CN116931072A - Volcanic reservoir fracture quantitative prediction method based on OVT domain prestack fusion attribute - Google Patents
Volcanic reservoir fracture quantitative prediction method based on OVT domain prestack fusion attribute Download PDFInfo
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Abstract
The invention provides a volcanic reservoir fracture quantitative prediction method based on an OVT domain prestack fusion attribute, which comprises the following steps: step 1, pre-stack seismic data of an OVT domain in a work area are selected to obtain an inclination angle guiding filtering data body; step 2, respectively extracting variance and coherence attribute data volumes by using the dip angle guiding filtering data volumes; step 3, calculating to obtain a fracture prediction fusion attribute body based on the variance attribute body and the eigenvalue coherent data body; and 4, selecting a fracture development zone with fusion attribute of drilling calibration in the work area, and carrying out amplitude filtering treatment of the fusion attribute according to a fracture zone drilling calibration threshold value to predict a volcanic reservoir fracture development area. The quantitative prediction method for the volcanic reservoir cracks based on the OVT domain prestack fusion attribute can intuitively and clearly predict the morphology, the size, the density and the planar distribution of the cracks, and provides reliable technical support for the volcanic reservoir crack prediction.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration and reservoir prediction, in particular to a volcanic reservoir fracture quantitative prediction method based on an OVT domain prestack fusion attribute.
Background
Volcanic reservoir fracture identification has been a popular choice for exploration, development and research. Because of the complex lithology of volcanic rock, the variety of minerals is numerous, and accurate identification of the reservoir fracture has been a difficulty. The fracture is an important component of the volcanic reservoir space, and plays a role in communicating pores and seepage channels, and meanwhile, the fracture development zone is also a favorable volcanic reservoir. Therefore, it is particularly important to accurately predict volcanic reservoir cracks. The crack identification method is various, such as conventional logging identification, imaging logging identification, core identification, sheet identification and the like. The core and sheet identification data are less; the conventional logging crack identification data is easy to obtain and convenient to process, but the longitudinal sampling interval is larger (generally 0.125 m), the longitudinal resolution is low, and the crack identification accuracy is low. The imaging logging data has high longitudinal resolution, visual and accurate reservoir fracture identification, but has high cost and high expense, and is only limited to prediction on points. The application of the seismic technology to identify the cracks can effectively identify the cracks. The common crack prediction technology mainly comprises pre-stack OVT domain crack prediction, AVAZ crack prediction and the like; post-stack seismic attribute prediction mainly comprises a method for predicting cracks, such as coherence, variance, curvature and the like.
Compared with the conventional gather, the OVT domain gather contains offset and azimuth information which is beneficial to crack prediction, the anisotropy phenomenon is corrected, and the gather energy is more uniform. The method can provide more accurate and rapid basic data for prestack orientation attribute fusion, prestack prediction and inversion orientation optimization, and achieves obvious effects. The OVT domain seismic attribute analysis can improve the seismic attribute analysis efficiency on the premise of ensuring the seismic attribute analysis precision.
The coherence technique is the most widely applied crack prediction technique at present, and is to calculate dissimilarity between adjacent channels in a seismic data volume to form a coherent data volume for measuring the similarity degree among multiple channels of seismic data. The data volume is used to characterize the change in the continuity of the seismic wave. The development of coherent technology has progressed to the third generation today. The first generation technology is mainly based on a normalized cross-correlation algorithm and is proposed by Bahor i ch et al; the second generation technology is based on a multi-channel similarity measurement algorithm, and the similarity between seismic channels is calculated by constructing a covariance matrix, so that the noise resistance and the stability are further improved, and the method is proposed by Marfurt et al; the third generation technology is based on an intrinsic structural algorithm, calculates similarity through the characteristic structure of the matrix, and improves the transverse resolution on the basis of the second generation algorithm. The coherent algorithm can better predict the spatial distribution of various scale crack forms so far, but only can carry out qualitative prediction, and quantitative description cannot be achieved.
The variance is used for representing the dispersity of a group of data, and the adjacent seismic data variance represents the discontinuity of the seismic data in the seismic data interpretation, so that the irrelevance of the seismic data is highlighted through the variance of the seismic data, and the variance is used for describing the integral space distribution characteristics of structures such as faults and lithology. The core of the variance technology is to calculate the variance value of the weighted movement of all the sample points of the whole post-stack three-dimensional data.
Aiming at the development characteristics of the volcanic reservoir, a single seismic attribute has certain limitation in identifying the plane distribution characteristics of the cracks, and the development characteristics and the distribution range of the cracks can not be clearly and intuitively predicted. But the requirements of clearly identifying the crack beads and describing the distribution of the volcanic rock crack planes are not met.
In application number: in CN201510293984.1, a quantitative prediction method and device for a volcanic crack are related, where the quantitative prediction method for a volcanic crack includes: acquiring drilling core data, imaging logging data and a logging curve, and generating a single well crack density indication curve according to the drilling core data, the imaging logging data and the logging curve; establishing a three-dimensional geological model of an oil reservoir, extracting geological properties reflecting cracks from the three-dimensional geological model of the oil reservoir, and extracting geophysical properties reflecting the cracks from seismic data; performing correlation judgment according to the single well fracture density indication curve, the geological attribute and the geophysical attribute, and generating a pre-judgment result, wherein the pre-judgment result comprises a plurality of fracture attributes with fracture correlation larger than a preset value; and carrying out crack distribution simulation in a three-dimensional space according to the pre-judging result, calculating a continuous distribution model of the three-dimensional space cracks, and carrying out quantitative prediction of the inter-well cracks. The method can realize quantitative prediction of the inter-well cracks, and can quickly and accurately determine the favorable targets in volcanic oil reservoir exploration and development.
In application number: the Chinese patent application of CN201910230936.6 relates to a method and a device for predicting a coherent fracture by multiple integral of seismic data. The method comprises the following steps: acquiring logging seismic data; extracting seismic wavelets according to the well logging seismic data, and determining a time depth relation and top and bottom reservoir parameters according to the well logging seismic data; generating a synthetic seismic record according to the seismic wavelet, the time-depth relation and the top-bottom parameters; performing multiple integral operation on the synthetic seismic records, and determining calculation parameters and sliding time windows; performing relative impedance operation on the seismic data body according to the calculation parameters and the sliding time window to generate a relative impedance data body; performing coherent operation on the relative impedance data body by adopting a third-generation coherent algorithm to generate a coherent data body; and determining the fracture of the target reservoir according to the section of the target reservoir in the coherent data volume. The application compresses the seismic waveform through multiple integral operation, overcomes the influence of the seismic wavelet length on the seismic data, and reduces the multi-resolution and uncertainty of crack prediction.
In application number: in CN201010205983.4, a method and apparatus for crack prediction are related, where the method includes: acquiring pre-stack common-center-point dynamic correction gather seismic data and relevant parameters for crack prediction; generating corresponding index data according to the pre-stack common-center-point dynamic correction gather seismic data, and acquiring horizon data according to post-stack seismic data interpretation; generating conventional wide-azimuth pre-stack common-center-point moving correction gather data from conventional narrow-azimuth pre-stack common-center-point moving correction gather data by using pre-stack common-center-point moving correction gather seismic data; acquiring the related track head information of each seismic track according to conventional wide-azimuth prestack common-center point dynamic correction track set data; calculating azimuth angles of all seismic channels by using the acquired channel head information; taking the target layer as a pickup center, and acquiring the reflection amplitude of each seismic channel by using a pickup time window; ellipse fitting is performed using the acquired azimuth and reflection amplitude to determine fracture direction and fracture density. By the embodiment of the invention, the precision of fracture reservoir prediction can be improved; and the method can realize three-dimensional prediction on the fracture reservoir, and can also perform two-dimensional prediction, thereby improving the success rate of exploratory wells and well drilling.
The prior art is greatly different from the method, the technical problem which is needed to be solved by the user is not solved, and the method for quantitatively predicting the volcanic reservoir fracture based on the OVT domain prestack fusion attribute is invented.
Disclosure of Invention
The invention aims to provide a quantitative prediction method for volcanic reservoir cracks based on OVT domain prestack fusion attribute, which can clearly and intuitively identify the volcanic reservoir cracks.
The aim of the invention can be achieved by the following technical measures: the volcanic reservoir fracture quantitative prediction method based on the OVT domain prestack fusion attribute comprises the following steps:
step 1, pre-stack seismic data of an OVT domain in a work area are selected to obtain an inclination angle guiding filtering data body;
step 2, respectively extracting variance and coherence attribute data volumes by using the dip angle guiding filtering data volumes;
step 3, calculating to obtain a fracture prediction fusion attribute body based on the variance attribute body and the eigenvalue coherent data body;
and 4, selecting a fracture development zone with fusion attribute of drilling calibration in the work area, and carrying out amplitude filtering treatment of the fusion attribute according to a fracture zone drilling calibration threshold value to predict a volcanic reservoir fracture development area.
The aim of the invention can be achieved by the following technical measures:
in step 1, pre-stack seismic data of an OVT domain in a work area are selected, a target interval time window needing to be interpreted is set, and dip angle guiding filtering processing is carried out by utilizing the pre-stack seismic data of the OVT domain, so that a dip angle guiding filtered data body is obtained.
In step 1, when dip angle guiding median filtering processing is performed on the OVT domain prestack seismic data, the method includes:
defining a time window in the three-dimensional seismic data, the time window comprising 2j+1 sample points; the number of the sample points is required to be smaller than the number of the sample points in the longitudinal time domain of the seismic data body, and the number of the sample points is an odd number, so that 2j+1 sample points are set;
calculating the value of the (j+1) th sample point in the time window by adopting a median filtering method, wherein the calculation formula is as follows
In formula 1, i=1, 2, …, N;the value of the j+1th sample point in the time window obtained by adopting the median filtering method.
The extraction of the dip angle information is an important precondition of dip angle guiding filtering, the dip angle calculation is carried out by using a plane wave destructive filtering method, and the local plane waves are expressed as follows:
where u (x, t) represents the wave field function, x represents the offset or horizontal distance, t is time, σ is the local dip of the airspace continuously; when the formation dip varies along the spatio-temporal direction, adjacent seismic traces may be predicted using local operators.
In step 1, under the linear operator representation, the plane wave decomposition operator may be represented as:
d=d×s 3
Wherein s is a seismic trace, D is a residual error, D is a plane wave destructive filter, and D is defined as formula 4:
wherein P is i，j To be the predictor from the ith to the jth lanes, I and P are functions of the local tilt angle sigma i，j M is a column vector; the local dip σ of the seismic trace may be estimated by minimizing the objective function, prediction error d, as shown in equation (5):
wherein epsilon is a regularized scaling coefficient and S is a shaping regularization operator; and using the inclination angle information obtained by the plane destructive filtering as the input of the median filtering to obtain an inclination angle guiding filtering data body.
In step 2, variance attribute calculation is performed by using the three-dimensional seismic data volume subjected to dip angle guiding filtering, and a variance attribute volume is obtained.
In step 2, when the variance attribute calculation is performed by using the three-dimensional seismic data body subjected to the dip angle guiding filtering processing, n+1 channels are added to n channels adjacent to the periphery, the sampling point is taken as the center, the number of sampling points in the length of each half of the time window is up and down, the sampling interval is assumed to be 1ms, the time window length is L=500 ms, the average value of the amplitude of the corresponding sampling point in the time window length of 500ms in n+1 channels is firstly calculated, the sum of the variance of the amplitude of each sampling point in n+1 channels and the average value of the amplitude in n+1 channels at the same moment is calculated, and finally the weighted value of the sine trigonometric function is multiplied, and normalization is performed, so that the variance value of the sampling point is obtained.
In step 2, the variance formula is defined as follows:
omega = sin theta (theta is more than or equal to 0 and less than or equal to 90 degrees; omega is more than or equal to 0 and less than or equal to 1) 8
Delta in t 2 Is the variance value; delta ω 2 (Q) is the weighted variance value; x is x ij The seismic amplitude value of j time i channels; l is the time window length of the variance; i is the number of adjacent tracks needed for calculating the variance value; omega j-t A triangle weighting function for a certain sampling point in a certain time window is 1 at maximum and 0 at minimum; by the above formula, the variance value of each sampling point in the whole three-dimensional data body can be calculated, and finally the three-dimensional variance data body is obtained.
In step 2, coherent attribute calculation is performed by using the three-dimensional seismic data volume subjected to dip angle guiding filtering, and a coherent attribute volume is obtained.
In step 2, performing coherence attribute calculation by using the three-dimensional seismic data body subjected to dip angle guided filtering, wherein the eigenvalue coherence algorithm calculates coherence attributes through eigenvalues of a covariance matrix; the eigenvalue coherence algorithm is defined as follows:
in formula 9, d KN The K sample value of the N-th channel;
the covariance matrix C of the three-dimensional data volume in the analysis window is calculated according to the formula shown in figure 10,for the K-th row vector in matrix D +.>Which represents the aggregate of the kth sample points of the data volume; d, d k Is->Is to be used in the present invention,
d k ＝[d k1 ,d k2 ,…,d kN ] T
let lambda be k Is the kth eigenvalue, lambda, of the covariance matrix C 1 The maximum characteristic value is shown in the formula 11, and the coherence attribute is calculated according to the formula 11;
lambda in 11 k Is the kth eigenvalue, lambda, of the covariance matrix C m Is the largest of these.
In step 3, based on the variance attribute volume and the eigenvalue coherence attribute volume, a fracture prediction fusion attribute volume is obtained through square weighting calculation.
In step 3, the calculation formula of the fracture prediction fusion attribute body is as follows:
Z i ＝mQ i 2 +nE i 2 12. Fig.
In formula 12, i=1, 2,..m, Z i Predicting the value of a sampling point in the fusion attribute body for the crack; q (Q) i For corresponding positions in the variance attribute volumeThe value of the sampling point; e (E) i Values of sampling points at corresponding positions in the eigenvalue coherence attribute volume; m and n are parameter values of data volumes in different areas.
In step 4, calibrating the fracture zone with the drilling, determining the fracture zone threshold comprises:
through the core fracture observation and imaging logging fracture characteristic analysis of the target interval in the zone, and the conventional logging fracture section of the core and imaging logging scale, the fracture section possibly developed by the target interval is identified;
calibrating the earthquake crack prediction fusion attribute by using the information of the drilling crack section, determining the form and the scale of the crack on the earthquake, and determining the amplitude value of the crack section;
and carrying out amplitude grading pickup according to the calibrated minimum amplitude value of the fracture zone, and predicting the fracture development characteristics of the target interval.
The volcanic reservoir fracture quantification prediction method based on the OVT domain prestack multi-attribute fusion further comprises the step of introducing an amplitude-graded fracture prediction fusion attribute body by utilizing 3D stereoscopic display software after the step 4, and predicting the profile and plane distribution characteristics of the fracture.
According to the volcanic reservoir fracture quantitative prediction method based on the OVT domain prestack fusion attribute, the dip angle guiding filtering processing is carried out by utilizing the OVT domain prestack seismic data, the variance body attribute and the eigenvalue coherence attribute are extracted by utilizing the dip angle guiding filtered data body, and after square weighting is carried out on the two attributes, the new fracture prediction fusion attribute is obtained, so that the morphology, the size, the density and the plane distribution of the fracture can be intuitively and clearly predicted, and reliable technical support is provided for the volcanic reservoir fracture prediction.
The technical scheme provided by the invention creatively fuses the seismic variance attribute body and the seismic coherence attribute body through square weighting operation to generate the fracture prediction fusion attribute body, and the attribute can intuitively, clearly and accurately describe the development position, the morphology and the scale of the volcanic reservoir fracture.
According to the technical scheme, the rock core is used for logging and identifying the cracks, the conventional logging is used for identifying the crack segments, and the threshold value of a crack zone is set on the well through comprehensive calibration of well vibration, so that the development form, scale and density of the cracks are determined. The technology has good guiding significance for comprehensive application of seismic attributes and volcanic reservoir fracture identification and prediction.
Drawings
FIG. 1 is a flowchart of an embodiment of a method for quantitative prediction of reservoir cracks in volcanic based on OVT domain prestack fusion properties according to the present invention;
FIG. 2 is a seismic cross-section of a quasi-North 1 three-dimensional, quasi-North 1 well result in accordance with an embodiment of the present invention;
FIG. 3 is a cross-sectional view of a quasi-North 1 three-dimensional, over-quasi-North 1 well dip steering filtered seismic section in accordance with an embodiment of the invention;
FIG. 4 is a cross-sectional view of quasi-north 1 three-dimensional quasi-north 1-well north-south eigenvalues in an embodiment of the present invention;
FIG. 5 shows a quasi-North 1 three-dimensional carbon stone system C according to an embodiment of the present invention 1 j 2 Eigenvalue coherence attribute slicing graph;
FIG. 6 is a cross-sectional view of a north-accurate 1-dimensional north-accurate 1-well north-south variance in accordance with an embodiment of the present invention;
FIG. 7 shows a quasi-North 1 three-dimensional carbon stone system C according to an embodiment of the present invention 1 j 2 A variance volume attribute slice;
FIG. 8 is a cross-sectional view of a quasi-North 1 three-dimensional super-quasi-North 1 North-south fusion attribute in accordance with an embodiment of the present invention;
FIG. 9 is a graph of a conventional log fracture identification for a quasi-north 1-well imaging log scale in accordance with an embodiment of the present invention;
FIG. 10 is a cross-sectional view of quantitative fracture prediction for a quasi-North 1 three-dimensional quasi-North 1 well northbound fusion attribute in accordance with an embodiment of the present invention;
FIG. 11 is a 3D perspective view of a quasi-North 1 three-dimensional super-quasi-North 1 well fusion attribute quantitative fracture prediction in accordance with an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular forms also are intended to include the plural forms unless the context clearly indicates otherwise, and furthermore, it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, and/or combinations thereof.
The following are several specific examples of the application of the present invention.
Example 1
In a specific embodiment 1 to which the present invention is applied, dip-guided filtering is performed using OVT domain prestack seismic data to obtain dip-guided filtered data volumes. The purpose of dip angle guiding filtering is to improve the signal to noise ratio of the seismic data, so that the continuous and discontinuous characteristics of the same phase axis of the seismic are more obvious. The basic principle of dip angle guided filtering is formation directional filtering based on dip angle control.
The median filtering after the inclination angle guiding filtering is added with the inclination angle constraint has stronger protection on the imaging of the high and steep stratum, in particular to the protection on the section wave, so the filtering method is more beneficial to the identification of faults and cracks.
And carrying out variance attribute calculation by using the three-dimensional seismic data volume subjected to inclination angle guiding filtering to obtain a variance attribute volume.
And performing coherent attribute calculation by using the three-dimensional seismic data volume subjected to dip angle guiding filtering to obtain a coherent attribute volume.
And obtaining a fracture prediction fusion attribute body through square weighting calculation based on the variance attribute body and the eigenvalue coherence attribute body.
And selecting a drilling calibration fracture development zone in the region, and carrying out amplitude grading on the fracture prediction fusion attribute according to a threshold value of the drilling calibration fracture zone to pick up and predict the volcanic fracture development region.
By using 3D stereo display software, the crack prediction fusion attribute body after amplitude grading is imported, so that the profile and plane distribution characteristics of the crack can be intuitively and clearly predicted.
And obtaining a fracture prediction fusion attribute body based on square weighting calculation of the variance attribute body and the eigenvalue coherent data body. The calculation formula of the fusion attribute body of crack prediction is as follows:
Z i ＝mQ i 2 +nE i 2 12. Fig.
In formula 12, i=1, 2, …, M, Z i Predicting the value of a sampling point in the fusion attribute body for the crack; q (Q) i Is the value of the sample point at the corresponding position in the variance attribute volume. E (E) i Is the value of the sample point at the corresponding position in the eigenvalue coherence attribute volume. m and n are parameter values of data volumes in different areas, and 0, 1 or 2 is generally selected for a certain regional property. The attribute enhances the function of the variance body for describing the microcracks in the volcanic rock, increases the identification factors of eigenvalue coherent large fracture, and can better describe the development characteristics of the reservoir cracks of the volcanic rock than the single attribute.
In the method, when the dip angle guiding median filtering processing is carried out on the pre-stack seismic data of the OVT domain, the method comprises the following steps: defining a time window in the three-dimensional seismic data, the time window comprising 2j+1 sample points; the number of the sample points is required to be smaller than the number of the sample points in the longitudinal time domain of the seismic data body, and the number of the sample points is an odd number, so that 2j+1 sample points are set;
calculating the value of the (j+1) th sample point in the time window by adopting a median filtering method, wherein the calculation formula is as follows
Wherein i=1, 2, …, N;the value of the j+1th sample point in the time window obtained by adopting the median filtering method.
Extracting tilt information is an important prerequisite for tilt-directed filtering. Many common tilt scanning algorithms are used, such as local gradient method, structure tensor method, tilt scanning method, etc. The tilt angle calculation is performed by using a plane wave destructive filtering method. The local plane wave is represented as follows:
where u (x, t) represents the wave field function, x represents the offset or horizontal distance, t is time, σ is the local dip of the airspace continuously; when the formation dip varies along the spatio-temporal direction, adjacent seismic traces may be predicted using local operators.
Under the linear operator representation, the plane wave decomposition operator can be represented as:
d=d×s 3
Wherein s is a seismic trace, D is a residual error, D is a plane wave destructive filter, and D is defined as formula 4:
wherein P is i，j To be the predictor from the ith to the jth lanes, I and P are functions of the local tilt angle sigma i，j M is a column vector. The local dip σ of the seismic trace may be estimated by minimizing the objective function, prediction error d, as shown in equation (5):
where ε is the regularization scaling factor and S is the shaping regularization operator. The inclination angle information obtained by plane destructive filtering is used as the input of median filtering to obtain an inclination angle guiding data body
And (3) calculating variance attribute by using the data volume subjected to the dip angle guiding filtering process, and obtaining variance attribute:
for example, the average value of the amplitudes of the corresponding samples in the time window length of 500ms in each of the 9 channels is obtained first, then the sum of the variances of the amplitudes of each sample in the 9 channels in the time window length and the average value of the amplitudes in the 9 channels at the same time is calculated, finally the weighted value of the sine trigonometric function is multiplied, and normalization is performed, so that the variance value of the sample is obtained.
The variance formula is defined as follows:
omega = sin theta (theta is more than or equal to 0 and less than or equal to 90 degrees; omega is more than or equal to 0 and less than or equal to 1) 8
Delta in t 2 Is the variance value; delta ω 2 (Q) is the weighted variance value; x is x ij The seismic amplitude value of j time i channels; l is the time window length of the variance; i is the number of adjacent tracks needed for calculating the variance value; omega j-t The maximum is 1 and the minimum is 0 as a trigonometric weighting function of a sampling point in a certain time window. By the above formula, the variance value of each sampling point in the whole three-dimensional data body can be calculated, and finally the three-dimensional variance data body is obtained.
The eigenvalue coherence algorithm calculates coherence properties by eigenvalues of covariance matrix based on the second generation algorithm by gersztenkon and Marfurt in 1999. The eigenvalue coherence algorithm is defined as follows:
in formula 9, d KN The K sample value of the N-th channel;
the covariance matrix C of the three-dimensional data volume in the analysis window is calculated according to the formula shown in figure 10,for the K-th row vector in matrix D +.>Which represents the aggregate of the kth sample points of the data volume; d, d k Is->Is to be used in the present invention,
d k ＝[d k1 ,d k2 ,…,d kN ] T
let lambda be k Is the kth eigenvalue, lambda, of the covariance matrix C 1 The maximum characteristic value is shown in the formula 11, and the coherence attribute is calculated according to the formula 11;
lambda in 11 k Is the kth eigenvalue, lambda, of the covariance matrix C m Is the largest of these.
In the method, the fracture zone is calibrated by using drilling, and the determination of the fracture zone threshold comprises the following steps:
and (3) observing the core fracture of the target interval in the zone, performing imaging logging fracture characteristic analysis, and identifying the fracture section possibly developed by the target interval by using the core and the conventional logging fracture section of the imaging logging scale.
And calibrating the earthquake crack prediction fusion attribute by using the information of the drilling crack section, determining the form and the scale of the crack on the earthquake, and determining the amplitude value of the crack section.
And carrying out amplitude grading pickup according to the calibrated minimum amplitude value of the fracture zone, and predicting the fracture development characteristics of the target interval.
Example 2
In a specific embodiment 2 of the present invention, the method for quantitatively predicting the fracture of the volcanic reservoir based on the OVT domain prestack fusion property includes the following steps:
firstly, pre-stack seismic data of an OVT domain in a work area are selected, a target interval time window needing interpretation is set, and dip angle guiding filtering processing is carried out on the pre-stack seismic data by using seismic interpretation software, so that a dip angle guiding filtering data body is obtained.
And respectively extracting variance and coherence attribute data volumes by using the dip angle guiding filtering data volumes.
And calculating to obtain a fracture prediction fusion attribute body based on the variance attribute body and the eigenvalue coherent data body.
The calculation formula of the fusion attribute body of the crack prediction is as follows
Z i ＝mQ i 2 +nE i 2 11. The method of the invention
In formula 11, i=1, 2, …, M, Z i Predicting the value of a sampling point in the fusion attribute body for the crack; q (Q) i Is the value of the sample point at the corresponding position in the variance attribute volume. E (E) i Is the value of the sample point at the corresponding position in the eigenvalue coherence attribute volume. m and n are parameter values of data volumes in different areas, and 0, 1 or 2 is generally selected for a certain regional property.
And selecting a fracture development zone with fusion attribute of drilling calibration in the work area, and carrying out amplitude filtering treatment on the fusion attribute according to a fracture zone drilling calibration threshold value to predict the fracture development area of the volcanic reservoir.
The 3D stereoscopic display software is used for importing the fracture prediction fusion attribute data body, so that the profile characteristics and the plane distribution rules of the volcanic reservoir fracture can be intuitively and clearly observed.
Example 3
In a specific embodiment 3 of the present invention, the present invention takes the example of the stone-North concave carbocoal system with the Pascal basin Wu Lungu depression, the carbocoal volcanic-sediment complex building and development, the volcanic rock building of the developing thick layer, and the identification and description of the volcanic rock reservoir are all the main factors restricting the oil and gas exploration in the area.
The research takes the identification of the cracks of the quasi-North 1 three-dimensional carboloy volcanic rock reservoir as an object to develop attack and close research, effectively utilizes the simplicity and practicability of earthquake attribute prediction cracks, generates new crack prediction fusion attribute by carrying out square weighting calculation on variance attribute and eigenvalue coherence attribute, effectively identifies the cracks of the carboloy volcanic rock reservoir, and has certain guiding significance for advancing carboloy volcanic reservoir description and well position deployment of the area.
The embodiment provides a fracture quantification prediction method (a flow is shown in fig. 1) based on seismic multi-attribute fusion, which comprises the following steps:
step 101, acquiring an OVT domain pre-stack seismic data volume (based on which an original seismic section of a target interval crack can be obtained, as shown in fig. 2) and logging data of a drilling target layer in a seismic work area, and performing horizon and fracture interpretation of the seismic data target layer after well-earthquake calibration, wherein the method is mainly used for providing a top and bottom boundary of a stratum interval for later crack development identification of the target layer and providing evidence for crack development analysis.
And 102, performing dip angle guiding median filtering processing on the OVT domain prestack seismic data body to obtain a dip angle guiding filtering data body.
The purpose of dip angle guiding filtering is to improve the signal to noise ratio of the seismic data, so that the continuous and discontinuous characteristics of the same phase axis of the seismic are more obvious. The basic principle of dip angle guided filtering is formation directional filtering based on dip angle control.
Calculating the value of the (j+1) th sample point in the time window by adopting a median filtering method, wherein the calculation formula is as follows
Wherein i=1, 2, …, N;the value of the j+1th sample point in the time window obtained by adopting the median filtering method.
Extracting tilt information is an important prerequisite for tilt-directed filtering. Many common tilt scanning algorithms are used, such as local gradient method, structure tensor method, tilt scanning method, etc. The tilt angle calculation is performed by using a plane wave destructive filtering method. The local plane wave is represented as follows:
where u (x, t) represents the wave field function, x represents the offset or horizontal distance, t is time, σ is the local dip of the airspace continuously; when the formation dip varies along the spatio-temporal direction, adjacent seismic traces may be predicted using local operators.
Under the linear operator representation, the plane wave decomposition operator can be represented as:
d=d×s 3
Wherein s is a seismic trace, D is a residual error, D is a plane wave destructive filter, and D is defined as formula 4:
wherein P is i，j To be the predictor from the ith to the jth lanes, I and P are functions of the local tilt angle sigma i，j M is a column vector. The local dip σ of the seismic trace may be estimated by minimizing the objective function, prediction error d, as shown in equation (5):
where ε is the regularization scaling factor and S is the shaping regularization operator. And adopting inclination angle information obtained by plane destructive filtering as the input of median filtering to obtain an inclination angle guiding data body. Based on dip guided filtered seismic profile, as shown in FIG. 3
Step 103, performing variance attribute calculation by using the data volume after the dip angle guide filtering processing to obtain a variance volume attribute Q:
for example, the average value of the amplitudes of the corresponding samples in the time window length of 500ms in each of the 9 channels is obtained first, then the sum of the variances of the amplitudes of each sample in the 9 channels in the time window length and the average value of the amplitudes in the 9 channels at the same time is calculated, finally the weighted value of the sine trigonometric function is multiplied, and normalization is performed, so that the variance value of the sample is obtained.
The variance formula is defined as follows:
omega = sin theta (theta is more than or equal to 0 and less than or equal to 90 degrees; omega is more than or equal to 0 and less than or equal to 1) 8
Delta in t 2 Is the variance value; delta ω 2 (Q) is the weighted variance value; x is x ij The seismic amplitude value of j time i channels; l is the time window length of the variance; i is the number of adjacent tracks needed for calculating the variance value; omega j-t The maximum is 1 and the minimum is 0 as a trigonometric weighting function of a sampling point in a certain time window. By the above formula, the variance value of each sampling point in the whole three-dimensional data body can be calculated, and finally the three-dimensional variance data body is obtained.
In the step, the results shown in the figures 4 and 5 can be obtained by predicting the carbocoal fracture based on the variance body, wherein the figures 4 and 5 are respectively a target layer C obtained based on a coherent algorithm 1 j 2 Segment formation variance body fracture prediction planes and profiles.
104, eigenvalue coherence attribute calculation is carried out on the three-dimensional seismic data volume after dip angle guiding filtration, and an eigenvalue coherence attribute volume E is obtained:
in the process 1, the three-dimensional seismic data body after the dip angle guiding filtering process is taken as an analysis window, wherein the analysis window contains n=9 channels of seismic data and k=9 sampling points, and then the three-dimensional seismic data body after the dip angle guiding filtering process can be expressed as a matrix D shown in formula 9:
in formula 9, d KN The K sample value of the N-th channel;
process 2, K-th row vector in matrix DWhich represents the set of kth sample points of the data volume; the covariance matrix C of the entire denoised three-dimensional seismic data volume can be expressed as:
procedure 3, set lambda k Is the kth eigenvalue, lambda, of the covariance matrix C 1 The calculation formula of the coherence attribute is shown as formula 11, and the coherence attribute body E is calculated according to the formula of formula 11:
in the step, the eigenvalue-based coherent algorithm is extracted from the interpretation system for the carboy C 1 j 2 Prediction of the interval of interest may yield the graphs of fig. 6 and 7; wherein, figure 6 and figure 7 are respectively the carbolines C obtained based on eigenvalue coherent algorithm 1 j 2 Section and plane of formation fracture prediction.
The attribute of the variance body is more clear in the description of microcracks in the volcanic rock (fig. 4 and 5); eigenvalue coherence allows for a clear identification of the development of large breaks (fig. 6 and 7). The single attribute can identify the development of the crack, but the requirements of clearly identifying the morphological characteristics of the crack and characterizing the planar distribution of the volcanic rock crack are not met. How does the morphology and planar distribution of volcanic cracks in a region finely delineated?
Step 105, in the interpretation system, the variance attribute volume and eigenvalue coherence attribute are fused through volume calculation information to generate a new fracture prediction fusion attribute volume (as shown in formula 11). The fusion attribute body Z has the advantages of sensitive prediction and capability of realizing quantitative prediction of the crack morphology.
Z i ＝mQ i 2 +nE i 2 11. The method of the invention
In formula 11, i=1, 2, …, M, Z i Predicting the value of a sampling point in the fusion attribute body for the crack; q (Q) i Is the value of the sample point at the corresponding position in the variance attribute volume. m and n are parameter values of data volumes in different areas, and 0, 1 or 2 is generally selected for a certain regional property. The local area passes the multiparameter test with m=2 and n=1.
And 106, predicting the space distribution information of the carbon-stone-based cracks by using the finally obtained crack prediction fusion attribute body Z to obtain a prediction result (shown in figure 8).
And 107, performing in-zone drilling analysis, wherein oblique joints and vertical joints are found in the quasi-north 1-well carboloy 4164.6m green gray flash rock in the zone. At the same time, severe mud micro-cracks are seen on the imaging log of the section, indicating the development of the well section cracks. Conventional well logging is performed by utilizing the core and imaging logging data, the acoustic time difference of the well section is increased, and the well section has a characteristic of micro amplitude cycle jump, so that the crack of the non-core section is interpreted, and three crack development sections are interpreted in the quasi-north 1 well 4153.6-4169m,4189.6-4198.2m and 4236-4243m (shown in figure 9). Indicating that the regional carbon-based fracture reservoir develops.
Step 108, carrying out well-shock comprehensive calibration by aiming at the north 1 well through the synthesis record, and utilizing the drilling crack to scale the seismic crack characteristics and the crack development segment Cheng Chuan bead reflection characteristics. Beaded reflection is considered to be a cross-sectional reflective feature of a crack. The minimum amplitude value of the fracture zone was located at 4.8e+14. After the amplitude grading display (only the amplitude value larger than 4.8E+14 is displayed), the development scale, distribution form, size and density of the cracks can be intuitively and clearly observed on the section (figure 10).
Step 109 uses 3D display software to display C 1 j 2 Bottom T 0 Superimposed with the fusion properties (after amplitude grading) shows a planar prediction of the fracture reservoir (fig. 11).
As can be seen from the figure, the North stone pit carboloy C 1 j 2 The section stratum cracks develop in a bead shape, and the volcanic development area has larger crack density. The development of cracks is consistent with the eruption period of volcanic rock. And the crack density near the large fracture zone is large, and the cracks far away from the fracture zone are less.
Comparing FIGS. 4-7 with FIGS. 10 and 11, it can be seen that the carbon system C predicted based on variance body attributes 1 j 2 The section cracks, the micro cracks in the volcanic rock are clearly delineated, and only the range and the characteristics of the cracks can be qualitatively predicted; the development of large fracture can be well predicted based on eigenvalue coherence properties, but the internal cracks of volcanic rock cannot be clearly predicted. The carboloy C obtained by the method of the embodiment of the invention 1 j 2 The section stratum fracture prediction result can clearly and intuitively predict the development occurrence, size and plane distribution characteristics of the volcanic reservoir fracture.
Applicability of the technique: the volcanic reservoir cracks are further analyzed on the basis of identifying the volcanic favorable areas by combining the conventional profile, the variance profile and the coherence profile, and finally the development areas of the volcanic reservoir cracks can be predicted on a plane.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but although the present invention has been described in detail with reference to the foregoing embodiment, it will be apparent to those skilled in the art that modifications may be made to the technical solution described in the foregoing embodiment, or equivalents may be substituted for some of the technical features thereof. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Other than the technical features described in the specification, all are known to those skilled in the art.
Claims (13)
1. The quantitative prediction method for the volcanic reservoir cracks based on the OVT domain prestack fusion attribute is characterized by comprising the following steps of:
step 1, pre-stack seismic data of an OVT domain in a work area are selected to obtain an inclination angle guiding filtering data body;
step 2, respectively extracting variance and coherence attribute data volumes by using the dip angle guiding filtering data volumes;
step 3, calculating to obtain a fracture prediction fusion attribute body based on the variance attribute body and the eigenvalue coherent data body;
and 4, selecting a fracture development zone with fusion attribute of drilling calibration in the work area, and carrying out amplitude filtering treatment of the fusion attribute according to a fracture zone drilling calibration threshold value to predict a volcanic reservoir fracture development area.
2. The quantitative prediction method for the volcanic reservoir cracks based on the OVT domain prestack fusion attribute according to claim 1 is characterized in that in step 1, OVT domain prestack seismic data in a work area are selected, a target interval time window needing interpretation is set, dip angle guiding filtering processing is conducted by using the OVT domain prestack seismic data, and a dip angle guiding filtered data body is obtained.
3. The quantitative prediction method for the volcanic reservoir cracks based on the OVT domain prestack fusion attribute according to claim 2, wherein in step 1, when the dip angle guiding median filtering processing is performed on the OVT domain prestack seismic data, the method comprises the following steps:
defining a time window in the three-dimensional seismic data, the time window comprising 2j+1 sample points; the number of the sample points is required to be smaller than the number of the sample points in the longitudinal time domain of the seismic data body, and the number of the sample points is an odd number, so that 2j+1 sample points are set;
calculating the value of the (j+1) th sample point in the time window by adopting a median filtering method, wherein the calculation formula is as follows
Wherein i=1, 2, …, N;the value of the (j+1) th sample point in the time window obtained by adopting a median filtering method;
the extraction of the dip angle information is an important precondition of dip angle guiding filtering, the dip angle calculation is carried out by using a plane wave destructive filtering method, and the local plane waves are expressed as follows:
where u (x, t) represents the wave field function, x represents the offset or horizontal distance, t is time, σ is the local dip of the airspace continuously; when the formation dip varies along the spatio-temporal direction, adjacent seismic traces may be predicted using local operators.
4. A method for quantitative prediction of reservoir cracks based on OVT domain prestack fusion properties according to claim 3, characterized in that in step 1, the plane wave decomposition operator can be expressed as:
d=d×s 3
Wherein s is a seismic trace, D is a residual error, D is a plane wave destructive filter, and D is defined as formula 4:
wherein P is i，j To be the predictor from the ith to the jth lanes, I and P are functions of the local tilt angle sigma i，j Is an M-column vector; the local dip σ of the seismic trace may be estimated by minimizing the objective function, prediction error d, as shown in equation (5):
wherein epsilon is a regularized scaling coefficient and S is a shaping regularization operator; and using the inclination angle information obtained by the plane destructive filtering as the input of the median filtering to obtain an inclination angle guiding filtering data body.
5. The quantitative prediction method for the reservoir cracks of the volcanic based on the OVT domain prestack fusion attribute according to claim 1, wherein in step 2, variance attribute calculation is performed by using the three-dimensional seismic data volume subjected to dip angle guiding filtering, and a variance attribute volume is obtained.
6. The method for quantitatively predicting the volcanic reservoir cracks based on the OVT domain prestack fusion attribute according to claim 5, wherein in the step 2, when the variance attribute is calculated by utilizing the three-dimensional seismic data body subjected to the dip angle guiding filtering treatment, n+1 channels are added to n channels adjacent to the periphery, the sampling point is taken as the center, the number of the sampling points in the upper half time window length and the lower half time window length is assumed to be 1ms, the time window length is L=500 ms, the average value of the amplitudes of the corresponding sampling points in the time window length of 500ms in the n+1 channels is firstly calculated, the sum of the variances of the amplitudes of the sampling points in the time window length n+1 channels and the average value of the amplitudes in the n+1 channels at the same time is calculated, and finally the weighted value of the sine trigonometric function is multiplied, and normalization is carried out, so that the variance value of the sampling point is obtained.
7. The method for quantitative prediction of volcanic reservoir cracks based on OVT domain prestack fusion properties according to claim 6, wherein in step 2, the variance formula is defined as follows:
omega = sin theta (theta is more than or equal to 0 and less than or equal to 90 degrees; omega is more than or equal to 0 and less than or equal to 1) 8
Delta in t 2 Is the variance value; delta ω 2 (Q) is the weighted variance value; x is x ij The seismic amplitude value of j time i channels; l is the time window length of the variance; i is the number of adjacent tracks needed for calculating the variance value; omega j-t A triangle weighting function for a certain sampling point in a certain time window is 1 at maximum and 0 at minimum; by the above formula, the variance value of each sampling point in the whole three-dimensional data body can be calculated, and finally the three-dimensional variance data body is obtained.
8. The quantitative prediction method for the reservoir cracks of the volcanic based on the OVT domain prestack fusion attribute according to claim 1, wherein in step 2, coherent attribute calculation is performed by using a three-dimensional seismic data volume subjected to dip angle guiding filtering, so as to obtain a coherent attribute volume.
9. The quantitative prediction method of volcanic reservoir cracks based on the OVT domain prestack fusion attribute according to claim 8, wherein in the step 2, coherence attribute calculation is performed by using a three-dimensional seismic data body subjected to dip angle guided filtering, and eigenvalue coherence algorithm calculates coherence attribute through eigenvalues of covariance matrix; the eigenvalue coherence algorithm is defined as follows:
in formula 9, d KN The K sample value of the N-th channel;
the covariance matrix C of the three-dimensional data volume in the analysis window is calculated according to the formula shown in figure 10,for the K-th row vector in matrix D +.>Which represents the aggregate of the kth sample points of the data volume; d, d k Is->Is to be used in the present invention,
d k ＝[d k1 ，d k2 ，…，d kN ] T
let lambda be k Is the kth eigenvalue, lambda, of the covariance matrix C 1 The maximum characteristic value is shown in the formula 11, and the coherence attribute is calculated according to the formula 11;
lambda in 11 k Is the kth eigenvalue, lambda, of the covariance matrix C m Is the largest of these.
10. The quantitative prediction method for the reservoir cracks based on the OVT domain prestack fusion attribute volcanic according to claim 1, wherein in step 3, the crack prediction fusion attribute is obtained through square weighting calculation based on a variance attribute and an eigenvalue coherence attribute.
11. The method for quantitatively predicting the fracture of the volcanic reservoir based on the OVT domain prestack fusion attribute according to claim 10, wherein in step 3, a calculation formula of the fracture prediction fusion attribute body is as follows:
Z i ＝mQ i 2 +nE i 2 12. Fig.
In formula 12, i=1, 2, …, M, Z i Predicting the value of a sampling point in the fusion attribute body for the crack;Q i values of sampling points at corresponding positions in the variance attribute body; e (E) i Values of sampling points at corresponding positions in the eigenvalue coherence attribute volume; m and n are parameter values of data volumes in different areas.
12. The method for quantitative prediction of volcanic reservoir cracks based on OVT domain prestack fusion properties according to claim 1, wherein in step 4, calibrating the fracture zone with the well drilling, determining the fracture zone threshold value comprises:
through the core fracture observation and imaging logging fracture characteristic analysis of the target interval in the zone, and the conventional logging fracture section of the core and imaging logging scale, the fracture section possibly developed by the target interval is identified;
calibrating the earthquake crack prediction fusion attribute by using the information of the drilling crack section, determining the form and the scale of the crack on the earthquake, and determining the amplitude value of the crack section;
and carrying out amplitude grading pickup according to the calibrated minimum amplitude value of the fracture zone, and predicting the fracture development characteristics of the target interval.
13. The quantitative prediction method for the reservoir cracks based on the OVT domain prestack fusion attribute volcanic according to claim 1, further comprising the step of introducing an amplitude-graded crack prediction fusion attribute body by using 3D stereoscopic display software after the step 4 to predict the profile and plane distribution characteristics of the crack.
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