CN111158045B - Reservoir transformation microseism event scattered point clustering analysis method and system - Google Patents

Reservoir transformation microseism event scattered point clustering analysis method and system Download PDF

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CN111158045B
CN111158045B CN202010012257.4A CN202010012257A CN111158045B CN 111158045 B CN111158045 B CN 111158045B CN 202010012257 A CN202010012257 A CN 202010012257A CN 111158045 B CN111158045 B CN 111158045B
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CN111158045A (en
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刁瑞
尚新民
张云银
芮拥军
隆文韬
柳光华
揭景荣
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China Petroleum and Chemical Corp
Geophysical Research Institute of Sinopec Shengli Oilfield Co
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Geophysical Research Institute of Sinopec Shengli Oilfield Co
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    • G01—MEASURING; TESTING
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    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/288—Event detection in seismic signals, e.g. microseismics

Abstract

The invention provides a scattered point clustering analysis method and a scattered point clustering analysis system for micro-seismic events in oil reservoir reconstruction, wherein the method comprises the following steps: step 1, acquiring micro-seismic event scatter points of oil reservoir reconstruction in real time; step 2, calculating error ellipses of all microseism events; step 3, performing weighting operation according to the moment magnitude of the microseism event; step 4, calculating the geometric center of the microseism event within the error ellipse range; and 5, minimizing the objective function through continuous iteration, and accurately describing and finely depicting the artificial fracture for oil reservoir reconstruction. The method and the system for analyzing the scattered point clustering of the micro-seismic events for oil reservoir transformation can effectively evaluate the oil reservoir transformation effect and guide the optimization and perfection of the transformation process, and have an important effect on the efficient exploration and development of unconventional oil and gas resources.

Description

Reservoir transformation microseism event scattered point clustering analysis method and system
Technical Field
The invention relates to the technical field of micro-seismic monitoring for petroleum and natural gas reservoir transformation, in particular to a scattered point clustering analysis method and a scattered point clustering analysis system for micro-seismic events of reservoir transformation.
Background
The oil reservoir transformation operations such as hydraulic fracturing, water injection, gas injection, heavy oil thermal recovery and the like can induce micro-seismic events, and the micro-seismic activities have important influence on the exploration and development of unconventional oil and gas fields. In order to effectively evaluate and optimize oil and gas yield increasing operation measures, the oil reservoir transformation effect needs to be rapidly and timely mastered, so that the process technology and the transformation parameters are optimized, and micro-seismic monitoring is one of the most effective technologies for evaluating the oil reservoir transformation effect. At present, due to the problems of longitudinal wave (P wave) or transverse wave (S wave) first arrival time error, uncertainty of a velocity model, source vector angle error, unsatisfactory space-time distribution of a detector and the like of a microseism event, the reliability of a microseism monitoring result is low, the error is large, the problem of divergence of microseism event points exists, the framework structure of an artificial fracture for oil deposit reconstruction is difficult to effectively extract, and the popularization and the application of a microseism monitoring technology in unconventional oil and gas exploration and development are restricted. Therefore, a novel method and a novel system for analyzing scattered point clustering of microseism events in oil reservoir transformation are invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide a scattered point clustering analysis method and a scattered point clustering analysis system for micro-seismic events of oil reservoir reconstruction, which can quickly acquire an artificial fracture skeleton of oil reservoir reconstruction through a scattered point clustering analysis method and clearly display dynamic distribution and real-time change conditions of the artificial fracture skeleton of oil reservoir reconstruction.
The object of the invention can be achieved by the following technical measures: the scattered point cluster analysis method for the oil reservoir reconstruction micro-seismic events comprises the following steps: step 1, acquiring micro-seismic event scatter points of oil reservoir reconstruction in real time; step 2, calculating error ellipses of all microseism events; step 3, performing weighting operation according to the moment magnitude of the microseism event; step 4, calculating the geometric center of the microseism event within the error ellipse range; and 5, minimizing the objective function through continuous iteration, and accurately describing and finely depicting the artificial fracture for oil reservoir reconstruction.
The object of the invention can also be achieved by the following technical measures:
in step 1, the microseism event is obtained by positioning a plurality of detectors arranged in a well or on the ground to record waveforms, and comprises the occurrence time, the spatial position and the seismic source parameter information of the microseism event.
In step 2, error ellipses of all the micro-seismic event scatter points are calculated and used as constraint conditions of scatter point cluster analysis, namely as a movable geometric range of the micro-seismic event.
In step 2, calculating an error ellipse after acquiring the microseism scatter points, wherein the error ellipse calculation formula of the microseism event scatter points is as follows:
Figure BDA0002356356900000021
wherein: equation of time difference r with sigma value as residuei＝ti-t0-Ti(x0,y0,z0) Variance of partial differential matrix, t0Is the initial time of occurrence of the microseismic event, (x)0,y0,z0) Is the spatial position of the occurrence of the microseism event, i is the serial number of the detector, i is 1,2, …, N is the total number of the detectors, tiIs the observed microseismic event arrival time, T, of the ith geophoneiIs the theoretical analog arrival time of the ith detector; the B value is represented by the residual time difference equation ri＝ti-t0-Ti(x0,y0,z0) Calculating a partial differential matrix;
Figure BDA0002356356900000022
is the χ with confidence coefficient of alpha and degree of freedom of beta2Distribution, α is the confidence parameter, β is the degree of freedom parameter, i.e. the number of independent variables, χ2Is a chi-square distribution.
In step 2, the value B in the error ellipse calculation formula is represented by the residual time difference equation ri＝ti-t0-Ti(x0,y0,z0) Is defined by the partial differential matrix a, the operational formula is as follows:
B＝ATA
wherein:
Figure BDA0002356356900000023
is formed by the equation of residual time difference ri＝ti-t0-Ti(x0,y0,z0) Is calculated from the partial differential matrix of (a),
Figure BDA0002356356900000024
is the partial differential of the theoretical simulated time function T; a. theTIs the transpose of matrix a.
In step 2, the sigma value in the error ellipse calculation formula is represented by the residual time difference equation ri＝ti-t0-Ti(x0,y0,z0) Is defined by the variance of the partial differential matrix a, the formula is as follows:
Figure BDA0002356356900000031
wherein: r isiIs residual time, i ═ 1,2, …, N; n is the total number of detectors.
In step 3, weighting operation is carried out according to the moment magnitude of the microseism event, the weight is obtained by calculating the moment magnitude of the microseism event, and the specific calculation formula is as follows:
Mw＝a·log10(M0)+b
wherein: a is a constant factor, taking value 2/3; b is a constant factor, and takes the value of-6; m0The seismic moment is determined by the amplitude low-frequency component of the seismic wave, and reflects the size of the fracture energy at the seismic source point, and the larger the fault area is, the larger the energy of the excited long-period seismic wave is, and the longer the period is.
In step 4, an Euclidean distance algorithm is adopted for analysis, whether the microseism event scatter point can move to the geometric center in the error ellipse range is judged, and the Euclidean distance is calculated through the following formula:
Figure BDA0002356356900000032
wherein: (xx)j,yyj,zzj) Is the spatial three-dimensional coordinate of the microseismic event scatter point in the error ellipse; (xx)0,yy0,zz0) Is the geometric center three-dimensional coordinate of the error ellipse; m is the number of microseismic event scatter points.
The object of the invention can also be achieved by the following technical measures:
the microseism incident scatter cluster analysis system is reformed transform to oil reservoir, and this microseism incident scatter cluster analysis system is reformed transform to oil reservoir includes: a microseismic event acquisition module: acquiring a microseism event, wherein the microseism event is obtained by positioning waveforms recorded by a plurality of detectors distributed in a well or on the ground, and the dispersion point of the microseism event is positioned in the oil reservoir transformation range and comprises the occurrence time, the spatial position and the seismic source point parameter information of the microseism event; an error ellipse calculation module: calculating an error ellipse of a scattered point of the microseism event, and taking the error ellipse as a constraint condition of scattered point clustering analysis, namely as a movable geometric range of the microseism event; moment-magnitude weighting module: calculating the weighting weight of the dispersion point of the microseism event, wherein the value of the weighting weight is obtained by calculating the moment magnitude of the microseism event; an error ellipse judgment module: calculating the geometric center of the microseism event in the error ellipse range, analyzing by adopting an Euclidean distance algorithm, and judging whether the microseism event can move to the geometric center in the error ellipse range; a microseismic event cluster iteration module: and minimizing the objective function through continuous iteration, and accurately describing and finely depicting the artificial fracture for oil reservoir reconstruction.
According to the scattered point cluster analysis method and system for the oil reservoir reconstruction micro-seismic events, scattered point cluster analysis is carried out on a micro-seismic event point set, and through the application of the method and system, the framework structure of the oil reservoir reconstruction artificial fracture can be rapidly obtained, and the dynamic spreading and real-time change conditions of the oil reservoir reconstruction artificial fracture framework can be clearly displayed, so that the reliability of the oil reservoir reconstruction micro-seismic monitoring result is greatly improved, and the function of the micro-seismic monitoring result in improving the oil gas recovery ratio is exerted. The scattered point clustering analysis method and the scattered point clustering analysis system for the micro-seismic events in oil reservoir reconstruction solve the problem of divergence of the micro-seismic events caused by P-wave or S-wave first arrival time-arrival errors, uncertainty of a velocity model, source vector angle errors and unsatisfactory space-time distribution of a detector. This patent can draw oil reservoir transformation artificial crack skeleton fast, and the clear artificial crack skeleton of showing oil reservoir transformation is opened up and is distributed and the situation of change to improve the reliability of oil reservoir transformation microseism monitoring result by a wide margin, the effect of performance microseism monitoring result.
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FIG. 1 is a flow chart of an embodiment of a method for reservoir reformation microseismic event scatter cluster analysis of the present invention;
FIG. 2 is a block diagram of an embodiment of a reservoir reformation microseismic event scatter cluster analysis system of the present invention.
Detailed Description
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
As shown in fig. 1, fig. 1 is a flow chart of a reservoir reformation microseismic event scatter cluster analysis method of the present invention.
In step 101, the dispersion point of the micro-seismic event of oil reservoir reconstruction is obtained in real time, wherein the micro-seismic event is obtained by positioning the recording waveforms of a plurality of detectors arranged in a well or on the ground and comprises the occurrence time, the spatial position and the seismic source parameter information of the micro-seismic event.
Reservoir reformation over a period of time may produce tens or hundreds of microseismic events that are distributed over the reformed formation. The micro-seismic event occurrence time can reflect the opening sequence of the artificial cracks, the micro-seismic event space position can reflect the expansion condition and the spreading characteristic of the artificial cracks, and the micro-seismic event seismic source parameters can reflect the opening and sliding characteristics of the artificial cracks.
In step 102, error ellipses of all micro-seismic event scatter points are calculated and used as constraint conditions of scatter point cluster analysis, namely as a geometric range in which the micro-seismic events can move.
Calculating an error ellipse after acquiring the microseism scatter points, wherein the error ellipse calculation formula of the microseism event scatter points is as follows:
Figure BDA0002356356900000051
wherein: equation of time difference r with sigma value as residuei＝ti-t0-Ti(x0,y0,z0) Variance of partial differential matrix, t0Is the initial moment of occurrence of a microseismic event，(x0,y0,z0) Is the spatial position of the occurrence of the microseism event, i is the serial number of the detector, i is 1,2, …, N is the total number of the detectors, tiIs the observed microseismic event arrival time, T, of the ith geophoneiIs the theoretical analog arrival time of the ith detector; the B value is represented by the residual time difference equation ri＝ti-t0-Ti(x0,y0,z0) Calculating a partial differential matrix;
Figure BDA0002356356900000052
is the χ with confidence coefficient of alpha and degree of freedom of beta2Distribution, α is the confidence parameter, β is the degree of freedom parameter, i.e. the number of independent variables, χ2Is a chi-square distribution.
The value B in the error ellipse calculation formula is represented by the residual time difference equation ri＝ti-t0-Ti(x0,y0,z0) Is defined by the partial differential matrix a, the operational formula is as follows:
B＝ATA
wherein:
Figure BDA0002356356900000053
is formed by the equation of residual time difference ri＝ti-t0-Ti(x0,y0,z0) Is calculated from the partial differential matrix of (a),
Figure BDA0002356356900000054
is the partial differential of the theoretical simulated time function T; a. theTIs the transpose of matrix a.
Residual time difference equation r of sigma value in error ellipse calculation formulai＝ti-t0-Ti(x0,y0,z0) Is defined by the variance of the partial differential matrix a, the formula is as follows:
Figure BDA0002356356900000061
wherein: r isiIs residual time, i ═ 1,2, …, N; n is the total number of detectors.
At step 103, a weighting operation is performed based on the magnitude of the microseismic event. The weight is obtained by calculating the moment magnitude of the microseismic event, and the specific calculation formula is as follows:
Mw＝a·log10(M0)+b
wherein: a is a constant factor, taking value 2/3; b is a constant factor, and takes the value of-6; m0The seismic moment is determined by the amplitude low-frequency component of the seismic wave, and reflects the size of the fracture energy at the seismic source point, and the larger the fault area is, the larger the energy of the excited long-period seismic wave is, and the longer the period is.
In step 104, the geometric center of the microseismic event within the error ellipse is calculated, and the euclidean distance algorithm is used for analysis to determine whether the microseismic event scatter points can move towards the geometric center within the error ellipse.
The euclidean distance is calculated by the following formula:
Figure BDA0002356356900000062
wherein: (xx)j,yyj,zzj) Is the spatial three-dimensional coordinate of the microseismic event scatter point in the error ellipse; (xx)0,yy0,zz0) Is the geometric center three-dimensional coordinate of the error ellipse; m is the number of microseismic event scatter points.
In step 105, through continuous optimization iteration of a nonlinear optimization algorithm, the optimization method enables the objective function value to be minimum, and finally the skeleton of the artificial fracture is generated through calculation, so that accurate description and fine drawing of the skeleton of the artificial fracture for oil reservoir reconstruction are achieved.
The nonlinear optimization algorithm is the combination of the least square optimization algorithm and the particle swarm optimization algorithm, has the advantages of fast convergence of a target function, high operation efficiency and small iteration error, and can meet the requirement of real-time dynamic analysis of artificial fractures in an oil reservoir reconstruction field.
A reservoir reforming micro-seismic event scatter cluster analysis system, as shown in fig. 2, comprising the following modules:
(1) a microseismic event acquisition module: the microseism event is obtained by positioning the waveform recorded by a plurality of detectors arranged in a well or on the ground, and the dispersion point of the microseism event is positioned in the oil reservoir transformation range and comprises information such as the occurrence time, the spatial position, the seismic source point parameter and the like of the microseism event;
(2) an error ellipse calculation module: the method comprises the steps of calculating an error ellipse of a microseism event scatter point, and using the error ellipse as a constraint condition of scatter point clustering analysis, namely as a movable geometric range of the microseism event;
(3) moment-magnitude weighting module: the weighting weight is used for calculating the scatter point of the microseism event, and the value of the weighting weight is calculated by the moment magnitude of the microseism event;
(4) an error ellipse judgment module: calculating the geometric center of the microseism event in the error ellipse range, analyzing by adopting an Euclidean distance algorithm, and judging whether the microseism event can move to the geometric center in the error ellipse range;
(5) a microseismic event cluster iteration module: the objective function is minimized through continuous iteration, so that the generated microseism event clusters are gathered and compacted as much as possible, the purpose of obtaining an artificial fracture framework is achieved, and accurate description and fine drawing of the oil reservoir reconstruction artificial fracture are achieved.
In the embodiment of the invention, the scattered-point clustering analysis method and the scattered-point clustering analysis system for the micro-seismic events of oil reservoir reconstruction are provided, the framework shape of the artificial fracture of oil reservoir reconstruction can be rapidly and clearly displayed, and the real-time analysis capability of the dynamic extension of the artificial fracture of oil reservoir reconstruction is greatly improved.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. The method for analyzing the scattered point clustering of the oil reservoir reconstruction micro-seismic events is characterized by comprising the following steps of:
step 1, acquiring micro-seismic event scatter points of oil reservoir reconstruction in real time;
step 2, calculating error ellipses of all microseism events;
step 3, performing weighting operation according to the moment magnitude of the microseism event;
step 4, calculating the geometric center of the microseism event within the error ellipse range;
step 5, minimizing the objective function through continuous iteration, and accurately describing and finely depicting the artificial fracture for oil reservoir reconstruction;
in step 2, calculating an error ellipse after acquiring the microseism scatter points, wherein the error ellipse calculation formula of the microseism event scatter points is as follows:
Figure FDA0003318583350000011
wherein: equation of time difference r with sigma value as residuei＝ti-t0-Ti(x0,y0,z0) Variance of partial differential matrix, t0Is the initial time of occurrence of the microseismic event, (x)0,y0,z0) Is the spatial position of the occurrence of the microseism event, i is the serial number of the detector, i is 1,2, …, N is the total number of the detectors, tiIs the observed microseismic event arrival time, T, of the ith geophoneiIs the theoretical analog arrival time of the ith detector; the B value is represented by the residual time difference equation ri＝ti-t0-Ti(x0,y0,z0) Calculating a partial differential matrix;
Figure FDA0003318583350000012
is the χ with confidence coefficient of alpha and degree of freedom of beta2Distribution, α is the confidence parameter, β is the degree of freedom parameter, i.e. the number of independent variables, χ2Is distributed in chi fang;
the value B in the error ellipse calculation formula is represented by the residual time difference equation ri＝ti-t0-Ti(x0,y0,z0) Is defined by the partial differential matrix a, the operational formula is as follows:
B＝ATA
wherein:
Figure FDA0003318583350000021
is formed by the equation of residual time difference ri＝ti-t0-Ti(x0,y0,z0) Is calculated from the partial differential matrix of (a),
Figure FDA0003318583350000022
is the partial differential of the theoretical simulated time function T; a. theTIs the transpose of partial differential matrix a;
residual time difference equation r of sigma value in error ellipse calculation formulai＝ti-t0-Ti(x0,y0,z0) Is defined by the variance of the partial differential matrix a, the formula is as follows:
Figure FDA0003318583350000023
wherein: r isiIs residual time, i ═ 1,2, …, N; n is the total number of detectors.
2. The method of claim 1, wherein in step 1, the microseismic events are obtained by positioning the recording waveforms of a plurality of detectors deployed in the well or on the ground, including the occurrence time, spatial position and source parameter information of the microseismic events.
3. The method of claim 1, wherein in step 2, the error ellipses of all the microseismic event scatters are calculated and used as constraints of the scatterer cluster analysis, namely, as the movable geometric range of the microseismic event.
4. The method for performing scattered clustering analysis on the microseismic events for oil reservoir reconstruction as claimed in claim 1, wherein in step 3, the weighting operation is performed according to the moment magnitude of the microseismic events, the weighting is obtained by calculating the moment magnitude of the microseismic events, and the specific calculation formula is as follows:
Mw＝a·log10(M0)+b
wherein: a is a constant factor, taking value 2/3; b is a constant factor, and takes the value of-6; m0The seismic moment is determined by the amplitude low-frequency component of the seismic wave, and reflects the size of the fracture energy at the seismic source point, and the larger the fault area is, the larger the energy of the excited long-period seismic wave is, and the longer the period is.
5. The method of claim 1, wherein in step 4, the method uses Euclidean distance algorithm to determine whether the microseismic event scatter moves towards the geometric center within the error ellipse, and the Euclidean distance is calculated by the following formula:
Figure FDA0003318583350000031
wherein: (xx)j,yyj,zzj) Is the spatial three-dimensional coordinate of the microseismic event scatter point in the error ellipse; (xx)0,yy0,zz0) Is the geometric center three-dimensional coordinate of the error ellipse; m is the number of microseismic event scatter points.
6. Little earthquake event scatter cluster analysis system is reformed transform to oil reservoir, its characterized in that, this little earthquake event scatter cluster analysis system of oil reservoir transformation includes:
a microseismic event acquisition module: acquiring a microseism event, wherein the microseism event is obtained by positioning waveforms recorded by a plurality of detectors distributed in a well or on the ground, and the dispersion point of the microseism event is positioned in the oil reservoir transformation range and comprises the occurrence time, the spatial position and the seismic source point parameter information of the microseism event;
an error ellipse calculation module: calculating an error ellipse of a scattered point of the microseism event, and taking the error ellipse as a constraint condition of scattered point clustering analysis, namely as a movable geometric range of the microseism event;
moment-magnitude weighting module: calculating the weighting weight of the dispersion point of the microseism event, wherein the value of the weighting weight is obtained by calculating the moment magnitude of the microseism event;
an error ellipse judgment module: calculating the geometric center of the microseism event in the error ellipse range, analyzing by adopting an Euclidean distance algorithm, and judging whether the microseism event can move to the geometric center in the error ellipse range;
a microseismic event cluster iteration module: minimizing a target function through continuous iteration, and accurately describing and finely depicting the artificial fracture for oil reservoir reconstruction;
the error ellipse calculation formula of the microseism event scatter point is as follows:
Figure FDA0003318583350000041
wherein: equation of time difference r with sigma value as residuei＝ti-t0-Ti(x0,y0,z0) Variance of partial differential matrix, t0Is the initial time of occurrence of the microseismic event, (x)0,y0,z0) Is the spatial position of the occurrence of the microseism event, i is the serial number of the detector, i is 1,2, …, N is the total number of the detectors, tiIs the observed microseismic event arrival time, T, of the ith geophoneiIs the theoretical analog arrival time of the ith detector; the B value is represented by the residual time difference equation ri＝ti-t0-Ti(x0,y0,z0) Calculating a partial differential matrix;
Figure FDA0003318583350000042
is the χ with confidence coefficient of alpha and degree of freedom of beta2Distribution, α is the confidence parameter, β is the degree of freedom parameter, i.e. the number of independent variables, χ2Is distributed in chi fang;
the value B in the error ellipse calculation formula is represented by the residual time difference equation ri＝ti-t0-Ti(x0,y0,z0) Is defined by the partial differential matrix a, the operational formula is as follows:
B＝ATA
wherein:
Figure FDA0003318583350000051
is formed by the equation of residual time difference ri＝ti-t0-Ti(x0,y0,z0) Is calculated from the partial differential matrix of (a),
Figure FDA0003318583350000052
is the partial differential of the theoretical simulated time function T; a. theTIs the transpose of partial differential matrix a;
residual time difference equation r of sigma value in error ellipse calculation formulai＝ti-t0-Ti(x0,y0,z0) Is defined by the variance of the partial differential matrix a, the formula is as follows:
Figure FDA0003318583350000053
wherein: r isiIs residual time, i ═ 1,2, …, N; n is the total number of detectors.
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CN107479093A (en) * 2017-09-18 2017-12-15 中南大学 A kind of micro-seismic event denoising and clustering method based on potential function

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