Method for identifying shale cracks based on conventional logging information

Abstract

The invention provides a method for identifying shale cracks based on conventional logging information. The method comprises the steps that 1, an interval transit time logging curve and a natural gamma logging curve of a shale section are obtained; 2, discrete wavelet transform and box dimension calculation are carried out on the interval transit time curve and the natural gamma curve, and a wavelet coefficient and a box dimension are obtained; 3, solving a difference value between the wavelet coefficient of the interval transit time curve and the wavelet coefficient of the natural gamma curve to obtain a wavelet coefficient difference value, and solving a difference value between the box dimension of the interval transit time curve and the box dimension of the natural gamma curve to obtain a box dimension difference value; 4, averaging the wavelet coefficient difference value and the fractal dimension difference value, and constructing a fracture development response index; and step 5, according to the relatively high value of the fracture development response index, performing fracture identification in the shale section. According to the method for identifying the shale crack based on the conventional logging information, the shale crack development section can be accurately and reliably identified.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield logging, and particularly to a method for identifying shale fractures based on conventional logging data. Background Art

[0002] Shale reservoirs are characterized by low porosity and low permeability, and are generally good source rocks and cap rocks. Fractures refer to discontinuous surfaces formed by the rock being stressed and fractured, and often develop in extra-low permeability reservoirs. The reservoir space of shale reservoirs usually consists of pores and fractures. Fractures are both migration channels and reservoir spaces, and are one of the main controlling factors for the productivity of shale reservoirs.

[0003] Core and imaging logging data are important means for identifying fractures, with characteristics such as intuitiveness and high identification accuracy. However, core and imaging logging are costly and the data is limited. Therefore, it is of great significance to identify fractures using conventional logging data. The existing methods for identifying shale fractures using conventional logging data are achieved by calibrating conventional logging data with limited drilling core data and imaging logging data, such as the logging response characteristic method, the three porosity ratio method, the resistivity invasion correction difference ratio method, etc. Conventional logging data is jointly affected by factors such as lithology, fluid, and fractures. Logging curves have a certain response to fractures, but the response characteristics are not obvious. At the same time, the response characteristics of conventional logging curves have multiple solutions, and fractures are not the only factor causing the response of logging curves. It is relatively difficult to directly identify fractures using conventional logging data.

[0004] When fractures appear in shale, transient characteristics will appear on the logging curve, which are reflected in the changes and details of the logging signal. For this reason, we change our thinking and identify shale fractures by extracting and amplifying the logging response of fractures and eliminating the influence of non-fracture factors. Obtain the formation heterogeneity information in the logging curve, eliminate the vertical heterogeneity caused by the cyclic change of formation lithology, and highlight the vertical heterogeneity of the formation caused by fractures.

[0005] Based on the theoretical basis of wavelet analysis, combining logging with multi-scale analysis theory, extract the wavelet high-frequency attributes of the logging signal, and amplify the oscillation characteristics of fracture information through high-frequency attributes, so as to identify shale fractures. The fractal dimension itself is a quantitative characterization parameter describing the complexity of an object. When fractures develop in the reservoir section, it increases the complexity and abnormality of the logging data, which is reflected in the fractal feature as an increase in the fractal dimension.

[0006] To identify the fracture development section in the shale section by performing wavelet transform and fractal dimension analysis on conventional logging curves, it is also necessary to combine the core data and imaging logging data in the study area for calibration and correction to achieve the best results.

[0007] In the Chinese patent application with the application number: CN202010010511.7, it involves a method and device for identifying fractures and characterizing the development degree of a shale reservoir. The method includes: based on the distribution difference cross-plot of acoustic travel time and resistivity in the fracture development section and the non-fracture development section, weighing the influence of lithology, using a weighted algorithm to fuse the natural gamma logging data and the acoustic travel time logging data to obtain the lithology-physical property fusion parameter for fracture identification, and combining the normalized resistivity to obtain the response chart for preliminary fracture identification; using the response chart for preliminary fracture identification to perform a primary separation on the data points, and performing a secondary separation on the data points in the overlapping part of the response chart for preliminary fracture identification to obtain the final fracture identification result. The embodiment of the invention uses conventional logging data to identify and characterize fractures, greatly improving the efficiency of logging interpretation and reducing the cost of logging interpretation.

[0008] In the Chinese patent application with the application number: CN201710487285.X, it involves a method for identifying fractures in a tight limestone reservoir, belonging to the technical field of logging, and is used to solve the technical problems of low identification accuracy and easy misjudgment existing in the prior art. The invention improves the comprehensive fracture identification model by using the acoustic porosity, natural gamma value, and relative value of deep lateral resistivity, constructs a new comprehensive fracture identification index, which has a simple form and high identification accuracy, avoiding the disadvantages of easy misjudgment and low identification accuracy existing in the traditional method for identifying fractures, and establishes a fracture grading identification standard according to the size of the comprehensive fracture identification index FRA to guide the exploration and development work of this type of oil and gas reservoir.

[0009] In the Chinese patent application with the application number: CN201911241396.8, it involves a method for identifying fractures and quantitatively calculating porosity in a tight reservoir, including: cutting the acoustic logging curve and density logging curve respectively for acoustic wave and density, cutting the extreme points of the curve, and when encountering a straight line segment, taking its midpoint as the cutting point; calculating the correlation coefficient for the cut curve segments according to the principle of the correlation coefficient; calculating the AC-DEN correlation coefficient to identify the fracture development section and non-fracture development section of the tight reservoir; evaluating the fractures, establishing an equation set using logging data to minimize the objective function, and obtaining the comprehensive skeleton of the acoustic travel time and density of the reservoir; calculating the shale content of the tight reservoir; establishing a calculation model for fracture porosity using the acoustic travel time and density logging data. Comparing the calculated result of fracture porosity with the calculation result of FMI, the fracture identification accuracy is improved, and using conventional logging data to identify and evaluate fractures greatly improves the efficiency of logging interpretation.

[0010] The above prior arts are all quite different from the present invention and fail to solve the technical problems we want to solve. Therefore, we have invented a new method for identifying shale fractures based on conventional logging data. Summary of the Invention

[0011] The object of the present invention is to provide an accurate and reliable method for identifying shale fractures based on conventional logging data.

[0012] The object of the present invention can be achieved by the following technical measures: A method for identifying shale fractures based on conventional logging data, the method for identifying shale fractures based on conventional logging data includes:

[0013] Step 1, obtaining the acoustic time difference logging curve and natural gamma logging curve of the shale section;

[0014] Step 2, performing discrete wavelet transform and box dimension calculation on the acoustic time difference curve and natural gamma curve to obtain wavelet coefficients and box dimensions;

[0015] Step 3, obtaining the difference between the wavelet coefficients of the acoustic time difference curve and the wavelet coefficients of the natural gamma curve to obtain the wavelet coefficient difference, and obtaining the difference between the box dimension of the acoustic time difference curve and the box dimension of the natural gamma curve to obtain the box dimension difference;

[0016] Step 4, averaging the wavelet coefficient difference and the fractal dimension difference to construct a fracture development response index;

[0017] Step 5. Identifying fractures in the shale section based on the relatively high values of the fracture development response index.

[0018] The object of the present invention can also be achieved by the following technical measures:

[0019] In step 1, analyzing the logging curves of the shale section to obtain the acoustic time difference logging curve AC and the natural gamma logging curve GR for shale fracture identification.

[0020] In step 1, according to logging theory, the acoustic time difference AC curve can not only reflect the vertical inhomogeneity caused by the lithologic cycle change of the formation, but also reflect the local inhomogeneity caused by secondary pores and fractures; the natural gamma GR curve mainly reflects the vertical inhomogeneity caused by the lithologic cycle change of the formation.

[0021] In step 2, performing discrete wavelet transform on the acoustic time difference curve and the natural gamma curve, decomposing the logging curve into approximate wavelet coefficients and detailed wavelet coefficients, and the discrete wavelet transform is defined as the sum of all original signals f(t) multiplied by the wavelet function ψ(t) after translation and scaling of the independent variable t. The calculation formula of the wavelet transform coefficient is as follows:

[0022]

[0023] Where W f(a, b) are wavelet transform coefficients, f(t) is the original signal, ψ(t) is the wavelet function, and a and b are the scale factor and displacement factor respectively.

[0024] In step 2, the db 11 wavelet function is used to perform three-layer wavelet decomposition on the AC curve and GR curve. According to the conformity with the fracture development situation obtained from the imaging logging in the shale section, the detailed wavelet coefficients cD2 of the second-layer decomposition are mainly analyzed. For the convenience of subsequent analysis, cD2 is normalized in the shale section, and the upper envelope of the normalized wavelet coefficients is obtained through Hilbert transform, and finally the wavelet coefficients of the acoustic travel time curve and the natural gamma curve are obtained.

[0025] In step 2, normalization processing of wavelet coefficients:

[0026]

[0027] where cD2N is the normalized wavelet coefficient, and cD2 max is the maximum value of the wavelet coefficients in the shale section.

[0028] In step 2, fractal dimension analysis is performed on the acoustic travel time curve and the natural gamma curve to obtain the box dimensions of the acoustic travel time curve and the natural gamma curve. The box dimension is defined as:

[0029]

[0030] where r is the length of the small box, N(r) is the minimum number of boxes covering the logging curve, and D is the box dimension.

[0031] In step 2, the box dimension of the AC curve and GR curve is obtained by using the sliding window method. The step size is 1 logging data point, and the depth value corresponding to the middle logging data point of the window is used as the depth value of the box dimension. According to the conformity with the fracture development situation obtained from the imaging logging in the shale section, the box dimension with a window length of 11 logging data points is mainly analyzed, and finally the box dimensions of the acoustic travel time curve and the natural gamma curve are obtained.

[0032] In step 2, the wavelet coefficients and fractal dimensions of the AC curve reflect the vertical inhomogeneity characteristics caused by the local change of rock physical properties superimposed on the lithology change; although the wavelet coefficients and fractal dimensions of the GR curve have no obvious advantage in detecting the local physical property changes of formation rocks, they mainly reflect the vertical inhomogeneity caused by the cyclic change of formation lithology.

[0033] In step 3, the difference between the wavelet coefficients of the acoustic travel time curve and the wavelet coefficients of the natural gamma curve is calculated to obtain the wavelet coefficient difference. The formula is as follows:

[0034] cD2M = |c2AB - c2GB|

[0035] Wherein, cD2M is the difference of wavelet coefficients, c2AB is the wavelet coefficient of the acoustic travel time curve, and c2GB is the wavelet coefficient of the natural gamma ray curve.

[0036] In step 3, obtain the difference between the box dimension of the acoustic travel time curve and the box dimension of the natural gamma ray curve to get the box dimension difference. The formula is as follows:

[0037] BFDM = |BFDA - BFDG|

[0038] Wherein, BFDM is the box dimension difference, BFDA is the box dimension of the acoustic travel time curve, and BFDG is the box dimension of the natural gamma ray curve.

[0039] In step 3, use the difference between the wavelet coefficients of the acoustic travel time curve and the natural gamma ray curve, and the difference between the box dimensions of the acoustic travel time curve and the natural gamma ray curve to eliminate the vertical heterogeneity caused by the cyclic variation of formation lithology and highlight the vertical heterogeneity of the formation caused by fractures.

[0040] In step 4, comprehensively consider the responses of the difference of wavelet coefficients and the difference of fractal dimensions to shale fractures, average the difference of wavelet coefficients and the difference of fractal dimensions, and construct a fracture development response index;

[0041]

[0042] Wherein, FDR is the fracture development response index, cD2M is the difference of wavelet coefficients, and BFDM is the box dimension difference.

[0043] The method for identifying shale fractures based on conventional logging data in the present invention starts from extracting and amplifying the logging responses of fractures and eliminating the influence of non-fracture factors, obtains the wavelet coefficients and box dimensions of the acoustic travel time curve and the natural gamma ray curve to obtain the formation heterogeneity information in the logging curves, obtains the difference of wavelet coefficients and the difference of box dimensions of the acoustic travel time curve and the natural gamma ray curve to highlight the vertical heterogeneity of the formation caused by fractures, comprehensively constructs a fracture development response index with the difference of wavelet coefficients and the difference of box dimensions, and finally identifies shale fractures according to the relatively high value of the fracture development response index, and can accurately and reliably identify the development of shale fractures through conventional logging data.

[0044] Based on conventional logging data, the present invention obtains the formation heterogeneity information in conventional logging curves through wavelet analysis and fractal theory, and then uniquely uses the differences in wavelet coefficients and box dimensions between the acoustic travel time curve and the natural gamma curve to highlight the vertical formation heterogeneity caused by fractures, achieving the purpose of shale fracture identification with new ideas and methods. Core and imaging logging are important and accurate means for fracture identification, but only a few wells have core and imaging logging data, making it difficult to apply widely. The present invention can be widely applied by using conventional logging data available in most wells to identify shale fractures in the absence of core and imaging logging data. In shale reservoirs, fractures are both migration channels and storage spaces, and are one of the main controlling factors of productivity. Therefore, the present invention is of great significance for the evaluation of shale reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 FIG. is a flow chart of a specific embodiment of the method for identifying shale fractures based on conventional logging data of the present invention;

[0046] Figure 2 FIG. is a logging curve and fracture identification result diagram of the shale section at 3160 - 3255 m in Well A in the embodiment of the present invention;

[0047] Figure 3 FIG. is a logging curve and fracture identification result diagram of the shale section at 3615 - 3675 m in Well B in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0049] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0050] The present invention relates to a method for identifying shale fractures based on conventional logging data. The following are several specific embodiments of applying the present invention.

[0051] Embodiment 1

[0052] As Figure 1 shown, Figure 1The figure is a flow chart of the method for identifying shale fractures based on conventional logging data according to the present invention. The method for identifying shale fractures based on conventional logging data includes:

[0053] 1) Analyze the logging curves in the shale section to obtain the acoustic travel time logging curve (AC) and the natural gamma logging curve (GR) for identifying shale fractures;

[0054] 2) Perform discrete wavelet transform on the acoustic travel time curve and the natural gamma curve, and decompose the logging curves into approximate wavelet coefficients and detailed wavelet coefficients. The discrete wavelet transform is defined as the sum of all original signals f(t) multiplied by the wavelet function ψ(t) after translation and scaling of the independent variable t. The formula is as follows:

[0055]

[0056] In the formula, W f (a, b) is the wavelet transform coefficient, f(t) is the original signal, ψ(t) is the wavelet function, and a and b are the scale factor and the displacement factor respectively;

[0057] Use the db11 wavelet function to perform three-layer wavelet decomposition on the AC curve and the GR curve. Mainly analyze the detailed wavelet coefficients cD2 of the second-layer decomposition. For the convenience of subsequent analysis, normalize cD2 in the shale section, and obtain the upper envelope of the normalized wavelet coefficients through Hilbert transform. Finally, obtain the wavelet coefficients of the acoustic travel time curve and the natural gamma curve.

[0058] Normalization processing of wavelet coefficients:

[0059]

[0060] In the formula, cD2N is the normalized wavelet coefficient, and cD2 max is the maximum value of the wavelet coefficient in the shale section.

[0061] Perform fractal dimension analysis on the acoustic travel time curve and the natural gamma curve to obtain the box dimensions of the acoustic travel time curve and the natural gamma curve. The box dimension is defined as:

[0062]

[0063] In the formula, r is the length of the small box, N(r) is the minimum number of boxes covering the logging curve, and D is the box dimension;

[0064] Use the sliding window method to calculate the box dimensions of the AC curve and the GR curve. The step size is 1 logging data point. Take the depth value corresponding to the middle logging data point of the window as the depth value of the box dimension. Mainly analyze the box dimension with a window length of 11 logging data points. Finally, obtain the box dimensions of the acoustic travel time curve and the natural gamma curve.

[0065] 3) Calculate the difference between the wavelet coefficients of the acoustic travel time curve and the wavelet coefficients of the natural gamma curve. To avoid negative values, take the absolute value of the obtained difference to get the wavelet coefficient difference. The formula is as follows:

[0066] cD2M = |c2AB - c2GB|

[0067] In the formula, cD2M is the wavelet coefficient difference, c2AB is the wavelet coefficient of the acoustic travel time curve, and c2GB is the wavelet coefficient of the natural gamma curve;

[0068] Calculate the difference between the box dimensions of the acoustic travel time curve and the box dimensions of the natural gamma curve. To avoid negative values, take the absolute value of the obtained difference to get the box dimension difference. The formula is as follows:

[0069] BFDM = |BFDA - BFDG|

[0070] In the formula, BFDM is the box dimension difference, BFDA is the box dimension of the acoustic travel time curve, and BFDG is the box dimension of the natural gamma curve;

[0071] 4) Synthesize the responses of the wavelet coefficient difference and the fractal dimension difference to the shale fractures, average the wavelet coefficient difference and the fractal dimension difference, and construct a fracture development response index;

[0072]

[0073] In the formula, FDR is the fracture development response index;

[0074] 5) Identify the shale fractures based on the relatively high values of the fracture development response index in the shale section.

[0075] In step 1), according to well logging theory, the acoustic travel time (AC) curve can not only reflect the vertical heterogeneity caused by the cyclic variation of formation lithology, but also reflect the local heterogeneity caused by secondary pores and fractures. The natural gamma (GR) curve mainly reflects the vertical heterogeneity caused by the cyclic variation of formation lithology.

[0076] In step 2), it can be considered that the wavelet coefficients and fractal dimensions of the AC curve reflect the vertical heterogeneity characteristics caused by the local variation of rock physical properties superimposed on the lithology variation. Although the wavelet coefficients and fractal dimensions of the GR curve have no obvious advantages in detecting the local physical property variation of formation rocks, they mainly reflect the vertical heterogeneity caused by the cyclic variation of formation lithology.

[0077] In step 3), the difference between the wavelet coefficient of the acoustic time difference curve and the wavelet coefficient of the natural gamma curve, and the difference between the box dimension of the acoustic time difference curve and the box dimension of the natural gamma curve are used to eliminate the vertical heterogeneity caused by the cyclic change of the formation lithology and highlight the vertical heterogeneity of the formation caused by the fracture.

[0078] Example 2

[0079] The method for identifying shale fractures based on conventional logging data in this embodiment takes the shale reservoir of Well A in a certain oil field as an example. The flow chart is shown in FIG. Figure 1 , including the following steps:

[0080] 1) Analyze the logging curves of the shale section and obtain the acoustic transit time logging curve (AC) and natural gamma logging curve (GR) for shale fracture identification.

[0081] 2) The db 11 wavelet function is used to perform three-layer wavelet decomposition on the AC curve and the GR curve. The detailed wavelet coefficient cD2 of the second layer of decomposition is mainly analyzed. To facilitate subsequent analysis, cD2 is normalized in the shale section, and the upper envelope of the normalized wavelet coefficient is obtained through Hilbert transform. Finally, the wavelet coefficients of the acoustic time difference curve and the natural gamma curve are obtained.

[0082] The sliding window method is used to obtain the box dimension of the AC curve and the GR curve. The step length is 1 logging data point. The corresponding depth of the logging data point in the middle of the window is taken as the depth value of the box dimension. The box dimension with a window length of 11 logging data points is mainly analyzed. Finally, the box dimension of the acoustic time difference curve and the natural gamma curve is obtained.

[0083] 3) Calculate the difference between the wavelet coefficients of the acoustic time difference curve and the wavelet coefficients of the natural gamma curve, and calculate the difference between the box dimension of the acoustic time difference curve and the box dimension of the natural gamma curve.

[0084] 4) The response of wavelet coefficient difference and fractal dimension difference to shale fractures is integrated, and the wavelet coefficient difference and fractal dimension difference are averaged to construct the fracture development response index.

[0085] 5) Based on the relatively high value of the fracture development response index in the shale section, shale fracture identification is performed.

[0086] The fracture occurrence and fracture parameters obtained from the imaging logging data are used to verify the shale fracture identification effect of this method. Figure 2 . Figure 2 The first track is the lithology curve, the second track is the depth track, the third track is the resistivity curve, the fourth track is the porosity curve, the fifth track is the fracture occurrence obtained by imaging logging, the sixth track is the fracture parameters obtained by imaging logging, the seventh and eighth tracks are the difference of wavelet coefficients and the difference of box dimension respectively, and the ninth track is the fracture development response index.

[0087] Well A is located in the lower part of the sag. The sedimentary thickness of the target layer is large and relatively stable. Shale is well developed, and the organic matter abundance is high. The interval of 3160 - 3255 m in Well A is the shale section. In this section, the GR is about 60 gAPI, the resistivity is about 10 ohm-m, and the density is about 2.55 g / cm 3 or so. A large number of fractures are developed, and the fracture angle is relatively high, about 60 degrees. Figure 2 In total, 4 fracture development sections are obtained from the imaging logging. And the fracture development response index shows obvious relative high values at the corresponding depths. The shale fractures can be identified through the fracture development response index.

[0088] Example 3

[0089] The method for identifying shale fractures based on conventional logging data in this example takes the shale reservoir of Well B in a certain oilfield as an example. The flow chart is shown in Figure 1 , and includes the following steps:

[0090] 1) Analyze the logging curves of the shale section to obtain the acoustic time difference logging curve (AC) and the natural gamma logging curve (GR) for shale fracture identification.

[0091] 2) Perform three-layer wavelet decomposition on the AC curve and the GR curve using the db11 wavelet function. Mainly analyze the detailed wavelet coefficients cD2 of the second-layer decomposition. For the convenience of subsequent analysis, normalize cD2 in the shale section, and obtain the upper envelope of the normalized wavelet coefficients through Hilbert transform. Finally, obtain the wavelet coefficients of the acoustic time difference curve and the natural gamma curve.

[0092] Use the sliding window method to calculate the box dimension of the AC curve and the GR curve. The step size is 1 logging data point. Take the depth value corresponding to the middle logging data point of the window as the depth value of the box dimension. Mainly analyze the box dimension with a window length of 11 logging data points. Finally, obtain the box dimensions of the acoustic time difference curve and the natural gamma curve.

[0093] 3) Calculate the difference between the wavelet coefficients of the acoustic time difference curve and the wavelet coefficients of the natural gamma curve, and calculate the difference between the box dimensions of the acoustic time difference curve and the box dimensions of the natural gamma curve.

[0094] 4) Synthesize the responses of the wavelet coefficient difference and the fractal dimension difference to the shale fractures, average the wavelet coefficient difference and the fractal dimension difference, and construct the fracture development response index.

[0095] 5) Identify the shale fractures based on the relative high values of the fracture development response index in the shale section.

[0096] The fracture occurrence and fracture parameters obtained from imaging logging data are used to verify the shale fracture identification effect of this method, as shown in Figure 3 . Figure 3 In the first track is the lithology curve, the second track is the depth track, the third track is the resistivity curve, the fourth track is the porosity curve, the fifth track is the fracture occurrence obtained from imaging logging, the sixth track is the fracture parameters obtained from imaging logging, the seventh and eighth tracks are the wavelet coefficient difference and the box dimension difference respectively, and the ninth track is the fracture development response index.

[0097] Different from Well A, Well B is located on the east slope of the sag zone and is developed with large sets of mudstone and oil shale. The interval of 3615 - 3675m in Well B is the shale section. In this interval, GR is about 75gAPI, resistivity is about 6 ohm-m, density is about 2.43g / cm 3 , and fractures are developed with a relatively low fracture angle of about 30 degrees. Figure 3 In , a total of 3 fracture development intervals are obtained from imaging logging, and obvious relative high values appear in the fracture development response index at the corresponding depths. The shale fractures can be identified through the fracture development response index.

[0098] When this invention is applied in different wells, although the specific reservoir parameters and fracture parameters are different, the fracture identification effect is relatively obvious. The method for identifying shale fractures based on conventional logging data is feasible and effective.

[0099] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0100] Except for the technical features described in the specification, they are all well-known technologies to those skilled in the art.

Claims

1. Method for identifying shale fractures based on conventional logging data, characterized in that, the method for identifying shale fractures based on conventional logging data includes: Step 1, obtain the acoustic time difference logging curve and natural gamma logging curve of the shale section; Step 2, perform discrete wavelet transform and box dimension calculation on the acoustic time difference curve and natural gamma curve to obtain wavelet coefficients and box dimensions; Step 3, calculate the difference between the wavelet coefficients of the acoustic time difference curve and the wavelet coefficients of the natural gamma curve to obtain the wavelet coefficient difference, and calculate the difference between the box dimensions of the acoustic time difference curve and the box dimensions of the natural gamma curve to obtain the box dimension difference; Step 4, average the wavelet coefficient difference and the fractal dimension difference to construct a fracture development response index; Step 5. Identify fractures in the shale section based on the relatively high values of the fracture development response index.

2. The method for identifying shale fractures based on conventional logging data according to claim 1, characterized in that, in Step 1, analyze the logging curves of the shale section, and obtain the acoustic time difference logging curve AC and the natural gamma logging curve GR for shale fracture identification.

3. The method for identifying shale fractures based on conventional logging data according to claim 2, characterized in that, in Step 1, from well logging theory, it can be known that the acoustic time difference AC curve can not only reflect the vertical heterogeneity caused by the lithologic cycle change of the formation, but also reflect the local heterogeneity caused by secondary pores and fractures; the natural gamma GR curve mainly reflects the vertical heterogeneity caused by the lithologic cycle change of the formation.

4. The method for identifying shale fractures based on conventional logging data according to claim 1, characterized in that, in Step 2, perform discrete wavelet transform on the acoustic time difference curve and the natural gamma curve, decompose the logging curve into approximate wavelet coefficients and detailed wavelet coefficients, and the discrete wavelet transform is defined as the sum of all original signals f(t) multiplied by the wavelet function ψ(t) after the translation and scaling of the independent variable t, and the calculation formula of the wavelet transform coefficient is as follows: where W f (a, b) are wavelet transform coefficients, f(t) is the original signal, ψ(t) is the wavelet function, and a and b are the scale factor and displacement factor, respectively.

5. The method for identifying shale fractures based on conventional logging data according to claim 4, characterized in that, in Step 2, use the db 11 wavelet function to perform three-layer wavelet decomposition on the AC curve and the GR curve, and mainly analyze the detailed wavelet coefficients cD2 of the second-layer decomposition according to the coincidence with the fracture development situation obtained from the imaging logging of the shale section. For the convenience of subsequent analysis, perform normalization processing on cD2 in the shale section, and obtain the upper envelope of the normalized wavelet coefficients through Hilbert transform, and finally obtain the wavelet coefficients of the acoustic time difference curve and the natural gamma curve.

6. The method for identifying shale fractures based on conventional logging data according to claim 5, characterized in that, in Step 2, wavelet coefficient normalization processing: where cD2N is the normalized wavelet coefficient, and cD2 max is the maximum value of the wavelet coefficient in the shale section.

7. The method for identifying shale fractures based on conventional logging data according to claim 1, characterized in that, in Step 2, perform fractal dimension analysis on the acoustic time difference curve and the natural gamma curve to obtain the box dimensions of the acoustic time difference curve and the natural gamma curve, and the box dimension is defined as: Wherein, r is the length of the small box, N(r) is the minimum number of boxes covering the logging curve, and D is the box dimension.

8. The method for identifying shale fractures based on conventional logging data according to claim 7, characterized in that in step 2, the box dimension of the AC curve and the GR curve is calculated by using the sliding window method, the step size is 1 logging data point, the depth value corresponding to the middle logging data point of the window is used as the depth value of the box dimension, and according to the conformity with the fracture development situation obtained from the imaging logging of the shale section, the box dimension with a window length of 11 logging data points is mainly analyzed, and finally the box dimensions of the acoustic time difference curve and the natural gamma curve are obtained.

9. The method for identifying shale fractures based on conventional logging data according to claim 1, characterized in that in step 2, the wavelet coefficients and fractal dimensions of the AC curve reflect the vertical inhomogeneity characteristics caused by the local change of rock physical properties superimposed on the lithology change; although the wavelet coefficients and fractal dimensions of the GR curve have no obvious advantages in detecting the local physical property changes of formation rocks, they mainly reflect the vertical inhomogeneity caused by the cyclic change of formation lithology.

10. The method for identifying shale fractures based on conventional logging data according to claim 1, characterized in that in step 3, the difference between the wavelet coefficient of the acoustic time difference curve and the wavelet coefficient of the natural gamma curve is calculated to obtain the wavelet coefficient difference, and the formula is as follows: cD2M = |c2AB - c2GB| Wherein, cD2M is the wavelet coefficient difference, c2AB is the wavelet coefficient of the acoustic time difference curve, and c2GB is the wavelet coefficient of the natural gamma curve.

11. The method for identifying shale fractures based on conventional logging data according to claim 1, characterized in that in step 3, the difference between the box dimension of the acoustic time difference curve and the box dimension of the natural gamma curve is calculated to obtain the box dimension difference, and the formula is as follows: BFDM = |BFDA - BFDG| Wherein, BFDM is the box dimension difference, BFDA is the box dimension of the acoustic time difference curve, and BFDG is the box dimension of the natural gamma curve.

12. The method for identifying shale fractures based on conventional logging data according to claim 1, characterized in that in step 3, the difference between the wavelet coefficient of the acoustic time difference curve and the wavelet coefficient of the natural gamma curve, and the difference between the box dimension of the acoustic time difference curve and the box dimension of the natural gamma curve are used to eliminate the vertical inhomogeneity caused by the cyclic change of formation lithology and highlight the vertical inhomogeneity of the formation caused by fractures.

13. The method for identifying shale fractures based on conventional logging data according to claim 1, characterized in that in step 4, the responses of the wavelet coefficient difference and the fractal dimension difference to shale fractures are integrated, and the wavelet coefficient difference and the fractal dimension difference are averaged to construct a fracture development response index; Wherein, FDR is the fracture development response index, cD2M is the wavelet coefficient difference, and BFDM is the box dimension difference.

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