CN119986801B - Method for predicting fracture-cavity filling degree by pre-stack earthquake based on structure tensor constraint - Google Patents
Method for predicting fracture-cavity filling degree by pre-stack earthquake based on structure tensor constraint Download PDFInfo
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Abstract
The invention discloses a method for predicting fracture and hole filling degree by using pre-stack earthquakes based on structural tensor constraint, which belongs to the technical field of geophysics and comprises the steps of acquiring logging data and logging data, performing fracture and hole model forward modeling, selecting and obtaining a most sensitive gradient structural tensor attribute body, constructing body constraint of a pre-stack elastic parameter body according to the well count statistics coincidence rate of the most sensitive gradient structural tensor attribute body, performing AVA trace forward modeling, constructing a low-frequency model to perform pre-stack inversion to obtain the pre-stack elastic parameter body, determining fracture and hole development boundaries of drilling sample points in a real drilling area by using the body constraint of the pre-stack elastic parameter body, analyzing fracture and hole full filling, half filling and unfilled elastic parameter value range by combining leakage characteristics to obtain a petrophysical interpretation model, and predicting the fracture and hole filling degree in the real drilling area based on the petrophysical interpretation model to obtain a fracture and hole filling degree prediction result. The method solves the problem that the seam hole is difficult to accurately and effectively predict.
Description
Technical Field
The invention belongs to the technical field of geophysics, and particularly relates to a method for predicting fracture-cavity filling degree of a prestack earthquake based on structural tensor constraint.
Background
The fracture-cavity type reservoir is a main reservoir type of a carbonate fracture-cavity type oil and gas reservoir and is a key for high and stable production of oil and gas fields. The effectiveness of the fracture-cavity type reservoir is mainly influenced by the filling degree, so that the accurate prediction of the filling degree of the fracture-cavity type reservoir is significant for the exploration and development of the fracture-cavity type oil and gas reservoir. The existing method for predicting the filling degree through geophysics comprises (1) a method based on logging, such as indicating the filling degree of a hole through abnormal changes of drilling and logging parameters, identifying the filling degree of the hole through logging, or predicting the filling degree of the hole through electric imaging, (2) a method based on earthquakes, such as directly predicting the filling degree of the hole through post-stack earthquake sensitive attributes or post-stack inversion bodies, or directly predicting the filling degree of the hole through pre-stack earthquake sensitive attributes or pre-stack elastic parameters, (3) a method based on forward modeling or simulation, such as directly predicting the filling degree of the hole through forward modeling technology, or filling and designing the filling degree of the hole through a three-dimensional physical model, (4) a method based on machine learning, such as predicting the filling degree of the hole based on BP neural network, wherein input targets mainly comprise a river level, a river type, a hole pattern, a relationship with a river entrance and exit, a relationship with a hall hole and the like, so as to establish a filling degree prediction model, and the filling degree of the hole is predicted according to the model.
The existing method for predicting the filling degree through geophysics mainly has the defects that (1) a method mainly based on logging is usually used for analyzing and predicting a single well, the filling degree of a reservoir cannot be effectively predicted in the transverse direction, (2) a method mainly based on earthquakes is usually used for predicting the filling degree by directly applying relevant earthquake attributes or elastic parameters, influences of non-fracture holes are ignored, the reliability of a prediction effect is low, (3) a method mainly based on forward modeling or simulation is used for simulating numerical values of a fracture hole model, the actual fracture hole filling degree cannot be directly predicted, and (4) the data reliability of an input end of the method mainly based on machine learning is still to be verified, and the accuracy of a prediction result is difficult to guarantee.
Disclosure of Invention
In order to overcome the defects in the prior art, the method for predicting the filling degree of the fracture hole by using the prestack earthquake based on the structure tensor constraint provided by the invention predicts the fracture hole body by using the structure tensor attribute body, and further optimizes the prestack elastic parameter longitudinal-transverse wave velocity ratio and longitudinal wave impedance to realize the prediction of the filling degree of the fracture hole by using the structure tensor attribute body as the constraint, thereby evaluating the effectiveness of the fracture hole and solving the problem that the fracture hole is difficult to accurately and effectively predict.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention provides a method for predicting the filling degree of a fracture-cavity by using a prestack earthquake based on structural tensor constraint, which comprises the following steps:
s1, acquiring logging data and logging data;
S2, performing fracture-cavity model forward modeling according to logging data and logging data, and selecting to obtain a most sensitive gradient structure tensor attribute body;
S3, according to the well count statistics coincidence rate of the most sensitive gradient structure tensor attribute body, constructing the body constraint of the pre-stack elastic parameter body;
s4, performing AVA gather forward modeling according to logging data and logging data, and constructing a low-frequency model to perform pre-stack inversion to obtain a pre-stack elastic parameter body;
s5, determining a fracture-cavity development boundary of a drilling sample point in the real drilling area by utilizing the body constraint of the pre-stack elastic parameter body, and analyzing the range of the full-filled, half-filled and unfilled elastic parameter values of the fracture-cavity by combining the leakage characteristics to obtain a rock physical interpretation model;
S6, predicting the fracture and hole filling degree in the real drilling area based on the rock physical interpretation model to obtain a fracture and hole filling degree prediction result.
The method has the beneficial effects that the method for predicting the filling degree of the seam hole based on the prestack earthquake of the structural tensor constraint provided by the invention can accurately delineate the most sensitive gradient structural tensor attribute body of the seam hole body based on forward modeling optimization of the seam hole related to fracture, so that the seam hole body in the seam hole development boundary in a real logging area is used as the body constraint of the prestack elastic parameter body to determine the seam hole filling degree prediction range, the feasibility of predicting the seam hole filling degree by utilizing prestack data is verified through forward modeling of an AVA (automatic Voltage) gather, then the prestack elastic parameter body capable of representing the seam hole filling degree is optimized, and finally a rock physical interpretation measuring plate is established by combining leakage characteristics to predict the filling degree of the seam hole.
Other advantages that are also present with respect to the present invention will be more detailed in the following examples.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the drawings needed in the embodiments, it being understood that the following drawings illustrate only some embodiments of the invention and are therefore not to be considered limiting of its scope, since other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a method for predicting fracture-cavity filling level in a pre-stack seismic based on structural tensor constraints in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
As shown in fig. 1, in one embodiment of the present invention, the present invention provides a method for predicting a hole filling degree in a prestack earthquake based on a structure tensor constraint, comprising the steps of:
s1, acquiring logging data and logging data;
S2, performing fracture-cavity model forward modeling according to logging data and logging data, and selecting to obtain the tensor attribute body of the most sensitive gradient structure, wherein the fracture-cavity model forward modeling index is used for constructing a geologic model containing a complex geologic structure through a numerical simulation method, simulating the propagation and reflection characteristics of seismic waves in a complex medium, and generating theoretical seismic response data.
The step S2 comprises the following steps:
S21, establishing a fracture-related fracture cavity model according to logging data and logging data, and performing model forward modeling to obtain seismic response characteristics of the fracture cavity during development;
The model forward modeling means that an underground geological model is established by utilizing the existing logging data and logging data, and the characteristics of the established seismic model are calculated through forward modeling by methods such as ray tracing or wave equation migration according to the propagation principle of seismic waves in an underground medium;
s22, performing dip angle scanning and Gaussian filtering on the seismic data volume based on the seismic response characteristics during fracture and cavity development, decomposing a structure tensor matrix, and selecting a sensitive gradient structure tensor attribute volume with the highest well count statistics coincidence rate as a most sensitive gradient structure tensor attribute volume.
The step S22 includes the steps of:
s221, performing dip angle scanning on the seismic data body based on the seismic response characteristics during the development of the fracture and tunnel, and filtering the seismic data body by utilizing a first three-dimensional Gaussian function to obtain a smooth data body, wherein the standard deviation of the first three-dimensional Gaussian function is a first standard deviation;
the first standard deviation is determined based on the size and noise of the hole body in the research area, and in the embodiment, the first standard deviation is set to be 0.6;
The calculation expression of the smooth data volume is as follows:
,
,
wherein, Representing a body of smoothed data,Representing a first three-dimensional gaussian function,A volume of seismic data is represented,A first standard deviation is indicated and a second standard deviation,An exponential function with e as the base constant is represented,Representing the position of the gaussian distribution on the X-axis,Representing the center point of the three-dimensional gaussian distribution in the X-axis direction,Representing the position of the gaussian distribution on the Y axis,Representing the center point of the three-dimensional gaussian distribution on the Y-axis,Representing the position of the gaussian distribution on the Z axis,Representing the center point of the three-dimensional Gaussian distribution on the Z axis;
S222, calculating to obtain gradient vector of smooth data volume And gradient tensor, wherein,Representing the smooth data volume differentiated for position on the X-axis,Representing the smooth data volume differentiated from the position on the Y-axis,Representing the smoothed data volume differentiated from the position on the Z-axis, T representing the transpose;
s223, filtering the gradient tensor of the smooth data body by using a second three-dimensional Gaussian function to obtain the filtered gradient tensor of the smooth data body, wherein the standard deviation of the second three-dimensional Gaussian function is a second standard deviation;
The computational expression of the second three-dimensional gaussian function is as follows:
,
wherein, Representing a second three-dimensional gaussian function,Representing a second standard deviation;
The second standard deviation is in inverse proportion to the accuracy of the hole body and in direct proportion to noise pressing, and in the scheme, the second standard deviation is 1.6;
S224, calculating to obtain a gradient structure tensor matrix based on the gradient vector of the smooth data body and the filtered gradient tensor;
the calculation expression of the gradient structure tensor matrix is as follows:
,
wherein, Representing a gradient structure tensor matrix;
In the scheme, the gradient structure tensor matrix is used as a real symmetric matrix, has a semi-positive definite quadratic property, has three non-negative eigenvalues, and corresponds to three eigenvectors which are orthogonal to each other, and can be decomposed into three eigenvectors and corresponding eigenvectors for the structure tensor matrix of any three-dimensional data.
S225, decomposing to obtain three eigenvalues and corresponding eigenvectors based on the gradient structure tensor matrix;
the three feature values and the corresponding feature vectors are calculated as follows:
,
wherein, A first feature vector is represented and is used to represent,A second feature vector is represented as such,A third feature vector is represented and is used to represent,A first characteristic value is indicated and a second characteristic value is indicated,A second characteristic value is indicated and is used to represent,Representing a third characteristic value;
The three eigenvalues are a first eigenvalue, a second eigenvalue and a third eigenvalue respectively, the first eigenvalue corresponds to the first eigenvector, the direction corresponding to the first eigenvector is the direction in which the local stratum structure changes most rapidly, the local plane perpendicular to the reflection phase axis is the first eigenvalue, the corresponding change amount is the first eigenvalue, the second eigenvalue corresponds to the second eigenvector, the third eigenvalue corresponds to the third eigenvector, the plane formed by the second eigenvector and the third eigenvector is perpendicular to the first eigenvector and represents the transverse discontinuous change direction, such as the seismic data change caused by fault reflection characteristics, the direction in which the local stratum structure changes most rapidly on the plane is the direction corresponding to the second eigenvector, the corresponding change rate is the second eigenvalue, the direction in which the local stratum structure changes most slowly on the plane is the direction corresponding to the third eigenvector, the corresponding change rate is the third eigenvector, and the three eigenvalues and the corresponding eigenvectors construct a structure change gradient representation system in different dimension directions in the three-dimensional space.
S226, selecting and obtaining a plurality of sensitive gradient structure tensor attribute bodies for describing the carbonate fracture-cavity body according to the three characteristic values and the corresponding characteristic vectors;
in this embodiment, for the carbonate fracture-cavity body, the first feature value reflects the layered reflection feature, and the second feature value and the third feature value delineate the fracture-cavity body reflection abnormal region, so that the second feature value and the third feature value can be used as sensitive gradient structure tensor attribute bodies delineated by the carbonate fracture-cavity body;
S227, comparing the well count statistics coincidence rate of the fracture-cavity body and the actual well count based on each sensitive gradient structure tensor attribute body, and selecting the sensitive gradient structure tensor attribute body with the highest well count statistics coincidence rate as the most sensitive gradient structure tensor attribute body.
The step S227 includes the steps of:
s2271, finely describing the fracture-cavity body by using sensitive gradient structure tensor attribute bodies respectively to obtain the number of wells corresponding to each sensitive gradient structure tensor attribute body;
S2272, dividing the number of wells corresponding to each sensitive gradient structure tensor attribute body by the actual number of wells to obtain the statistical coincidence rate of the number of wells corresponding to each sensitive gradient structure tensor attribute body;
S2273, selecting the sensitive gradient structure tensor attribute with the highest well count statistics matching rate as the most sensitive gradient structure tensor attribute.
In this embodiment, for the same drilling data, for example, if the total number of wells is 32, the number of coincident wells of the second feature value serving as the sensitive gradient structure tensor attribute body and the actual drilling is 26, and the number of coincident wells of the third feature value serving as the sensitive gradient structure tensor attribute body and the actual drilling is 22, so that the statistical coincidence rate of the number of wells corresponding to the second feature value is higher than the statistical coincidence rate of the number of wells corresponding to the third feature value, and the second feature value is selected as the most sensitive gradient structure tensor attribute body.
S3, according to the well count statistics coincidence rate of the most sensitive gradient structure tensor attribute body, constructing the body constraint of the pre-stack elastic parameter body;
the calculation expression of the volume constraint of the pre-stack elastic parameter body in the step S3 is as follows:
,
wherein, The development state of the fracture-cavity body is represented,The well count statistics fitness rate of the tensor attribute volume of the most sensitive gradient structure is represented,Representing a threshold value of the statistical fitness rate of the well number,The development state of the suture hole is indicated,Indicating the non-development state of the suture hole.
In this embodiment, the threshold value of the statistical fit rate of the well number is set to 0.19, that is, the statistical fit rate of the well number of the tensor attribute body of the most sensitive gradient structure is greater than 0.19, which indicates that the fracture hole is in a development state, and if the statistical fit rate of the well number of the tensor attribute body of the most sensitive gradient structure is less than or equal to 0.19, which indicates that the fracture hole is in an undeveloped state. In the scheme, the development condition of the fracture hole is used as the constraint of the structure tensor attribute body.
S4, carrying out AVA gather forward modeling according to logging data and logging data, and constructing a low-frequency model to carry out pre-stack inversion to obtain a pre-stack elastic parameter body, wherein the amplitude changes along with offset distance (Amplitude Variation WITH ANGLE, AVA) which is an important technology for analyzing lithology and fluid properties in seismic exploration, the gather forward modeling refers to a process of calculating reflection amplitudes of seismic waves under different incidence angles through a numerical simulation method to generate theoretical seismic gather data, and the AVA gather forward modeling refers to a process of generating the theoretical AVA gather data by establishing a geological model to simulate reflection characteristics of the seismic waves under different incidence angles;
the step S4 comprises the following steps:
s41, carrying out AVA gather forward modeling by utilizing real drilling data with transverse wave data according to logging data and logging data, and analyzing AVA characteristics of the fracture holes with different filling degrees to obtain AVA gathers with different filling degrees;
the AVA features with different filling degrees in the S41 comprise a no AVA feature, a one class AVA feature, a two class AVA feature and three classes AVA feature.
According to the scheme, real drilling data with transverse wave data are analyzed to find that no AVA features exist when a fracture hole is fully filled, one type of AVA features or two types of AVA features exist when the fracture hole is semi-filled, wherein one type of AVA features refer to the fact that the amplitude of a wave crest in-phase axis decreases along with the increase of an incident angle, two types of AVA features refer to the fact that the angle gather has the feature that one wave crest amplitude is converted into one wave trough amplitude from a zero incident angle to a maximum incident angle, and three types of AVA features exist when the fracture hole is not filled, wherein the three types of AVA features refer to the fact that the amplitude of a wave trough in-phase axis increases along with the increase of the incident angle. The above different degrees of AVA characteristics depend on longitudinal wave velocity, transverse wave velocity and density magnitude.
S42, denoising the AVA trace sets with different filling degrees, and performing trace set cutting and residual amplitude compensation on the denoised AVA trace set to obtain an optimized AVA trace set;
in the embodiment, AVA gathers with different filling degrees are denoised by means of filtering denoising, linear denoising, singular value denoising and the like so as to improve the signal to noise ratio of the gathers;
s43, overlapping the optimized AVA trace set to obtain near, middle and far overlapping data bodies;
S44, repeatedly iterating well earthquake calibration and extracting AVA wavelets of near, middle and far superimposed data volumes for preset times to obtain the horizontal constraint of the earthquake interpretation horizon;
In this embodiment, the well-seismic calibration aims at establishing a corresponding relationship between logging data and seismic data, namely near, middle and far superimposed data volumes, so that a geological horizon corresponding to a same phase axis of a seismic can be determined, unification of a time domain and a depth domain is realized, and a foundation is provided for geological interpretation and reservoir prediction. In the embodiment, the well earthquake calibration and the wavelet extraction of the near, middle and far superimposed data volumes are mutually iterated, the transverse constraint of the earthquake interpretation horizon is fully extracted through multiple iterations, the earthquake data has continuity in space, correlation exists between adjacent earthquake tracks, the transverse constraint of the horizon utilizes the characteristic, the interpretation precision is improved through the following mode, and key information such as geological structures, reservoir spread and the like can be effectively identified by utilizing the transverse correlation of the earthquake data and combining with geological priori information.
S45, performing layer-by-layer interpolation on the logging data by utilizing the horizontal constraint of the seismic interpretation layer to construct a low-frequency model, wherein the low-frequency model comprises a longitudinal wave velocity ratio model and a longitudinal wave impedance model;
s46, performing prestack inversion based on the low-frequency model to obtain a prestack elastic parameter body.
In the scheme, a Zoeppritz equation in Jason software is used for carrying out prestack inversion to obtain a prestack elastic parameter body.
The pre-stack elastic parameter bodies in the S46 are a longitudinal-transverse wave velocity ratio body and a longitudinal wave impedance body.
S5, determining a fracture-cavity development boundary of a drilling sample point in the real drilling area by utilizing the body constraint of the pre-stack elastic parameter body, and analyzing the range of the full-filled, half-filled and unfilled elastic parameter values of the fracture-cavity by combining the leakage characteristics to obtain a rock physical interpretation model;
the step S5 comprises the following steps:
s51, judging the seam hole development condition of a drilling sample point in the real drilling area by utilizing the body constraint of the pre-stack elastic parameter body to obtain a seam hole development boundary;
In the scheme, the region in the development state of the fracture hole in the real drilling region can be distinguished from the region in the non-development state of the fracture hole based on the pre-stack elastic parameters, so that the fracture hole development boundary is obtained, and only the part in the development state of the fracture hole is subjected to filling degree prediction.
S52, carrying out matching analysis on the filling state and the prestack elastic parameter body value range in the region in the seam hole development state in the drilling sample point according to the seam hole development boundary to obtain the prestack elastic parameter body value range in the process of full filling of the seam hole, the process of half filling of the seam hole and the process of unfilling the seam hole, wherein the prestack elastic parameter body value range refers to the longitudinal and transverse wave speed ratio body and the longitudinal wave impedance body value range;
s53, acquiring leakage characteristics in logging data of a real drilling area;
And S54, verifying and adjusting the value range of the longitudinal and transverse wave speed ratio body and the longitudinal wave impedance body when the seam hole is fully filled, the seam hole is half filled and the seam hole is unfilled based on the leakage characteristics, so as to obtain a rock physical interpretation model.
In this embodiment, the value range of the longitudinal-transverse wave speed ratio body and the value range of the longitudinal wave impedance body in different filling situations of the fracture cavity are adjusted based on the leakage characteristics, so that the adjusted value range can be accurately matched with the filling state of the fracture cavity, i.e. the value range of the adjusted longitudinal-transverse wave speed ratio body and the value range of the longitudinal wave impedance body are used as a petrophysical interpretation model.
In the embodiment, taking a W1 well as an example, a petrophysical interpretation model constructed based on the W1 well is characterized in that the full filling of the fracture cavity is realized when the longitudinal wave impedance threshold value is larger than 17200/s x g/cm and the longitudinal and transverse wave speed ratio threshold value is larger than 1.91, the unfilled fracture cavity is characterized when the longitudinal wave impedance threshold value is smaller than 17200m/s x g/cm and the longitudinal and transverse wave speed ratio threshold value is smaller than 1.91, and the rest half filling is characterized. Due to the influence of the prediction resolution, the logging scale can identify 11 slots, 1-7 of which are unfilled, 8, 10 and 11 of which are fully filled, 9 of which are half-filled, and the seismic scale predicts 2 slots, 1 of which are unfilled and 2 of which are half-filled.
S6, predicting the fracture and hole filling degree in the real drilling area based on the rock physical interpretation model to obtain a fracture and hole filling degree prediction result.
The step S6 comprises the following steps:
S61, respectively extracting plan views of unfilled fracture holes, full-filled fracture holes and half-filled fracture holes by taking the top and bottom of a target layer in a real drilling area as a time window according to a petrophysical interpretation model;
s62, overlapping the plan views of unfilled, full-filled and half-filled to obtain a seam filling degree prediction result.
In this embodiment, the filling degree prediction section of the earthquake prediction is consistent with the well logging interpretation, wherein the unfilled slot 1 is lost through 1838.5m 3 during the drilling process. According to the value range of the longitudinal and transverse wave velocity ratio body and the longitudinal wave impedance body in the petrophysical interpretation model, respectively extracting unfilled, fully filled and semi-filled plane views by taking the top and bottom of a target layer as time windows, and then superposing and displaying that the seam holes are distributed along faults, most of the seam holes are fully filled or semi-filled, only a small amount of the seam holes are unfilled, the unfilled seam holes are mainly distributed in the range related to large-scale fracture, and the small-scale fracture is mostly fully filled, which can be related to the stress direction, the strength and the crack opening degree of the fracture.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention.
Claims (7)
1. The method for predicting the seam hole filling degree by using the pre-stack earthquake based on the structure tensor constraint is characterized by comprising the following steps:
s1, acquiring logging data and logging data;
S2, performing fracture-cavity model forward modeling according to logging data and logging data, and selecting to obtain a most sensitive gradient structure tensor attribute body;
S3, according to the well count statistics coincidence rate of the most sensitive gradient structure tensor attribute body, constructing the body constraint of the pre-stack elastic parameter body;
s4, performing AVA gather forward modeling according to logging data and logging data, and constructing a low-frequency model to perform pre-stack inversion to obtain a pre-stack elastic parameter body;
the prestack elastic parameter body is a longitudinal-transverse wave speed ratio body and a longitudinal wave impedance body;
s5, determining a fracture-cavity development boundary of a drilling sample point in the real drilling area by utilizing the body constraint of the pre-stack elastic parameter body, and analyzing the range of the full-filled, half-filled and unfilled elastic parameter values of the fracture-cavity by combining the leakage characteristics to obtain a rock physical interpretation model;
the step S5 comprises the following steps:
s51, judging the seam hole development condition of a drilling sample point in the real drilling area by utilizing the body constraint of the pre-stack elastic parameter body to obtain a seam hole development boundary;
s52, carrying out matching analysis on the filling state and the prestack elastic parameter body value range in the region in the seam hole development state in the drilling sample point according to the seam hole development boundary to obtain the prestack elastic parameter body value range in the process of full filling of the seam hole, the process of half filling of the seam hole and the process of unfilling the seam hole, wherein the prestack elastic parameter body value range refers to the longitudinal and transverse wave speed ratio body and the longitudinal wave impedance body value range;
s53, acquiring leakage characteristics in logging data of a real drilling area;
s54, verifying and adjusting the value range of the longitudinal and transverse wave speed ratio body and the longitudinal wave impedance body when the seam hole is fully filled, the seam hole is half filled and the seam hole is unfilled based on the leakage characteristics to obtain a rock physical interpretation model;
S6, predicting the fracture and hole filling degree in the real drilling area based on the rock physical interpretation model to obtain a fracture and hole filling degree prediction result.
2. The method for predicting the fracture-cavity filling degree of a prestack earthquake based on the structure tensor constraint according to claim 1, wherein the step S2 comprises the following steps:
S21, establishing a fracture-related fracture cavity model according to logging data and logging data, and performing model forward modeling to obtain seismic response characteristics of the fracture cavity during development;
s22, performing dip angle scanning and Gaussian filtering on the seismic data volume based on the seismic response characteristics during fracture and cavity development, decomposing a structure tensor matrix, and selecting a sensitive gradient structure tensor attribute volume with the highest well count statistics coincidence rate as a most sensitive gradient structure tensor attribute volume.
3. The method for predicting the fracture-cavity filling degree of a prestack earthquake based on the structure tensor constraint according to claim 2, wherein the step S22 comprises the following steps:
s221, performing dip angle scanning on the seismic data body based on the seismic response characteristics during the development of the fracture and tunnel, and filtering the seismic data body by using a first three-dimensional Gaussian function to obtain a smooth data body, wherein the standard deviation of the first three-dimensional Gaussian function is a first standard deviation;
The calculation expression of the smooth data volume is as follows:
,
,
wherein, Representing a body of smoothed data,Representing a first three-dimensional gaussian function,A volume of seismic data is represented,A first standard deviation is indicated and a second standard deviation,An exponential function with e as the base constant is represented,Representing the position of the gaussian distribution on the X-axis,Representing the center point of the three-dimensional gaussian distribution in the X-axis direction,Representing the position of the gaussian distribution on the Y axis,Representing the center point of the three-dimensional gaussian distribution on the Y-axis,Representing the position of the gaussian distribution on the Z axis,Representing the center point of the three-dimensional Gaussian distribution on the Z axis;
S222, calculating to obtain gradient vector of smooth data volume And gradient tensor, wherein,Representing the smooth data volume differentiated for position on the X-axis,Representing the smooth data volume differentiated from the position on the Y-axis,Representing the smoothed data volume differentiated from the position on the Z-axis, T representing the transpose;
s223, filtering the gradient tensor of the smooth data body by using a second three-dimensional Gaussian function to obtain the filtered gradient tensor of the smooth data body, wherein the standard deviation of the second three-dimensional Gaussian function is a second standard deviation;
The computational expression of the second three-dimensional gaussian function is as follows:
,
wherein, Representing a second three-dimensional gaussian function,Representing a second standard deviation;
S224, calculating to obtain a gradient structure tensor matrix based on the gradient vector of the smooth data body and the filtered gradient tensor;
the calculation expression of the gradient structure tensor matrix is as follows:
,
wherein, Representing a gradient structure tensor matrix;
S225, decomposing to obtain three eigenvalues and corresponding eigenvectors based on the gradient structure tensor matrix;
the three feature values and the corresponding feature vectors are calculated as follows:
,
wherein, A first feature vector is represented and is used to represent,A second feature vector is represented as such,A third feature vector is represented and is used to represent,A first characteristic value is indicated and a second characteristic value is indicated,A second characteristic value is indicated and is used to represent,Representing a third characteristic value;
S226, selecting and obtaining a plurality of sensitive gradient structure tensor attribute bodies for describing the carbonate fracture-cavity body according to the three characteristic values and the corresponding characteristic vectors;
S227, comparing the well count statistics coincidence rate of the fracture-cavity body and the actual well count based on each sensitive gradient structure tensor attribute body, and selecting the sensitive gradient structure tensor attribute body with the highest well count statistics coincidence rate as the most sensitive gradient structure tensor attribute body.
4. A method for predicting the filling degree of a fracture-cavity by using a prestack earthquake based on a structure tensor constraint according to claim 3, wherein the calculation expression of the volume constraint of the prestack elastic parameter body in S3 is as follows:
,
wherein, The development state of the fracture-cavity body is represented,The well count statistics fitness rate of the tensor attribute volume of the most sensitive gradient structure is represented,Representing a threshold value of the statistical fitness rate of the well number,The development state of the suture hole is indicated,Indicating the non-development state of the suture hole.
5. The method for predicting the fracture-cavity filling degree of a prestack earthquake based on the structure tensor constraint according to claim 1, wherein the step S4 comprises the following steps:
s41, carrying out AVA gather forward modeling by utilizing real drilling data with transverse wave data according to logging data and logging data, and analyzing AVA characteristics of the fracture holes with different filling degrees to obtain AVA gathers with different filling degrees;
s42, denoising the AVA trace sets with different filling degrees, and performing trace set cutting and residual amplitude compensation on the denoised AVA trace set to obtain an optimized AVA trace set;
s43, overlapping the optimized AVA trace set to obtain near, middle and far overlapping data bodies;
S44, repeatedly iterating well earthquake calibration and extracting AVA wavelets of near, middle and far superimposed data volumes for preset times to obtain the horizontal constraint of the earthquake interpretation horizon;
S45, performing layer-by-layer interpolation on the logging data by utilizing the horizontal constraint of the seismic interpretation layer to construct a low-frequency model, wherein the low-frequency model comprises a longitudinal wave velocity ratio model and a longitudinal wave impedance model;
s46, performing prestack inversion based on the low-frequency model to obtain a prestack elastic parameter body.
6. The method for predicting fracture-cavity filling level in pre-stack seismic based on structural tensor constraints of claim 5, wherein the AVA features of different filling levels in S41 comprise a no AVA feature, a one AVA feature, a two AVA feature, and three AVA features.
7. The method for predicting the fracture-cavity filling degree of a prestack earthquake based on the structure tensor constraint according to claim 1, wherein the step S6 comprises the following steps:
S61, respectively extracting plan views of unfilled fracture holes, full-filled fracture holes and half-filled fracture holes by taking the top and bottom of a target layer in a real drilling area as a time window according to a petrophysical interpretation model;
s62, overlapping the plan views of unfilled, full-filled and half-filled to obtain a seam filling degree prediction result.
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