CN115144894A - Shale fracture prediction method and device - Google Patents
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Abstract
The application relates to a shale fracture prediction method and device, and belongs to the technical field of oil and gas exploration. According to the technical scheme provided by the embodiment of the application, the first pre-stack seismic data needs to be acquired, the post-stack seismic data of at least three azimuths are acquired through division and superposition processing of the first pre-stack seismic data, each post-stack seismic data corresponds to one central azimuth angle, namely, the post-stack seismic data contains seismic information with azimuth angles, so that azimuth information is provided for acquiring subsequent crack development information, and the subsequently acquired crack development information has high accuracy.
Description
Technical Field
The application relates to the technical field of oil and gas exploration, in particular to a shale fracture prediction method and device.
Background
In the exploitation construction of shale gas, fractures of different scales have different influences on the preservation, drilling, hydraulic fracturing, yield and the like of the shale gas. Most of these effects are negative: the large fault can break through the cover layer of the upper area to form a passage for shale gas to dissipate, so that the shale gas reservoir is damaged or the gas content is reduced; drilling fluid leakage may occur or target entering difficulty is caused when moderate fault is encountered during drilling; small faults encountered in drilling may cause the well trajectory to deviate from the casing, resulting in inefficient footage. The effect of some cracks is also positive: the small-scale natural fracture network is a storage space and a seepage channel, is a necessary way for shale gas to flow into a well bottom from matrix pores, plays an important role in oil gas seepage, and is easy to form relatively high yield as long as the shale reservoir with low permeability is matched with the oil gas seepage. Therefore, it is significant to finely predict fractures of different scales, especially fault and fracture development zones of small scale.
As one of effective methods for predicting fracture of current shale, the basic idea of the attribute extraction technology based on the gray level co-occurrence matrix is as follows: by counting the matrix of which the gray levels of the adjacent pixels in a local area or a whole area or two pixels in a certain distance in the image show a certain relation, the comprehensive information of the image on the direction, the change amplitude, the change speed and the adjacent interval length can be reflected. The technique has strong sensitivity to seismic waveform response differences caused by structural, lithological and physical changes, and is very effective in predicting faults and special lithologies, judging the spatial position and plane combination relation of the faults and the like. However, this method is currently processed for conventional post-stack seismic data, and although larger scale fractures can be predicted, it is difficult to accurately predict smaller scale, more voluminous, small or micro-fracture systems.
Disclosure of Invention
The embodiment of the application provides a shale fracture prediction method and device, which can enable the acquired fracture development information to have higher accuracy. The technical scheme is as follows:
in one aspect, a shale fracture prediction method is provided, and the method includes:
acquiring first pre-stack seismic data of a shale reservoir;
dividing the first pre-stack seismic data into second pre-stack seismic data of at least three azimuths based on the fracture trend of the shale reservoir;
partially stacking the second pre-stack seismic data of at least three azimuths to obtain post-stack seismic data of at least three azimuths, wherein each post-stack seismic data corresponds to a central azimuth;
converting the stacked seismic data into an image gray value;
constructing a seismic processing element by taking a preset pixel as a center based on the graph gray value;
establishing a gray level co-occurrence matrix based on the seismic processing element;
based on the gray level co-occurrence matrix, obtaining information corresponding to the attribute of the gray level co-occurrence matrix of the shale reservoir, wherein the attribute of the gray level co-occurrence matrix comprises: homogeneity, contrast, entropy and angular second moment;
and acquiring fracture development information of the shale fracture based on the information corresponding to the gray level co-occurrence matrix attribute.
In one possible implementation, before the partially stacking the second pre-stack seismic data for at least three azimuths, the method further comprises:
the second pre-stack seismic data for at least three azimuths is sorted based on the corresponding azimuths.
In one possible implementation, the post-stack seismic data is converted to image gray scale values using relational 1;
in the formula, x and y respectively represent a seismic data line number and a track number;
z represents a sampling point in the time or depth direction;
SeisGray (x, y, z) is seismic data after the original seismic data are transformed to the specified gray level;
seisamp (x, y, z) is the amplitude value of the original seismic data;
min { Seisamp ((i, j) } is the minimum value of the amplitude value of the seismic data;
max { Seisamp (x, y, z) } is the maximum value of the amplitude value of the seismic data;
GrayLevel is the gray level number after seismic data conversion;
[] Indicating taking an integer.
In one possible implementation, the first pre-stack seismic data further includes offset information.
In one possible implementation, the constructing the seismic processing primitive includes:
and constructing a seismic processing area element.
In a possible implementation manner, the obtaining fracture development information of the shale fracture based on the information corresponding to the gray level co-occurrence matrix attribute includes:
obtaining the gray level co-occurrence matrix attribute with the highest crack sensitivity in the homogeneity, the contrast, the entropy and the angular second moment as a target attribute;
and acquiring fracture development information of the shale fracture based on the information corresponding to the target attribute.
In one aspect, a shale fracture prediction device is provided, and the device includes:
the data acquisition module is used for acquiring first pre-stack seismic data of the shale reservoir;
the data processing module is used for dividing the first pre-stack seismic data into second pre-stack seismic data of at least three azimuths based on the fracture trend of the shale reservoir;
the data processing module is also used for carrying out partial superposition on the second pre-stack seismic data of at least three azimuths to obtain post-stack seismic data of at least three azimuths, and each post-stack seismic data corresponds to a central azimuth angle;
the gray value acquisition module is used for converting the stacked seismic data into an image gray value;
the gray value processing module is used for constructing a seismic processing element by taking a preset pixel as a center based on the graph gray value;
the matrix acquisition module is used for establishing a gray level co-occurrence matrix based on the seismic processing element;
the information acquisition module is used for acquiring information corresponding to the gray level co-occurrence matrix attribute of the shale reservoir based on the gray level co-occurrence matrix, wherein the gray level co-occurrence matrix attribute comprises: homogeneity, contrast, entropy and angular second moment;
and the information acquisition module is also used for acquiring the fracture development information of the shale fracture based on the information corresponding to the gray level co-occurrence matrix attribute.
In one possible implementation, the data processing module is further configured to:
the second pre-stack seismic data for at least three azimuths is sorted based on the corresponding azimuths.
In one possible implementation, the post-stack seismic data is converted to image gray scale values using relational 1;
in the formula, x and y respectively represent seismic data line numbers and track numbers;
z represents a sampling point in the time or depth direction;
SeisGray (x, y, z) is seismic data after the original seismic data are transformed to the specified gray level;
seisamp (x, y, z) is the amplitude value of the original seismic data;
min { Seisamp ((i, j) } is the minimum value of the amplitude value of the seismic data;
max { Seisamp (x, y, z) } is the maximum value of the amplitude value of the seismic data;
GrayLevel is the gray level after seismic data conversion;
[] Indicating taking an integer.
In one possible implementation, the first pre-stack seismic data further includes offset information.
In one possible implementation, the gray value processing module is configured to:
and constructing a seismic processing area element.
In one possible implementation, the information obtaining module is configured to:
obtaining the gray level co-occurrence matrix attribute with the highest crack sensitivity in the homogeneity degree, the contrast, the entropy and the angle second moment as a target attribute;
and acquiring fracture development information of the shale fracture based on the information corresponding to the target attribute.
According to the technical scheme provided by the embodiment of the application, the first pre-stack seismic data needs to be acquired, the post-stack seismic data of at least three azimuths are acquired through division and superposition processing of the first pre-stack seismic data, each post-stack seismic data corresponds to one central azimuth angle, namely, the post-stack seismic data contains seismic information with azimuth angles, so that azimuth information is provided for acquiring subsequent crack development information, and the subsequently acquired crack development information has high accuracy.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a shale fracture prediction method provided by an embodiment of the present application;
FIG. 2 is a flow chart of a shale fracture prediction method provided by an embodiment of the present application;
FIG. 3 is a schematic diagram of seismic signal excitation reception provided by an embodiment of the present application;
FIG. 4 is a schematic diagram of seismic signal offset provided by an embodiment of the present application;
FIG. 5 is a schematic diagram of a seismic azimuth gather provided by an embodiment of the present application;
FIG. 6 is a rose diagram of a seismic acquisition statistic provided by an embodiment of the present application;
FIG. 7 is a schematic illustration of a three-dimensional post-stack seismic data volume provided by an embodiment of the present application;
FIG. 8 is a block diagram of a partially overlapped azimuth gather according to an embodiment of the present application;
FIG. 9 is a plan view of an entropy property crack prediction based on pre-stack gray level co-occurrence matrix according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of a shale fracture prediction apparatus provided in an embodiment of the present application;
fig. 11 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a shale fracture prediction method according to an embodiment of the present application, please refer to fig. 1, which may be executed by a computer device, and the method includes:
101. first pre-stack seismic data of a shale reservoir is obtained.
102. And dividing the first pre-stack seismic data into second pre-stack seismic data of at least three azimuths based on the fracture strike of the shale reservoir.
103. And performing partial stacking on the second pre-stack seismic data of at least three azimuths to obtain post-stack seismic data of at least three azimuths, wherein each post-stack seismic data corresponds to a central azimuth.
104. The post-stack seismic data is converted to image gray scale values.
105. And constructing a seismic processing element by taking a preset pixel as a center based on the graph gray value.
106. Based on the seismic processing element, a gray level co-occurrence matrix is established.
107. And acquiring information corresponding to the attribute of the gray level co-occurrence matrix of the shale reservoir based on the gray level co-occurrence matrix.
Wherein the gray level co-occurrence matrix attribute comprises: homogeneity, contrast, entropy and angular second moment.
108. And acquiring fracture development information of the shale fracture based on the information corresponding to the gray level co-occurrence matrix attribute.
According to the shale fracture prediction method provided by the embodiment of the application, the first pre-stack seismic data is required to be acquired, the first pre-stack seismic data is divided and stacked to obtain the post-stack seismic data in at least three directions, each post-stack seismic data corresponds to a central azimuth angle, that is, the post-stack seismic data contains seismic information with azimuth angles, so that azimuth information is provided for acquiring subsequent fracture development information, and the subsequently acquired fracture development information has high accuracy.
In one possible implementation, before the partially stacking the second pre-stack seismic data for at least three azimuths, the method further comprises:
the second pre-stack seismic data for at least three azimuths is sorted based on the corresponding azimuths.
In one possible implementation, the post-stack seismic data is converted to image gray scale values using relational 1;
in the formula, x and y respectively represent seismic data line numbers and track numbers;
z represents a sampling point in the time or depth direction;
SeisGray (x, y, z) is seismic data after the original seismic data are transformed to the specified gray level;
seisamp (x, y, z) is the amplitude value of the original seismic data;
min { Seisamp ((i, j) } is the minimum value of the amplitude value of the seismic data;
max { Seisamp (x, y, z) } is the maximum value of the amplitude value of the seismic data;
GrayLevel is the gray level number after seismic data conversion;
[] Indicating taking an integer.
In one possible implementation, the first pre-stack seismic data further includes offset information.
In one possible implementation, the constructing the seismic processing primitive includes:
and constructing a seismic processing area element.
In a possible implementation manner, the obtaining fracture development information of the shale fracture based on the information corresponding to the gray level co-occurrence matrix attribute includes:
obtaining the gray level co-occurrence matrix attribute with the highest crack sensitivity in the homogeneity degree, the contrast, the entropy and the angle second moment as a target attribute;
and acquiring fracture development information of the shale fracture based on the information corresponding to the target attribute.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
Fig. 2 is a flowchart of a shale fracture prediction method according to an embodiment of the present application, please refer to fig. 2, which may be executed by a computer device, and the method includes:
201. first pre-stack seismic data of a shale reservoir is obtained.
In this embodiment, the gray level co-occurrence matrix attribute calculation may be performed along a certain seismic horizon, may also be performed along a certain section and a certain fixed time, and may also perform gray level co-occurrence matrix attribute calculation on the entire data volume, which is not limited in this embodiment.
For shale reservoirs, seismic waves are recorded at each observation point, and the seismic waves must pass through three basic links of a geophone, an amplification system and a recording system, and the three basic links are connected together and are generally called a seismic trace. Seismic traces are composed of energy over a range of frequencies. The fourier transform can decompose the signal into different sine waves whose amplitude and phase vary with frequency. The seismic trace data (profile) is composed of a series of traces whose images are waveforms, each trace being a one-dimensional signal trace. And in the step, the first pre-stack seismic data is obtained by covering the common center point seismic data for multiple times.
The above-mentioned acquisition process of the first pre-stack seismic data may be: the method comprises the steps of obtaining original acquisition data through field exploration and acquisition, and then obtaining pre-stack seismic data through seismic data processing, wherein the seismic data processing comprises the steps of spherical divergence correction, Q compensation for stratum absorption, amplitude processing, wavelet deconvolution, earth surface consistency static correction, velocity analysis, dynamic correction, residual static correction and the like.
The measurement principle of the first pre-stack seismic data is described below:
FIG. 3 is a schematic diagram of the excitation and reception of seismic signals according to an embodiment of the present application, and referring to FIG. 3, based on the subsurface reflection point, at point A, the excitation may be performed, and at point B, the correlation signal may be received; similarly, when the excitation is carried out at the point C, the related signal can be received at the point D; excitation at point E, and receiving related signals at point F; excited at point G and a correlation signal can be received at point H. In the excitation and acquisition processes, a reference direction may be set, and an angle between a connecting line of the excitation point and the detector point and the reference direction is defined as an azimuth angle, for example, a due north direction may be set as the reference direction, and an angle between a connecting line of the excitation point and the detector point and the due north direction is defined as the azimuth angle, so as to obtain a plurality of signals capable of reflecting the reflection point R.
In one possible implementation, the first pre-stack seismic data further includes offset information. FIG. 4 is a schematic diagram of seismic signal offset provided by an embodiment of the present application, please refer to FIG. 4, for a reflection point R, at point A, a correlation signal can be received at point B; when the A 'point is excited, the B' point can receive the related signal. The distances indicated by the parenthesis in fig. 4 are the offset distances.
The seismic channels with different directions and different offset distances can form a gather by combining the seismic channels. Referring to fig. 5, fig. 5 is a schematic diagram of a seismic azimuth gather provided in the embodiments of the present application. For a three-dimensional seismic gather, each gather is a four-dimensional array in which one dimension represents azimuth information.
202. And dividing the first pre-stack seismic data into second pre-stack seismic data of at least three azimuths based on the fracture strike of the shale reservoir.
Because of the differences in the signals excited and received at different orientations when a fracture develops in the subsurface, such differences are usually manifested in the amplitude, frequency, etc. of the signals. In other words, without developing a fracture, the signals received at each azimuth are, in theory, substantially the same, and the second pre-stack seismic data is used to predict the fracture for subsequent steps using this difference.
In the step, the fracture trend can be obtained by methods such as comprehensive post-stack coherence and the like according to a statistical coverage frequency-azimuth-offset rose diagram, and the gather is divided into more than 3 angles based on the fracture trend, for example, the gather is divided according to six azimuths of 0-30 degrees, 30-60 degrees, 60-90 degrees, 90-120 degrees, 120-150 degrees and 150-180 degrees, so that second pre-stack seismic data is obtained.
Specifically, referring to fig. 6, fig. 6 is a rose diagram of seismic acquisition statistics provided by the embodiment of the present application, from which can be obtained: and 3, seismic coverage times in any azimuth and any offset range, and further giving a partial stacking reasonable offset range and azimuth range.
203. The second pre-stack seismic data for at least three azimuths is sorted based on the corresponding azimuths.
In the step, the pre-stack seismic data of the adjacent directions are combined together through sorting, so that the subsequent stacking is convenient according to the directions. For example, the corresponding data are sorted in ascending order starting from 0 ° up to 180 °.
204. And carrying out partial superposition on the second pre-stack seismic gather in at least three directions to obtain post-stack seismic data in at least three directions, wherein each post-stack seismic data corresponds to a central azimuth angle.
In this step, seismic traces with the same central azimuth angle under each trace in the second prestack seismic trace gather may be extracted, and then, partial stacking may be performed according to six azimuths of 0-30 °, 30 ° -60 °, 60 ° -90 °, 90 ° -120 °, 120 ° -150 °, 150 ° -180 °, to obtain post-stack seismic data with 6 azimuths having central azimuths of 15 °, 45 °, 75 °, 105 °, 135 °, and 165 °, where the structure of the post-stack seismic data may refer to fig. 7, fig. 7 is a schematic diagram of a three-dimensional post-stack seismic data volume provided by an embodiment of the present application, and the post-stack seismic data is a three-dimensional array (x, y, z), where x represents a line number, y represents a trace number, and z represents a value at a time or a certain depth. For example, the range outlined in (0, 0) of FIG. 7, representing the first trace of seismic data read by the computer, is a discrete value of length N. Accordingly, a schematic of a partially overlapped azimuth gather as shown in FIG. 8 may be obtained.
The steps have the following functions: during stacking, azimuth angle characteristics contained in pre-stack seismic data are fully utilized, and anisotropy of a shale reservoir is guaranteed to be reflected in the process of detecting micro fracture, so that accuracy of shale fracture prediction results is improved.
205. The post-stack seismic data is converted to image gray scale values.
The shale reservoir is a continuous solid reservoir, so that points in the post-stack seismic data have continuity in space, physical properties of the reservoir are reflected through images, and the positions and sizes of cracks can be visually represented through the steps of converting the post-stack seismic data into image gray values and subsequently drawing corresponding images.
A two-dimensional seismic digital image can be a two-dimensional array, the element values of the array are called gray values, the gray values are generally quantized into different gray levels, and 4096 (12 bits) gray levels can be taken at most for ensuring the image quality.
In one possible implementation, the post-stack seismic data is converted to image gray scale values using relational 1;
in the formula, x and y respectively represent a seismic data line number and a track number;
z represents a sampling point in the time or depth direction;
SeisGray (x, y, z) is seismic data after the original seismic data are transformed to the specified gray level;
seisamp (x, y, z) is the amplitude value of the original seismic data;
min { Seisamp ((i, j) } is the minimum value of the amplitude value of the seismic data;
max { Seisamp (x, y, z) } is the maximum value of the amplitude value of the seismic data;
GrayLevel is the gray level number after seismic data conversion;
[] Indicating taking an integer.
206. And constructing a seismic processing element by taking a preset pixel as a center based on the graph gray value.
In this step, the dimension of the seismic processing element, for example, a trace element, a plane element, or a volume element, may be selected according to the dimension of the image gray value and the dimension required for subsequently establishing the gray co-occurrence matrix, which is not limited in this embodiment.
In a possible implementation manner, for two-dimensional seismic data, a seismic processing area element may be constructed based on the graph gray value with a preset pixel as a center, that is, a (2m + 1) × (2n + 1) seismic surface element is established with one pixel as a center, where m and n are positive integers.
In a possible implementation manner, for a three-dimensional seismic data volume, if a gray level co-occurrence matrix plane attribute along a certain layer or a certain surface is calculated, a surface element manner is adopted, if a gray level co-occurrence matrix body attribute is to be calculated, a volume element of (2m + 1) × (2n + 1) × (2p + 1) may be adopted as a reference window, and m, n, p represent the number of radius of sampling points along the line, track and sign direction.
207. Based on the seismic processing element, a gray level co-occurrence matrix is established.
In this step, the gray level co-occurrence matrix is calculated for the seismic processing elements of the converted gray levels. Since the values involved in the calculation are grayscaled seismic amplitude data, the resulting gray level co-occurrence matrix is commonly referred to as an amplitude co-occurrence matrix.
The gray level co-occurrence matrix is a matrix that counts the probability of occurrence of two pixels having a certain spatial relationship. The element values in the matrix represent joint probabilities (joint probabilities) between the gray levels. For example, for an image with G gray levels, the probability, also called frequency, that a gray value b appears starting from gray a is counted and expressed as P (a, b, d, θ) given the spatial distance d and the direction.
In relation 2:
x, y are pixel coordinates in the image;
a,b＝0,1,2,…,G；
m and N are the row and column numbers of the image respectively.
Writing the processed P (a, b, d, theta) into a gray level co-occurrence matrix form:
in addition, the gray level co-occurrence matrix can be normalized, that is, the frequency in the gray level co-occurrence matrix is converted into probability, the method is to divide each element value by the sum of all element values in the matrix, and the formula is as follows:
in the step, the attribute extraction technology based on the gray level co-occurrence matrix is derived from image processing, and the recognition of the internal structure and the structural change of the object is realized through the statistics of the change rule of the pixels. Specifically, the method is a matrix in which the gray levels of adjacent pixels or two pixels in a certain distance in a local area or a whole area in a statistical image show a certain relationship. Through statistics of the relation, comprehensive information of the image in the direction, the change amplitude, the change speed and the adjacent interval length can be reflected. The technique has strong sensitivity to seismic waveform response differences caused by structural, lithological and physical changes, and is very effective in identifying faults and special lithological bodies, distinguishing spatial positions and plane combination relations of the faults and the like. In the embodiment, the calculation based on the gray level co-occurrence matrix attribute is performed on the pre-stack seismic data, and the offset distance information and the azimuth angle information are embodied, so that the theoretical basis and the basis for detecting the micro fracture by anisotropy are increased.
208. And acquiring information corresponding to the attribute of the gray level co-occurrence matrix of the shale reservoir based on the gray level co-occurrence matrix.
Wherein the gray level co-occurrence matrix attribute comprises: homogeneity, contrast, entropy and angular second moment.
In a shale reservoir, when a crack develops, reflected wave signals between the same seismic channels at different azimuth angles change to different degrees, and abnormal responses to different degrees exist in the calculated gray level co-occurrence matrix attributes, namely homogeneity, contrast, entropy, angle second moment/energy and the like, and in one possible implementation mode, the gray level co-occurrence matrix attributes comprise: homogeneity, contrast, entropy and angular second moment.
Wherein, the homogeneity is an index for measuring the smoothness of the image. If the image is very uniform locally due to lack of variation between different regions, the homogeneity is high, and can be obtained by the following relation 5.
The Contrast (Contrast) can reflect the degree of sharpness of the image and the depth of texture grooves. The deeper the image groove, the greater the contrast and the clearer the visual effect; on the other hand, the contrast is small, the groove is shallow, and the visual effect is blurred, which can be obtained by the following relational expression 6.
Entropy (Entropy) is a measure of the amount of information an image has, reflecting the magnitude of the disorder and complexity of the texture in the image. If the texture in an image is disordered, the entropy value is large, and can be obtained by the following relational expression 7.
The angular second moment/Energy (Energy) is the sum of squares of the gray level co-occurrence matrix elements, and therefore also becomes Energy, and can reflect the degree of uniformity of the gray level distribution of the image and the degree of thickness of the texture, and can be obtained by the following relational expression 8.
209. And obtaining the gray level co-occurrence matrix attribute with the highest crack sensitivity in the homogeneity, the contrast, the entropy and the angular second moment as a target attribute.
In the step, aiming at abnormal responses of different gray level co-occurrence matrix attributes to different degrees of cracks, the gray level co-occurrence matrix attribute with the highest sensitivity is obtained as the target attribute, so that the calculation workload is reduced on the basis of ensuring the prediction accuracy. Specifically, the abnormal response means: for the same sampling point, the seismic data of different directions are different, when the gray level co-occurrence matrix attribute is calculated, some local values are large, some local values are small, and the difference of gray values and the difference of depths are reflected on an image, please refer to fig. 9, fig. 9 is an entropy attribute crack prediction plane diagram based on the pre-stack gray level co-occurrence matrix provided by the embodiment of the application, a black linear area in fig. 9 can be regarded as a crack development place, a black part is regarded as a crack high probability development area, and the prediction is also proved through actual drilling, so that the prediction has higher accuracy in shale natural crack prediction, especially in small-scale natural crack prediction with a fault distance of less than 20 m.
210. And acquiring fracture development information of the shale fracture based on the information corresponding to the target attribute.
The fracture development information may be in the form of data, or may be a picture corresponding to the data, for example, please refer to fig. 9, where in fig. 9, L205 is a well name, and a straight line in the figure is a projection of a well track of a horizontal section, and after the shale gas development well enters a target layer instead of a straight well, a kilometer is drilled in a horizontal well in an interval with the best gas content, and then fracturing is performed, that is, the well shape is approximately L-shaped, so that the line is a projection of the horizontal section of the target layer; two arrows respectively indicate the positions of the fractures (or called minor faults) calculated based on the method provided by the embodiment, and the positions of the two fractures are verified and matched with each other in the drilling process.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
According to the shale fracture prediction method provided by the embodiment of the application, the first pre-stack seismic data is required to be acquired, the first pre-stack seismic data is divided and stacked to obtain the post-stack seismic data in at least three directions, each post-stack seismic data corresponds to a central azimuth angle, that is, the post-stack seismic data contains seismic information with azimuth angles, so that azimuth information is provided for acquiring subsequent fracture development information, and the subsequently acquired fracture development information has high accuracy.
That is to say, the method takes the pre-stack seismic data as the basic input, utilizes the characteristic that the amplitude of the wide-azimuth three-dimensional seismic data changes along with the azimuth to calculate the gray level co-occurrence matrix attribute, is finally applied to crack prediction, has higher precision in the depiction of small-scale natural cracks, can predict the cracks with relatively small scale, reduces the multi-solution of the crack prediction to a certain extent, and provides powerful support for well location deployment, well track design and fracturing construction.
Fig. 10 is a schematic structural diagram of a shale fracture prediction apparatus according to an embodiment of the present application, please refer to fig. 10, where the apparatus includes:
the data acquisition module 1001 is used for acquiring first pre-stack seismic data of a shale reservoir;
the data processing module 1002 is configured to divide the first pre-stack seismic data into second pre-stack seismic data of at least three azimuths based on fracture strike of the shale reservoir;
the data processing module 1002 is further configured to perform partial stacking on the second pre-stack seismic data in at least three azimuths to obtain post-stack seismic data in at least three azimuths, where each post-stack seismic data corresponds to a central azimuth;
a gray value obtaining module 1003, configured to convert the stacked seismic data into an image gray value;
the gray value processing module 1004 is used for constructing a seismic processing element by taking a preset pixel as a center based on the graph gray value;
a matrix obtaining module 1005, configured to establish a gray level co-occurrence matrix based on the seismic processing primitive;
an information obtaining module 1006, configured to obtain information corresponding to the gray level co-occurrence matrix attribute of the shale reservoir based on the gray level co-occurrence matrix;
the information obtaining module 1006 is further configured to obtain fracture development information of the shale fracture based on information corresponding to the gray level co-occurrence matrix attribute.
In one possible implementation, the post-stack seismic data is converted to image gray scale values using relational 1;
in the formula, x and y respectively represent a seismic data line number and a track number;
z represents a sampling point in the time or depth direction;
SeisGray (x, y, z) is seismic data after the original seismic data are transformed to the specified gray level;
seisamp (x, y, z) is the amplitude value of the original seismic data;
min { Seisamp ((i, j) } is the minimum value of the amplitude value of the seismic data;
max { Seisamp (x, y, z) } is the maximum value of the amplitude value of the seismic data;
GrayLevel is the gray level number after seismic data conversion;
[] Indicating taking an integer.
In one possible implementation, the first pre-stack seismic data further includes offset information.
In one possible implementation, the gray value processing module 1004 is configured to:
and constructing a seismic processing area element.
In one possible implementation, the gray level co-occurrence matrix attribute includes: homogeneity, contrast, entropy and angular second moment.
In one possible implementation, the information obtaining module 1006 is configured to:
obtaining the gray level co-occurrence matrix attribute with the highest crack sensitivity in the homogeneity, the contrast, the entropy and the angular second moment as a target attribute;
and acquiring fracture development information of the shale fracture based on the information corresponding to the target attribute.
It should be noted that: in the shale fracture prediction device provided in the above embodiment, when shale fracture prediction is performed, only the division of the above functional modules is taken as an example, and in practical application, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the equipment is divided into different functional modules, so as to complete all or part of the above described functions. In addition, the shale fracture prediction device provided by the above embodiment and the shale fracture prediction method embodiment belong to the same concept, and the specific implementation process is described in the method embodiment, and is not described herein again.
The shale fracture prediction device provided by the embodiment of the application needs to acquire first pre-stack seismic data, and obtains post-stack seismic data in at least three azimuths by dividing and stacking the first pre-stack seismic data, wherein each post-stack seismic data corresponds to a central azimuth angle, that is, the post-stack seismic data contains seismic information with azimuth angles, so that azimuth information is provided for acquiring subsequent fracture development information, and the subsequently acquired fracture development information has higher accuracy.
Fig. 11 is a schematic structural diagram of a computer device according to an embodiment of the present application, please refer to fig. 11, where the computer device 1100 may generate a relatively large difference due to different configurations or performances, and may include one or more computer devices (CPUs) 1101 and one or more memories 1102, where the memory 1102 stores at least one instruction, and the at least one instruction is loaded and executed by the computer device 1101 to implement the methods provided by the shale fracture prediction method embodiments. Certainly, the computer device may further have a wired or wireless network interface, a keyboard, an input/output interface, and other components to facilitate input and output, and the computer device may further include other components for implementing functions of the device, which are not described herein again.
The computer device may be an independent physical computer device, a computer device cluster or a distributed system formed by a plurality of physical computer devices, or a cloud computer device providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, middleware service, a domain name service, a security service, a CDN, a big data and artificial intelligence platform, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the computer device may be directly or indirectly connected through a wired or wireless communication manner, and the application is not limited herein.
In an example embodiment, a computer-readable storage medium, such as a memory including instructions executable by a computer device in a terminal, to perform the shale fracture prediction method of the above embodiments is also provided. For example, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk.
The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A shale fracture prediction method is characterized by comprising the following steps:
acquiring first pre-stack seismic data of a shale reservoir;
dividing the first pre-stack seismic data into second pre-stack seismic data of at least three azimuths based on the fracture trend of the shale reservoir;
partially stacking the second pre-stack seismic data of at least three azimuths to obtain post-stack seismic data of at least three azimuths, wherein each post-stack seismic data corresponds to a central azimuth;
converting the post-stack seismic data into an image gray value;
constructing a seismic processing element by taking a preset pixel as a center based on the graph gray value;
establishing a gray level co-occurrence matrix based on the seismic processing elements;
based on the gray level co-occurrence matrix, obtaining information corresponding to the attribute of the gray level co-occurrence matrix of the shale reservoir, wherein the attribute of the gray level co-occurrence matrix comprises: homogeneity, contrast, entropy and angular second moment;
and acquiring fracture development information of the shale fracture based on the information corresponding to the gray level co-occurrence matrix attribute.
2. The method of claim 1, wherein prior to partially stacking the second pre-stack seismic data for at least three azimuths, the method further comprises:
ordering the second pre-stack seismic data for at least three azimuths based on the corresponding azimuths.
3. The method of claim 1, wherein the post-stack seismic data is converted to image gray scale values using relation 1;
in the formula, x and y respectively represent seismic data line numbers and track numbers;
z represents a sampling point in the time or depth direction;
SeisGray (x, y, z) is seismic data after the original seismic data are transformed to the specified gray level;
seisamp (x, y, z) is the amplitude value of the original seismic data;
min { Seisamp ((i, j) } is the minimum value of the amplitude value of the seismic data;
max { Seisamp (x, y, z) } is the maximum value of the amplitude value of the seismic data;
GrayLevel is the gray level after seismic data conversion;
[] Indicating taking an integer.
4. The method of claim 1, wherein the first pre-stack seismic data further comprises offset information.
5. The method according to claim 1, wherein the obtaining fracture development information of the shale fracture based on the information corresponding to the gray level co-occurrence matrix attribute comprises:
obtaining the gray level co-occurrence matrix attribute with the highest crack sensitivity in the homogeneity, the contrast, the entropy and the angular second moment as a target attribute;
and acquiring fracture development information of the shale fracture based on the information corresponding to the target attribute.
6. A shale fracture prediction apparatus, the apparatus comprising:
the data acquisition module is used for acquiring first pre-stack seismic data of the shale reservoir;
the data processing module is used for dividing the first pre-stack seismic data into second pre-stack seismic data of at least three azimuths based on the fracture trend of the shale reservoir;
the data processing module is further used for partially stacking the second pre-stack seismic data of at least three azimuths to obtain post-stack seismic data of at least three azimuths, and each post-stack seismic data corresponds to a central azimuth;
the gray value acquisition module is used for converting the stacked seismic data into an image gray value;
the gray value processing module is used for constructing a seismic processing element by taking a preset pixel as a center based on the graph gray value;
the matrix acquisition module is used for establishing a gray level co-occurrence matrix based on the seismic processing element;
an information obtaining module, configured to obtain information corresponding to a gray level co-occurrence matrix attribute of the shale reservoir based on the gray level co-occurrence matrix, where the gray level co-occurrence matrix attribute includes: homogeneity, contrast, entropy and angular second moment;
and the information acquisition module is further used for acquiring fracture development information of the shale fracture based on the information corresponding to the gray level co-occurrence matrix attribute.
7. The apparatus of claim 6, wherein the data processing module is further configured to:
ranking the second pre-stack seismic data for at least three azimuths based on the corresponding azimuths.
8. The apparatus of claim 6, wherein the post-stack seismic data is converted to image gray scale values using relation 1;
in the formula, x and y respectively represent seismic data line numbers and track numbers;
z represents a sampling point in the time or depth direction;
SeisGray (x, y, z) is seismic data after the original seismic data are transformed to the specified gray level;
seisamp (x, y, z) is the amplitude value of the original seismic data;
min { Seisamp ((i, j) } is the minimum value of the amplitude value of the seismic data;
max { Seisamp (x, y, z) } is the maximum value of the amplitude value of the seismic data;
GrayLevel is the gray level number after seismic data conversion;
[] Indicating taking an integer.
9. The apparatus of claim 6, wherein the first pre-stack seismic data further comprises offset information.
10. The apparatus of claim 6, wherein the information obtaining module is configured to:
obtaining the gray level co-occurrence matrix attribute with the highest crack sensitivity in the homogeneity, the contrast, the entropy and the angular second moment as a target attribute;
and acquiring fracture development information of the shale fracture based on the information corresponding to the target attribute.
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