Method for evaluating effectiveness of well-side fracture-cave reservoir based on acoustic wave remote detection imaging

Abstract

This invention discloses a method for evaluating the effectiveness of well-side fractured-cavity reservoirs based on acoustic long-range imaging, relating to the field of oil and gas exploration technology. The method includes: extracting amplitude attributes, including root mean square amplitude, instantaneous amplitude, mean absolute amplitude, and half-time window energy percentage; extracting frequency attributes, including instantaneous frequency and mean instantaneous frequency; using all extracted amplitude and frequency attributes as input data, employing principal component analysis (PCA) for multi-dimensional information dimensionality reduction and fusion, extracting the three principal components with the highest cumulative contribution rate as input data for RGB fusion; normalizing the three principal components and mapping them one-to-one with the red, green, and blue channels of the three primary colors of optical systems; and completing multi-attribute RGB fusion imaging through the superposition of the three primary colors to obtain a comprehensive identification result of the effectiveness of well-side fractured-cavity reservoirs. This invention provides a new method for well-logging evaluation of well-side fractured-cavity reservoirs and provides guidance for oil and gas reservoir exploration and development.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method for evaluating the effectiveness of well-side fractured-cavity reservoirs based on acoustic remote detection imaging. Background Technology

[0002] The effectiveness assessment of fractured-vuggy reservoirs is a crucial aspect of well logging interpretation in carbonate reservoirs. Acoustic remote sounding is an effective method for detecting fractured-vuggy reservoirs near the wellbore using reflected waves. Acoustic remote sounding logging data can be used to evaluate the length, dip, angle, and strike of fractures extending beyond the wellbore. Combined with well test permeability data or production data, lower limits for sensitive logging fracture attribute parameters corresponding to effective fractures and different fracture grades can be determined, and comprehensive logging evaluation standards for effective fractures and fracture grades can be established, thus achieving fracture effectiveness assessment. Current technologies only utilize acoustic remote sounding imaging data to calculate the geological parameters of fractured-vuggy reservoirs near the wellbore, failing to deeply explore the effective information in the imaging, unable to identify the filling characteristics of fractured-vuggy reservoirs, and requiring additional data such as well tests and production data to complete the evaluation, resulting in gaps in the content of fractured-vuggy reservoir effectiveness assessment. Summary of the Invention

[0003] To address the shortcomings of existing acoustic remote sounding imaging-based fracture-vuggy reservoir evaluation technologies, such as insufficient information mining and lack of evaluation dimensions, which fail to meet the needs of carbonate reservoir exploration, this invention discloses a well-side fracture-vuggy reservoir effectiveness evaluation method based on acoustic remote sounding imaging. This method first selects amplitude attributes from the raw acoustic remote sounding data that accurately highlight the morphological characteristics of the fracture-vuggy reservoir, achieving a detailed characterization of the spatial distribution of the well-side fracture-vuggy reservoir and improving the imaging signal-to-noise ratio. Second, it utilizes frequency attributes sensitive to the type of filling medium inside the fractures and cavities to classify the filling type of the well-side fracture-vuggy reservoir. Finally, it employs principal component analysis to fuse the amplitude and frequency attributes, deeply mining core information related to the effectiveness of the fracture-vuggy reservoir, ultimately achieving an effective evaluation of the well-side fracture-vuggy reservoir.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] The method for evaluating the effectiveness of fractured-cavity reservoirs near wells based on acoustic long-range imaging includes the following steps:

[0006] s1. For the raw acoustic remote sensing data of the target well, calculate the core amplitude attributes, including root mean square amplitude, instantaneous amplitude, mean absolute amplitude and half-time window energy percentage attributes, to characterize the spatial distribution of the well-side fractured-cavity reservoir and improve the imaging signal-to-noise ratio;

[0007] s2. For the raw acoustic remote detection data of the target well, calculate the core frequency attributes, including instantaneous frequency and average instantaneous frequency. Based on the frequency response characteristics, make a preliminary judgment on the type of filling medium inside the fractured-vuggy reservoir next to the well, which is used to distinguish between effective reservoirs and ineffective reservoirs.

[0008] s3. Using all amplitude and frequency attributes extracted in steps s1 and s2 as input data, principal component analysis is used to reduce and fuse multi-dimensional information, and the top three principal components with the highest cumulative contribution rate are extracted as input data for RGB fusion.

[0009] s4. Normalize the three principal components extracted in step s3, and map the normalized three principal components to the red, green and blue channels of the three primary colors of the optical system. Multi-attribute RGB fusion imaging is completed by superimposing the three primary colors, and finally the comprehensive identification results of the effectiveness of the well-side fractured cavern reservoir are obtained.

[0010] Amplitude properties primarily reflect the vibration intensity of seismic waves and are commonly used to identify amplitude anomalies and stratigraphic features. Based on a survey of commonly used amplitude properties in earthquakes and combined with acoustic long-range detection data, this invention selects root mean square amplitude, instantaneous amplitude, mean absolute amplitude, and half-time window energy percentage properties.

[0011] Optionally, in step s1, the root mean square (RMS) attribute is an important parameter in the amplitude attributes, reflecting a statistical characteristic of seismic wave amplitude. The RMS amplitude is obtained by calculating the RMS value of the amplitude at each sampling point within a fixed time window of the acoustic far-field probe signal. Its calculation formula is as follows:

[0012] ;

[0013] In the formula, The root mean square amplitude, To fix the amplitude values ​​of each sampling point within the time window, The time window length;

[0014] Instantaneous amplitude represents the amplitude of a seismic wave at a specific location at a particular moment during its propagation. It reflects the energy of the seismic wave at that point and is an important manifestation of the dynamic characteristics of seismic waves. Instantaneous amplitude is the square root of the real and imaginary parts of a complex signal, used to reflect changes in energy. The calculation formula is as follows:

[0015] ;

[0016] In the formula, For the instantaneous amplitude based on the Hilbert transform, For acoustic long-range detection signals, yes The Hilbert transform result represents the orthogonal components of the acoustic far-field detection signal;

[0017] The mean absolute amplitude is the average of the absolute values ​​of the amplitudes at all sampling points. It reflects the average displacement of a particle from its equilibrium position as the sound wave propagates through the underground medium. The formula for its calculation is:

[0018] ;

[0019] In the formula, The average absolute amplitude;

[0020] The energy half-decay time is calculated by accumulating energy values ​​point-by-point along the depth direction within a fixed time window. When the accumulated value reaches 50% of the total energy of the time window, it represents the percentage of the interval between the sample point at that location and the start point of the time window out of the total number of samples in the time window. The half-time window energy percentage attribute reflects the accumulation and distribution of seismic wave energy within the analysis time window, and the calculation formula is as follows:

[0021] ;

[0022] ;

[0023] In the formula, The amplitude is attenuated by half its energy. This represents the percentage of samples that occur when the energy decays by half, out of the total number of samples. This represents the number of samples corresponding to half the energy decay.

[0024] According to the numerical simulation results of acoustic remote sensing, the dominant signal frequency is 5 kHz when the formation is free of fractures, drops to 4 kHz when fractures are present, and decreases further when gas is present. Therefore, the frequency attribute can be used to obtain fluid information in the well-side reflector.

[0025] Optionally, in step s2, the instantaneous frequency attribute is closely related to the travel time of the reflected wave. It often exhibits low-frequency shift characteristics in fracture-developed areas, and the frequency decreases significantly when fluid is present. This characteristic makes it an effective marker for fracture identification and filling material classification. Instantaneous frequency The calculation formula is:

[0026] ;

[0027] The average instantaneous frequency algorithm, by calculating the first moment in the time-frequency domain of the signal, can significantly enhance the ability to identify reflective interfaces. Average instantaneous frequency The calculation formula is:

[0028] .

[0029] Optionally, in step s3, a multi-attribute RGB fusion method based on principal component analysis is used to further mine the morphological and fluid information in the acoustic remote detection imaging profile, providing richer logging data for reservoir evaluation. The amplitude, frequency, and special attributes of the acoustic remote detection data are calculated, and the results are used as input for multi-attribute fusion. It is assumed that the original data consists of several attribute volumes, which are combined into a new data volume Array of size m. n and m are the number of elements in the attribute body, and n is the number of attribute bodies; each element in the matrix is ​​represented as... , For group number, For the channel number; first, perform Z-Score normalization on the combined data volume Array:

[0030] ;

[0031] In the formula, Let j be the mean of the j-th attribute body. For the first The standard deviation of each attribute. The attribute data is standardized, with each attribute having a mean of 0 and a variance of 1.

[0032] Then, using the standardized attribute volume, the covariance matrix R is calculated:

[0033] ;

[0034] In the formula, For the first The earthquake attribute and the first The degree of linear correlation between earthquake attributes;

[0035] The calculated eigenvalues ​​are then processed according to... The order is rearranged; the corresponding feature vector is ,in ; n new attribute volumes are formed by eigenvectors, i.e., principal components. ,as follows:

[0036] ;

[0037] In the formula, ;

[0038] In principal components middle, Indicates the first The variance of an attribute is considered; a larger variance indicates a greater contribution of that component to the total variance, and more information is contained within the attribute. Therefore, through eigenvalues... Calculate the first The importance of each principal component in the data volume :

[0039] ;

[0040] In the formula, The principal components have been arranged in descending order. Contribution rate meets ;

[0041] Principal component analysis was used to extract the first three principal components as the initial data for RGB fusion.

[0042] Optionally, in step s4, each principal component is normalized so that its threshold matches the color channel:

[0043] ;

[0044] In the formula, It is a red channel. For green channel, The blue channel;

[0045] By superimposing three colors according to the three primary optical colors, multi-attribute RGB fusion for long-range acoustic detection is achieved, providing a basis for the identification and characterization of well-side fractured and cavernous reservoirs.

[0046] The beneficial effects of this invention are as follows: Addressing the limitation of existing in-well acoustic long-range detection technologies in evaluating the effectiveness of fractured-vuggy reservoirs due to their reliance on single attributes, this invention constructs a progressive reservoir evaluation method that enhances the boundary of fractured-vuggy reservoirs, identifies infill materials, and provides a comprehensive evaluation. First, it enhances reflectors through amplitude attribute optimization, effectively improving the signal-to-noise ratio of the imaging profile and clearly presenting the spatial distribution characteristics of fractures and vuggies, providing a high-quality data foundation for subsequent analysis. Second, it constructs a frequency attribute analysis method for acoustic long-range detection, mining frequency domain information and classifying the types of fractured-vuggy infill materials near the well, overcoming the inherent limitation of amplitude attributes in distinguishing the properties of infill materials. Third, it integrates amplitude and frequency attributes based on principal component analysis and utilizes the RGB three primary colors to achieve a method for evaluating the effectiveness of fractured-vuggy reservoirs. This invention forms a complete technology for evaluating the effectiveness of fractured-vuggy reservoirs, providing guidance for the exploration of carbonate fractured-vuggy oil and gas reservoirs. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method for evaluating the effectiveness of well-side fractured-cavity reservoirs based on acoustic remote detection imaging, as described in this invention.

[0048] Figure 2 The following is a principal component analysis result of well A shown in an embodiment of the present invention, wherein (a) is the loading matrix and (b) is the variance contribution rate;

[0049] Figure 3This is a two-dimensional imaging profile of the three principal component wells of well A shown in an embodiment of the present invention;

[0050] Figure 4 This is an example of the interpretation results of acoustic remote detection imaging of well A, as shown in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] A method for evaluating the effectiveness of well-side fractured-cavity reservoirs based on acoustic long-range imaging, such as... Figure 1 As shown, it includes the following steps:

[0053] s1. For the raw acoustic remote sensing data of the target well, calculate the core amplitude attributes, including root mean square amplitude, instantaneous amplitude, average absolute amplitude and half-time window energy percentage attributes, to characterize the spatial distribution of the well-side fractured-cavity reservoir and improve the imaging signal-to-noise ratio.

[0054] The root mean square amplitude is obtained by calculating the root mean square value of the amplitude of the sampling points within a fixed time window of the acoustic far-field detection signal. A time window size of 11 is recommended. The calculation formula is as follows:

[0055] ;

[0056] In the formula, The root mean square amplitude, To fix the amplitude values ​​of each sampling point within the time window, This represents the number of sampling points within the time window.

[0057] The instantaneous amplitude is the square root of the real and imaginary parts of a complex signal, used to reflect changes in energy. The calculation formula is as follows:

[0058] ;

[0059] In the formula, For the instantaneous amplitude based on the Hilbert transform, For acoustic long-range detection signals, yes The Hilbert transform result represents the orthogonal components of the acoustic far-field detection signal;

[0060] The mean absolute amplitude is the average of the absolute values ​​of the amplitudes at all sampling points. It reflects the average displacement of a particle from its equilibrium position as the sound wave propagates through the underground medium. The formula for its calculation is:

[0061] ;

[0062] In the formula, The average absolute amplitude;

[0063] The half-window energy percentage attribute reflects the accumulation and distribution of seismic wave energy within the analysis window. The calculation formula is as follows:

[0064] ;

[0065] ;

[0066] In the formula, The amplitude is attenuated by half its energy. This represents the percentage of samples that occur when the energy decays by half, out of the total number of samples. This represents the number of samples corresponding to half the energy decay.

[0067] s2. For the raw acoustic remote sensing data of the target well, calculate the core frequency attributes, including instantaneous frequency and average instantaneous frequency. Based on the frequency response characteristics, make a preliminary judgment on the type of filling medium inside the fractured-vuggy reservoir next to the well, which is used to distinguish between effective and ineffective reservoirs.

[0068] instantaneous frequency The calculation formula is:

[0069] ;

[0070] Average instantaneous frequency The calculation formula is:

[0071] .

[0072] s3. Using all amplitude and frequency attributes extracted in steps s1 and s2 as input data, principal component analysis is used to reduce and fuse multi-dimensional information, and the top three principal components with the highest cumulative contribution rate are extracted as input data for RGB fusion.

[0073] Assume the original data consists of several attribute bodies, which are combined into a new data body Array of size m. n and m are the number of elements in the attribute body, and n is the number of attribute bodies; each element in the matrix is ​​represented as... , For group number, For the channel number; first, perform Z-Score normalization on the combined data volume Array:

[0074] ;

[0075] In the formula, Let j be the mean of the j-th attribute body. For the first The standard deviation of each attribute. The attribute data is standardized, with each attribute having a mean of 0 and a variance of 1.

[0076] Then, using the standardized attribute volume, the covariance matrix R is calculated:

[0077] ;

[0078] In the formula, For the first The earthquake attribute and the first The degree of linear correlation between earthquake attributes;

[0079] The calculated eigenvalues ​​are then processed according to... The order is rearranged; the corresponding feature vector is ,in ; n new attribute volumes are formed by eigenvectors, i.e., principal components. ,as follows:

[0080] ;

[0081] In the formula, ;

[0082] In principal components middle, Indicates the first The variance of an attribute is considered; a larger variance indicates a greater contribution of that component to the total variance, and more information is contained within the attribute. Therefore, through eigenvalues... Calculate the first The importance of each principal component in the data volume :

[0083] ;

[0084] In the formula, The principal components have been arranged in descending order. Contribution rate meets ;

[0085] Principal component analysis was used to extract the first three principal components as the initial data for RGB fusion.

[0086] s4. Normalize the three principal components extracted in step s3, and map the normalized three principal components to the red, green and blue channels of the three primary colors of the optical system. Multi-attribute RGB fusion imaging is completed by superimposing the three primary colors, and finally the comprehensive identification results of the effectiveness of the well-side fractured cavern reservoir are obtained.

[0087] Normalize each principal component so that its threshold matches the color channel:

[0088] ;

[0089] In the formula, It is a red channel. For green channel, The blue channel;

[0090] By superimposing three colors according to the three primary optical colors, multi-attribute RGB fusion for long-range acoustic detection is achieved, providing a basis for the identification and characterization of well-side fractured and cavernous reservoirs.

[0091] The Tarim Basin is a region rich in deep to ultra-deep natural gas, with clastic rocks being the primary exploration target. Under tectonic stress, a rich network of fractures has formed, and core analysis and outcrop observations indicate that the reservoir space is dominated by fractures, with a small number of pores. The target stratum is primarily composed of fine sandstone, followed by medium and coarse sandstone, and minor amounts of siltstone and gravelly sandstone, with a porosity of approximately 4% and a permeability of approximately 1.47 mD, classifying it as a fractured tight sandstone reservoir.

[0092] Well A is an exploration well in this area. To accurately characterize the fracture development, sonic remote logging was conducted using the XMAC-F1 logging tool. Principal component analysis of Well A using the method proposed in this invention reveals that, as... Figure 2 As shown in (a), PC1 is mainly dominated by three amplitude-related attributes, with a load value of approximately 0.56; PC2 is dominated by two frequency-related attributes, with a load value of approximately 0.70; and PC3 is almost entirely contributed by the half-decay attribute alone, with a load value as high as 0.9845. This indicates that the three principal components correspond to the three independent physical dimensions of sound wave remote detection: energy, frequency, and attenuation. The cumulative contribution rate of the first three principal components can reach 91%, as shown in (a). Figure 2 As shown in (b). Figure 3The three principal component wells are shown in two-dimensional imaging profiles. The horizontal axis represents the distance from the wellbore, and the vertical axis represents the depth. The white strip in the middle represents the wellbore. PC1 has a dark blue background overall, with isolated bright yellow strong energy anomalies only appearing at specific depths. PC2 has a uniform yellow-green background with scattered spots to reflect the macroscopic lithology and frequency variations of the formation. PC3 shows a blue-green striped texture and an orange-yellow anomaly indicating strong attenuation at a depth similar to that of PC1. The combination of the two visually demonstrates the spatial distribution of geological information in different physical dimensions after dimensionality reduction, providing multi-dimensional evidence for the identification of lithology and fluidity of reservoirs near the well.

[0093] The RGB multi-attribute fusion method proposed in this invention was used to obtain the attribute fusion interpretation map of well-side acoustic remote detection, as shown in the figure. Figure 4 As shown, the first channel represents natural gamma, the second acoustic wave, the third traditional amplitude attribute, and the fourth fused attribute. In the 7275–7300 m depth range, the changes in natural gamma and acoustic waves are not significant, making it impossible to accurately determine well-side fracture information. In the 90° acoustic wave long-range detection profile at a depth of 7275 m, a cross-well fracture approximately 15 m long with an inclination of about 50° is developed. The multi-attribute fused image at a depth of 7300 m appears red and purple, indicating fracture development, fluid filling, and good reservoir effectiveness; while the multi-attribute fused image at a depth of 7250 m appears blue, suggesting that the fracture is filled with solids, indicating an ineffective reservoir.

[0094] The application of this technology in the actual work of identifying and finely characterizing fractures and cavities in Well A has achieved good results, providing strong technical support for oil and gas exploration and reservoir location, which is of great significance.

[0095] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for evaluating the effectiveness of well-side fractured-cavity reservoirs based on acoustic long-range imaging, characterized in that, Includes the following steps: s1. For the raw acoustic remote sensing data of the target well, calculate the core amplitude attributes, including root mean square amplitude, instantaneous amplitude, mean absolute amplitude and half-time window energy percentage attributes, to characterize the spatial distribution of the well-side fractured-cavity reservoir and improve the imaging signal-to-noise ratio; s2. For the raw acoustic remote detection data of the target well, calculate the core frequency attributes, including instantaneous frequency and average instantaneous frequency. Based on the frequency response characteristics, make a preliminary judgment on the type of filling medium inside the fractured-vuggy reservoir next to the well, which is used to distinguish between effective reservoirs and ineffective reservoirs. s3. Using all amplitude and frequency attributes extracted in steps s1 and s2 as input data, principal component analysis is used to reduce and fuse multi-dimensional information, and the top three principal components with the highest cumulative contribution rate are extracted as input data for RGB fusion. s4. Normalize the three principal components extracted in step s3, and map the normalized three principal components to the red, green and blue channels of the three primary colors of the optical system. Multi-attribute RGB fusion imaging is completed by superimposing the three primary colors, and finally the comprehensive identification results of the effectiveness of the well-side fractured cavern reservoir are obtained.

2. The method for evaluating the effectiveness of well-side fractured-cavity reservoirs based on acoustic long-range imaging as described in claim 1, characterized in that, In step s1, the root mean square amplitude is obtained by calculating the root mean square value of the amplitude at each sampling point within a fixed time window of the acoustic far-field detection signal. The calculation formula is as follows: ； In the formula, The root mean square amplitude, To fix the amplitude values ​​of each sampling point within the time window, The time window length; The instantaneous amplitude is the square root of the real and imaginary parts of a complex signal, used to reflect changes in energy. The calculation formula is as follows: ； In the formula, For the instantaneous amplitude based on the Hilbert transform, For acoustic long-range detection signals, yes The Hilbert transform result represents the orthogonal components of the acoustic far-field detection signal; The mean absolute amplitude is the average of the absolute values ​​of the amplitudes at all sampling points. It reflects the average displacement of a particle from its equilibrium position as the sound wave propagates through the underground medium. The formula for its calculation is: ； In the formula, The average absolute amplitude; The half-time window energy percentage attribute reflects the accumulation and distribution of acoustic energy within the analysis time window. The calculation formula is as follows: ； ； In the formula, The amplitude is attenuated by half its energy. This represents the percentage of samples that occur when the energy decays by half, out of the total number of samples. This represents the number of samples corresponding to half the energy decay.

3. The method for evaluating the effectiveness of well-side fractured-cavity reservoirs based on acoustic long-range imaging as described in claim 1, characterized in that, In step s2, instantaneous frequency The calculation formula is: ； Average instantaneous frequency The calculation formula is: 。 4. The method for evaluating the effectiveness of well-side fractured-cavity reservoirs based on acoustic long-range imaging as described in claim 1, characterized in that, In step s3, it is assumed that the original data consists of several attribute bodies, which are combined into a new data body Array of size m. n and m are the number of elements in the attribute body, and n is the number of attribute bodies; each element in the matrix is ​​represented as... , For group number, For the channel number; first, perform Z-Score normalization on the combined data volume Array: ； In the formula, Let j be the mean of the j-th attribute body. For the first The standard deviation of each attribute. The attribute data is standardized, with each attribute having a mean of 0 and a variance of 1. Then, using the standardized attribute volume, the covariance matrix R is calculated: ； In the formula, For the first The earthquake attribute and the first The degree of linear correlation between earthquake attributes; The calculated eigenvalues ​​are then processed according to... The order is rearranged; the corresponding feature vector is ,in ; n new attribute volumes are formed by eigenvectors, i.e., principal components. ,as follows: ； In the formula, ; In principal components middle, Indicates the first The variance of an attribute is considered; a larger variance indicates a greater contribution of that component to the total variance, and more information is contained within the attribute. Therefore, through eigenvalues... Calculate the first The importance of each principal component in the data volume : ； In the formula, The principal components have been arranged in descending order. Contribution rate meets ; Principal component analysis was used to extract the first three principal components as the initial data for RGB fusion.

5. The method for evaluating the effectiveness of well-side fractured-cavity reservoirs based on acoustic long-range imaging as described in claim 1, characterized in that, In step s4, each principal component is normalized so that its threshold matches the color channel: ； In the formula, It is a red channel. For green channel, The blue channel; By superimposing three colors according to the three primary optical colors, multi-attribute RGB fusion for long-range acoustic detection is achieved, providing a basis for the identification and characterization of well-side fractured and cavernous reservoirs.

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