CN112180439A - Microseismic event detection method and system optimized by utilizing convex function - Google Patents

Microseismic event detection method and system optimized by utilizing convex function Download PDF

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Publication number
CN112180439A
CN112180439A CN202011068043.5A CN202011068043A CN112180439A CN 112180439 A CN112180439 A CN 112180439A CN 202011068043 A CN202011068043 A CN 202011068043A CN 112180439 A CN112180439 A CN 112180439A
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microseismic event
type
microseismic
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state matrix
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翟明岳
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Guangdong University of Petrochemical Technology
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Guangdong University of Petrochemical Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/288—Event detection in seismic signals, e.g. microseismics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
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Abstract

The embodiment of the invention discloses a microseismic event detection method and system optimized by utilizing a convex function, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; 102, acquiring a microseismic event type vector p through actual investigation; step 103, obtaining candidate microseismic event vector e and candidate state matrix alpha*(ii) a 104, solving a state matrix alpha; step 105 determines microseismic events.

Description

Microseismic event detection method and system optimized by utilizing convex function
Technical Field
The invention relates to the field of petroleum, in particular to a method and a system for detecting a microseismic event.
Background
The hydraulic fracturing microseismic monitoring technology is an important new technology developed in the fields of low-permeability reservoir fracturing, reservoir driving, water-drive leading edges and the like in recent years, and is also an important supporting technology for shale gas development. According to the technology, a multistage three-component detector array is arranged in an adjacent well, a microseismic event generated in a target interval of a fractured well in a hydraulic fracturing process is monitored, and the microseismic event is inverted to obtain parameters such as a seismic source position, so that the geometrical shape and the spatial distribution of crack growth in the hydraulic fracturing process are described, the length, the height, the width and the direction of the crack generated by hydraulic fracturing are provided in real time, and the industrial development of shale gas is realized. The hydraulic fracturing microseismic detection is a hotspot and difficulty of scientific research in the field of current shale gas development. From the social and national demand perspective, the development of the research on the aspect of the microseismic monitoring system is very important, and the microseismic monitoring system has great social and economic values.
An important task in microseismic monitoring systems is the localization of microseismic events. The positioning accuracy is the most important factor affecting the application effect of the microseismic monitoring system, and the accuracy of positioning the microseismic event mainly depends on the related factors such as the accuracy of the fluctuation first-arrival (also called first-arrival) reading. But the problem is that the first arrival pick-up is not as simple as it is imagined. The rock fracture form is very complex under the influence of the mining of ground instruments and geological structures, and then microseismic fluctuation with various forms and energy is generated, the form can be dozens or even hundreds, not only are the dominant frequency, the delay, the energy and the like different, but also the waveform form difference near the first arrival position is huge, and the non-uniformity of the waveform characteristics makes the first arrival picking very difficult. Further studies have also shown that the microseismic source mechanism also affects the first arrival point characteristics: most microseismic fluctuations generated by the shearing action of hard rock have large energy, higher main frequency, short time delay and the position of the maximum peak value closely follows the initial first arrival, and the first arrival point of the waves is clear, the jump-off time delay is short, and the waves are easy to pick up; however, most microseismic fluctuations generated by the stretching action have small energy, low main frequency, long delay time, slow take-off and uniform energy distribution, the amplitude of the waves at the first arrival point is small and is easily submerged by interference signals, the characteristic expressions of the first arrival point are inconsistent, and the first arrival pickup is not easy; the microseismic fluctuation generated by soft rock has concentrated energy distribution, fuzzy initial first arrival points, unobvious boundary lines, is obviously different from hard rock, and is difficult to pick up the first arrival. Meanwhile, according to foreign research, it is found that many algorithms want to certainly consider the first arrival wave as a P wave because the P wave velocity is greater than the S wave velocity, but the fact may be more complicated: the first arrivals may be P-waves, S-waves, and even outliers (outliers). According to the study, 41% of the first arrivals are S-waves, and 10% of the first arrivals are caused by outliers. These all present considerable difficulties for first arrival pick-ups.
In addition to the complexity of first arrival point features, first arrival picking faces another greater challenge: microseismic recordings are mass data. For example, approximately 1 million microseismic events were recorded in a test area of month 1 of 2005. Meanwhile, in order to meet production requirements, the microseismic monitoring system needs to continuously record 24 hours a day. Not only is a significant portion of this data a noise and interference caused by human or mechanical activity, independent of microseisms. The literature further classifies noise into three basic types: high frequency (> 200Hz) noise, caused by various job related activities; low frequency noise (<10Hz), typically caused by machine activity far from the recording site, and commercial current (50 Hz). In addition, the microseismic signals themselves are not pure, for example, the professor of sinus name in China considers that the microseismic signals include various signals.
Therefore, how to identify microseismic events and pick up first arrivals from mass data is the basis of microseismic data processing. Compared with the prior art, the production method mostly adopts a manual method, wastes time and labor, has poor precision and reliability, cannot ensure the picking quality, and cannot process mass data. The automatic first arrival pickup is one of the solutions, and the automatic first arrival pickup of the micro-seismic fluctuation is one of the key technologies for processing the micro-seismic monitoring data and is also a technical difficulty for realizing the automatic positioning of the micro-seismic source.
Disclosure of Invention
In a common microseismic event detection method, the size of a judgment threshold is determined randomly, a uniform criterion is not provided, the general applicability of the method is greatly limited, and particularly when the signal-to-noise ratio is low, the performance of the algorithm is greatly influenced.
The invention aims to provide a microseismic event detection method and system based on convex function optimization. The method has better robustness and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a method of microseismic event detection optimized using a convex function, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a microseismic event type vector p through actual investigation, specifically:
Figure BDA0002714423630000021
wherein:
Ni: microseismic event type total
i＝1,2,···,Ni: microseismic event type sequence number
pi: mean square error of the ith type of microseismic event, and arranged in ascending order, i.e.:
Figure BDA0002714423630000029
step 103, obtaining candidate microseismic event vector e and candidate state matrix alpha*The method specifically comprises the following steps:
Figure BDA0002714423630000022
Figure BDA0002714423630000023
wherein:
if Δ Pj≥p1Then the mth element e of the candidate microseismic event vector em＝j
Figure BDA0002714423630000024
The candidate state matrix α*Row o and column m elements of
m＝1,2,···,NE: candidate microseismic event sequence number
NE: total number of candidate microseismic events
Figure BDA0002714423630000025
E thmPoint RMS value
Figure BDA0002714423630000026
J point RMS value
Figure BDA0002714423630000027
Offset value
Figure BDA0002714423630000028
Get the whole on
*: representing any independent variable
N: length of the signal sequence S
sl: the l-th element of the signal sequence S
l: sum parameter
If the element serial number l is less than or equal to 0, setting the element serial number to be 1;
if the element serial number l is larger than or equal to N, setting the element serial number as N;
m0: mean value of the signal sequence S
o＝1,2,···,Ni: microseismic event type sequence number;
step 104, obtaining a state matrix α, specifically:
Figure BDA0002714423630000031
wherein the content of the first and second substances,
Figure BDA0002714423630000032
row o and column u elements of the state matrix alpha
Figure BDA0002714423630000033
Figure BDA0002714423630000034
Intermediate parameter
Figure BDA0002714423630000035
E thuPoint RMS value
Figure BDA0002714423630000036
Confidence for type o microseismic events
po: type o microseismic event RMS value
pq: type q microseismic event RMS value
q＝1,2,···,Ni: microseismic event type sequence number;
step 105, determining a microseismic event, specifically: for the ith row and jth column element α in the state matrix αijIf α isij1, indicating that a microseismic event of type i is present at data point j; if α isij0, indicates that there is no microseismic event of type i at data point j.
A microseismic event detection system optimized with a convex function comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 obtains the microseismic event type vector p through actual investigation, specifically:
Figure BDA0002714423630000037
wherein:
Ni: microseismic event type total
i＝1,2,···,Ni: microseismic event type sequence number
pi: mean square error of the ith type of microseismic event, and arranged in ascending order, i.e.:
Figure BDA00027144236300000410
module 203 finds candidate microseismic event vector e and candidate state matrix alpha*The method specifically comprises the following steps:
Figure BDA0002714423630000041
Figure BDA0002714423630000042
wherein:
if Δ Pj≥p1Then the mth element e of the candidate microseismic event vector em＝j
Figure BDA0002714423630000043
The candidate state matrix α*Row o and column m elements of
m＝1,2,···,NE: candidate microseismic event sequence number
NE: total number of candidate microseismic events
Figure BDA0002714423630000044
E thmPoint RMS value
Figure BDA0002714423630000045
J point RMS value
Figure BDA0002714423630000046
Offset value
Figure BDA0002714423630000047
Get the whole on
*: representing any independent variable
N: length of the signal sequence S
sl: the l-th element of the signal sequence S
l: sum parameter
If the element serial number l is less than or equal to 0, setting the element serial number to be 1;
if the element serial number l is larger than or equal to N, setting the element serial number as N;
m0: mean value of the signal sequence S
o＝1,2,···,Ni: microseismic event type sequence number;
the module 204 calculates a state matrix α, specifically:
Figure BDA0002714423630000048
wherein the content of the first and second substances,
Figure BDA0002714423630000049
row o and column u elements of the state matrix alpha
Figure BDA0002714423630000051
Figure BDA0002714423630000052
Intermediate parameter
Figure BDA0002714423630000053
E thuPoint RMS value
Figure BDA0002714423630000054
Confidence for type o microseismic events
po: type o microseismic event RMS value
pq: type q microseismic event RMS value
q＝1,2,···,Ni: microseismic event type sequence number;
module 205 determines microseismic events, specifically: for the ith row and jth column element α in the state matrix αijIf α isij1, indicating that a microseismic event of type i is present at data point j; if α isij0, indicates that there is no microseismic event of type i at data point j.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
in a common microseismic event detection method, the size of a judgment threshold is determined randomly, a uniform criterion is not provided, the general applicability of the method is greatly limited, and particularly when the signal-to-noise ratio is low, the performance of the algorithm is greatly influenced.
The invention aims to provide a microseismic event detection method and system based on convex function optimization. The method has better robustness and simpler calculation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of the system of the present invention;
FIG. 3 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow diagram of a microseismic event detection method using convex function optimization
FIG. 1 is a flow chart of a microseismic event detection method using convex function optimization according to the present invention. As shown in fig. 1, the microseismic event detection method optimized by using a convex function specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a microseismic event type vector p through actual investigation, specifically:
Figure BDA0002714423630000061
wherein:
Ni: microseismic event type total
i＝1,2,···,Ni: microseismic event type sequence number
pi: mean square error of the ith type of microseismic event, and arranged in ascending order, i.e.:
Figure BDA0002714423630000062
step 103, obtaining candidate microseismic event vector e and candidate state matrix alpha*The method specifically comprises the following steps:
Figure BDA0002714423630000063
Figure BDA0002714423630000064
wherein:
if Δ Pj≥p1Then the mth element e of the candidate microseismic event vector em＝j
Figure BDA0002714423630000065
The candidate state matrix α*Row o and column m elements of
m＝1,2,···,NE: candidate microseismic event sequence number
NE: total number of candidate microseismic events
Figure BDA0002714423630000066
E thmPoint RMS value
Figure BDA0002714423630000067
J point RMS value
Figure BDA0002714423630000068
Offset value
Figure BDA0002714423630000069
Get the whole on
*: representing any independent variable
N: length of the signal sequence S
sl: the l-th element of the signal sequence S
l: sum parameter
If the element serial number l is less than or equal to 0, setting the element serial number to be 1;
if the element serial number l is larger than or equal to N, setting the element serial number as N;
m0: mean value of the signal sequence S
o＝1,2,···,Ni: microseismic event type sequence number;
step 104, obtaining a state matrix α, specifically:
Figure BDA0002714423630000071
wherein the content of the first and second substances,
Figure BDA0002714423630000072
row o and column u elements of the state matrix alpha
Figure BDA0002714423630000073
Figure BDA0002714423630000074
Intermediate parameter
Figure BDA0002714423630000075
E thuPoint RMS value
Figure BDA0002714423630000076
Confidence for type o microseismic events
po: type o microseismic event RMS value
pq: type q microseismic event RMS value
q＝1,2,···,Ni: microseismic event type sequence number;
step 105, determining a microseismic event, specifically: for the ith row and jth column element α in the state matrix αijIf α isij1, indicating that a microseismic event of type i is present at data point j; if α isij0, indicates that there is no microseismic event of type i at data point j.
FIG. 2 structural intent of a microseismic event detection system optimized with convex function
FIG. 2 is a schematic diagram of a microseismic event detection system optimized using a convex function according to the present invention. As shown in fig. 2, the microseismic event detection system optimized by convex function comprises the following structure:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 obtains the microseismic event type vector p through actual investigation, specifically:
Figure BDA0002714423630000077
wherein:
Ni: microseismic event type total
i＝1,2,···,Ni: microseismic event type sequence number
pi: mean square error of the ith type of microseismic event, and arranged in ascending order, i.e.:
Figure BDA0002714423630000078
module 203 finds candidate microseismic event vector e and candidate state matrix alpha*The method specifically comprises the following steps:
Figure BDA0002714423630000081
Figure BDA0002714423630000082
wherein:
if Δ Pj≥p1Then the mth element e of the candidate microseismic event vector em＝j
Figure BDA0002714423630000083
The candidate state matrix α*Row o and column m elements of
m＝1,2,···,NE: candidate microseismic event sequence number
NE: total number of candidate microseismic events
Figure BDA0002714423630000084
E thmPoint RMS value
Figure BDA0002714423630000085
J point RMS value
Figure BDA0002714423630000086
Offset value
Figure BDA0002714423630000087
Get the whole on
*: representing any independent variable
N: length of the signal sequence S
sl: the l-th element of the signal sequence S
l: sum parameter
If the element serial number l is less than or equal to 0, setting the element serial number to be 1;
if the element serial number l is larger than or equal to N, setting the element serial number as N;
m0: mean value of the signal sequence S
o＝1,2,···,Ni: microseismic event type sequence number;
the module 204 calculates a state matrix α, specifically:
Figure BDA0002714423630000088
wherein the content of the first and second substances,
Figure BDA0002714423630000089
row o and column u elements of the state matrix alpha
Figure BDA00027144236300000810
Figure BDA00027144236300000811
Intermediate parameter
Figure BDA0002714423630000091
E thuPoint RMS value
Figure BDA0002714423630000092
Confidence for type o microseismic events
po: type o microseismic event RMS value
pq: type q microseismic event RMS value
q＝1,2,···,Ni: microseismic event type sequence number;
module 205 determines microseismic events, specifically: for the ith row and jth column element α in the state matrix αijIf α isij1, indicating that a microseismic event of type i is present at data point j; if α isij0, indicates that there is no microseismic event of type i at data point j.
The following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
step 301, acquiring a signal sequence S acquired according to a time sequence;
step 302 obtains a microseismic event type vector p through actual investigation, specifically:
Figure BDA0002714423630000093
wherein:
Ni: microseismic event type total
i＝1,2,···,Ni: microseismic event type sequence number
pi: mean square error of the ith type of microseismic event, and arranged in ascending order, i.e.:
Figure BDA0002714423630000097
step 303 finds candidate microseismic event vector e and candidate state matrix alpha*The method specifically comprises the following steps:
Figure BDA0002714423630000094
Figure BDA0002714423630000095
wherein:
if Δ Pj≥p1Then the mth element e of the candidate microseismic event vector em＝j
Figure BDA0002714423630000096
The candidate state matrix α*Row o and column m elements of
m＝1,2,···,NE: candidate microseismic event sequence number
NE: total number of candidate microseismic events
Figure BDA0002714423630000101
E thmPoint RMS value
Figure BDA0002714423630000102
J point RMS value
Figure BDA0002714423630000103
Offset value
Figure BDA0002714423630000104
Get the whole on
*: representing any independent variable
N: length of the signal sequence S
sl: the l-th element of the signal sequence S
l: sum parameter
If the element serial number l is less than or equal to 0, setting the element serial number to be 1;
if the element serial number l is larger than or equal to N, setting the element serial number as N;
m0: mean value of the signal sequence S
o＝1,2,···,Ni: microseismic event type sequence number;
step 304, obtaining a state matrix α, specifically:
Figure BDA0002714423630000105
wherein the content of the first and second substances,
Figure BDA0002714423630000106
row o and column u elements of the state matrix alpha
Figure BDA0002714423630000107
Figure BDA0002714423630000108
Intermediate parameter
Figure BDA0002714423630000109
E thuPoint RMS value
Figure BDA00027144236300001010
Confidence for type o microseismic events
po: type o microseismic event RMS value
pq: type q microseismic event RMS value
q＝1,2,···,Ni: microseismic event type sequence number;
step 305, determining microseismic events, specifically: for the ith row and jth column element α in the state matrix αijIf α isij1, indicating that a microseismic event of type i is present at data point j; if α isij0, indicates that there is no microseismic event of type i at data point j.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (2)

1. A method for microseismic event detection optimized using a convex function, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a microseismic event type vector p through actual investigation, specifically:
Figure FDA0002714423620000011
wherein:
Ni: microseismic event type total
i＝1,2,···,Ni: microseismic event type sequence number
pi: mean square error of the ith type of microseismic event, and arranged in ascending order, i.e.:
p1<p2<···<
Figure FDA0002714423620000019
step 103, obtaining candidate microseismic event vector e and candidate state matrix alpha*The method specifically comprises the following steps:
Figure FDA0002714423620000012
Figure FDA0002714423620000013
wherein:
if Δ Pj≥p1Then the mth element e of the candidate microseismic event vector em＝j
Figure FDA0002714423620000014
The candidate state matrix α*Row o and column m elements of
m＝1,2,···,NE: candidate microseismic event sequence number
NE: total number of candidate microseismic events
Figure FDA0002714423620000015
E thmPoint RMS value
Figure FDA0002714423620000016
J point RMS value
Figure FDA0002714423620000017
Offset value
Figure FDA0002714423620000018
Get the whole on
*: representing any independent variable
N: length of the signal sequence S
sl: the l-th element of the signal sequence S
l: sum parameter
If the element serial number l is less than or equal to 0, setting the element serial number to be 1;
if the element serial number l is larger than or equal to N, setting the element serial number as N;
m0: mean value of the signal sequence S
o＝1,2,···,Ni: microseismic event type sequence number;
step 104, obtaining a state matrix α, specifically:
Figure FDA0002714423620000021
wherein the content of the first and second substances,
Figure FDA0002714423620000022
row o and column u elements of the state matrix alpha
Figure FDA0002714423620000023
Figure FDA0002714423620000024
Intermediate parameter
Figure FDA0002714423620000025
E thuPoint RMS value
Figure FDA0002714423620000026
Confidence for type o microseismic events
po: type o microseismic event RMS value
pq: type q microseismic event RMS value
q＝1,2,···,Ni: microseismic event type sequence number;
step 105, determining a microseismic event, specifically: for the ith row and jth column element α in the state matrix αijIf α isij1, indicating that a microseismic event of type i is present at data point j; if α isij0, indicates that there is no microseismic event of type i at data point j.
2. A microseismic event detection system optimized using a convex function comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 obtains the microseismic event type vector p through actual investigation, specifically:
Figure FDA00027144236200000210
wherein:
Ni: microseismic event type total
i＝1,2,···,Ni: microseismic event type sequence number
pi: mean square error of the ith type of microseismic event, and arranged in ascending order, i.e.:
Figure FDA00027144236200000211
module 203 finds candidate microseismic event vector e and candidate state matrix alpha*The method specifically comprises the following steps:
Figure FDA0002714423620000027
Figure FDA0002714423620000028
wherein:
if Δ Pj≥p1Then the mth element e of the candidate microseismic event vector em＝j
The candidate state matrix α*Row o and column m elements of
m＝1,2,···,NE: candidate microseismic event sequence number
NE: total number of candidate microseismic events
Figure FDA0002714423620000032
E thmPoint RMS value
Figure FDA0002714423620000033
J point RMS value
Figure FDA0002714423620000034
Offset value
Figure FDA0002714423620000035
Get the whole on
*: representing any independent variable
N: length of the signal sequence S
sl: the l-th element of the signal sequence S
l: sum parameter
If the element serial number l is less than or equal to 0, setting the element serial number to be 1;
if the element serial number l is larger than or equal to N, setting the element serial number as N;
m0: mean value of the signal sequence S
o＝1,2,···,Ni: microseismic event type sequence number;
the module 204 calculates a state matrix α, specifically:
Figure FDA0002714423620000036
wherein the content of the first and second substances,
Figure FDA0002714423620000037
row o and column u elements of the state matrix alpha
Figure FDA0002714423620000038
Figure FDA0002714423620000039
Intermediate parameter
Figure FDA00027144236200000310
E thuPoint RMS value
Figure FDA00027144236200000311
Confidence for type o microseismic events
po: type o microseismic event RMS value
pq: type q microseismic event RMS value
q＝1,2,···,Ni: microseismic event type sequence number;
module 205 determines microseismic events, specifically: for the ith row and jth column element α in the state matrix αijIf α isijWhen 1, the data is indicatedA microseismic event of type i exists at point j; if α isij0, indicates that there is no microseismic event of type i at data point j.
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