image, which represents the original pixel value at the corresponding position in the layer image. The information at different resolution levels is combined, which not only retains the macroscopic structural features but also retains the microscopic detail information. The final fused pyramid image set not only enhances the expressiveness of the crack features.

[0059] S6.3. Restore detail information through the Laplacian pyramid method and convert the fused image pyramid set into a complete crack distribution image.

[0060] Furthermore, to restore detail information using the Laplacian pyramid method, we first need to take the image pyramid set previously obtained by weighted fusion as input, starting from the top layer of the pyramid, which is the image layer with the lowest resolution and the most significant macroscopic structure, and gradually expand and reconstruct it to the next layer. For each layer, we use the low-resolution image of the previous layer (after magnification) and the Gaussian difference image of the current layer (that is, the original Laplacian layer) to perform pixel-level addition operations to restore and enhance the detail information contained in the layer. Let the magnified image of the previous layer be , and the Laplacian image of the current layer be the restored image. By progressively proceeding layer by layer, we finally obtain a complete crack distribution image at the bottom of the pyramid. S7. Compare and verify the crack distribution image with the fracturing response data to obtain a comprehensive result of the spatial distribution characteristics.

[0061] S7.1. Unify the coordinates of the fracture distribution image and the fracturing response data, and use the squared error to evaluate the consistency between the fracture distribution and the fracturing response.

[0062] Furthermore, the fracture distribution image and the fracturing response data are aligned, and their consistency is assessed using the squared error. First, the fracture distribution image and the fracturing response data (such as pressure change or fracture extension length) must be spatially registered to ensure they are represented in the same coordinate system. This involves adjusting the scale, rotation angle, and translation to eliminate spatial deviations caused by different measurement methods. Once the coordinates are aligned, the squared error formula is used to quantify the consistency between the fracture distribution and the fracturing response.

[0063] S7.2. Compare the fracture distribution of each layer with the fracturing response data point by point and record the deviation map.

[0064] Furthermore, after completing the coordinate unification of the fracture distribution image and the fracturing response data, the fracture distribution of each layer is compared with the corresponding fracturing response data point by point. Starting from the highest resolution layer, for each matching point, the difference value between the fracture distribution image and the fracturing response data is calculated. For each analysis layer, the difference values ​​of all points are summarized to form a deviation map. The map intuitively shows the deviation distribution between the fracture prediction and the actual fracturing response. The deviation map not only records the specific deviation value of each position.

[0065] Specifically, the expression is, ; in, No. The difference between the fracture distribution image and the fracturing response data at the matching point, For coordinates The value of the final crack distribution image at the point, For coordinates The actual fracturing response data value at the point is the horizontal coordinate, is the vertical coordinate.

[0066] S7.3. Generate a comprehensive result of the spatial distribution characteristics based on the deviation map.

[0067] Furthermore, the deviation maps obtained by layer-by-layer comparison are normalized to eliminate the influence of different scales and units. Combined with the original information of the fracture distribution image and the fracturing response data, the areas with large deviations are classified and statistically analyzed to identify typical areas of systematic overestimation or underestimation. On this basis, the geological background data (such as lithology, formation dip, etc.) are used to analyze the cause of the deviation to determine whether it comes from model prediction errors, data acquisition limitations, or complex responses caused by geological heterogeneity. Finally, the deviation information of all layers is integrated with the spatial distribution characteristics of the fractures to generate a complete spatial layout map including fracture prediction results, fracturing response consistency areas, deviation distribution and cause explanation, and form a structured comprehensive results report.

[0068] This embodiment also provides a computer device, which is suitable for the case of a quantitative characterization method of the spatial distribution of microfractures in tight oil reservoirs based on remote acoustic wave detection, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the quantitative characterization method of the spatial distribution of microfractures in tight oil reservoirs based on remote acoustic wave detection as proposed in the above embodiment.

[0069] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0070] This embodiment also provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the program implements the method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0071] In summary, the present invention achieves high-precision detection of formation microcracks by detecting the harmonic components in the reflected signal through multi-frequency superposition of excitation signals based on the pre-processed original acoustic logging data. The function is to accurately capture the nonlinear response caused by microcracks by identifying different frequency components, thereby achieving the purpose of constructing a detailed fracture sensitivity index profile, which not only improves the accuracy of microcrack identification, but also provides a high-quality data foundation for subsequent model construction. Based on the microcrack development area and the processed original acoustic logging data, a fracture network model is constructed to obtain the spatial distribution information of microcracks, realizing the fracture connectivity analysis from point to surface. The function is to optimize the fracture network structure using graph theory methods, and ultimately achieve the effect of comprehensively characterizing the spatial distribution characteristics of microcracks.

[0072] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A quantitative characterization method for the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection, characterized by: include, Collect raw acoustic logging data and fracturing response data from inside and outside the wells and perform pre-processing; Based on the pre-processed original acoustic logging data, the harmonic components in the reflection signal are detected by multi-frequency superposition excitation signals, and the harmonic energy ratio of the depth layer is calculated using the harmonic component results. Based on the harmonic energy ratio of the depth layer, the fracture sensitivity index is extracted and the micro-fracture development area is identified; Based on the micro-fracture development area and the processed original acoustic logging data, a fracture network model is constructed to obtain the spatial distribution information of micro-fractures. The image pyramid algorithm is used for weighted fusion to generate a fracture distribution image. The fracture distribution image is compared and verified with the fracturing response data to obtain a comprehensive result of the spatial distribution characteristics.

2. The method for quantitatively characterizing the spatial distribution of microfractures in a tight oil reservoir based on acoustic remote detection according to claim 1, characterized in that: Collect raw acoustic logging data and fracturing response data between wells and within wells, and perform pre-processing. The following steps are included: Acoustic logging instruments and interwell acoustic detection equipment are used to collect raw acoustic logging data between wells and within the wells, and fracturing response data is collected during the operation; The collected original acoustic logging data and fracturing response data between wells and within wells are preprocessed.

3. The method for quantitatively characterizing the spatial distribution of microfractures in a tight oil reservoir based on acoustic remote detection according to claim 2, characterized in that: Based on the pre-processed original acoustic logging data, the harmonic components in the reflection signal are detected by multi-frequency superposition excitation signals. The following steps are included: Perform time-frequency analysis on the pre-processed raw acoustic logging data to identify the main frequency band information; According to the main frequency band information, the three-frequency excitation signal is superimposed and transmitted to the formation to obtain the formation reflection signal; Perform harmonic separation on the formation reflection signal to obtain the harmonic components.

4. The method for quantitatively characterizing the spatial distribution of microfractures in a tight oil reservoir based on acoustic remote detection according to claim 3, characterized in that: Using the harmonic component results, the harmonic energy ratio of the depth layer is calculated, including the following steps: Perform frequency domain transformation on the harmonic component results to extract the energy of the main frequency and its harmonics; Based on the energy of the main frequency and its harmonics, the harmonic energy ratio of the depth layer is calculated.

5. The method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection according to claim 4, characterized in that: Based on the harmonic energy ratio of the depth layer, the fracture sensitivity index is extracted and the micro-crack development area is identified. The following steps are included: Based on the harmonic energy ratio of the depth layer, a depth-harmonic energy ratio profile is constructed; Based on the depth-harmonic energy ratio profile, a sliding window statistical method was used to extract the crack sensitivity index and identify the microcrack development area.

6. The method for quantitatively characterizing the spatial distribution of microfractures in a tight oil reservoir based on acoustic remote detection according to claim 5, characterized in that: Based on the micro-fracture development area and the processed original acoustic logging data, a fracture network model is constructed to obtain the spatial distribution information of micro-fractures. The following steps are included: The microfracture development area and the processed original acoustic logging data are spatially registered to obtain a comprehensive dataset after spatial registration. Based on the microcrack development area, a preliminary fracture network model was formed using graph theory methods; The spatially registered comprehensive dataset is input into the fracture network model to obtain the spatial distribution information of microcracks.

7. The method for quantitatively characterizing the spatial distribution of microfractures in a tight oil reservoir based on acoustic remote detection according to claim 6, characterized in that: The image pyramid algorithm is used for weighted fusion to generate a crack distribution image, which includes the following steps: Construct a multi-scale Gaussian pyramid based on microcrack distribution information to generate image sets at different resolution levels; Use the weighted fusion formula to fuse image sets at different resolution levels to generate a fused pyramid image set; The detail information is restored by the Laplacian pyramid method, and the fused image pyramid set is converted into a complete crack distribution image.

8. The method for quantitatively characterizing the spatial distribution of microfractures in tight oil reservoirs based on acoustic remote detection according to claim 7, characterized in that: Comparing and verifying the crack distribution image with the fracturing response data, a comprehensive result of the spatial distribution characteristics is obtained, including the following steps: The fracture distribution image and the fracturing response data were unified, and the consistency between the fracture distribution and the fracturing response was evaluated using the squared error. Compare the fracture distribution of each layer with the fracturing response data point by point and record the deviation map; Based on the deviation map, a comprehensive result of the spatial distribution characteristics is generated.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for quantitatively characterizing the spatial distribution of microfractures in a tight oil reservoir based on acoustic remote detection are implemented as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for quantitatively characterizing the spatial distribution of microfractures in a tight oil reservoir based on acoustic remote detection are implemented.

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