CN110146921B - Microseismic event detection method and system based on Dirac distribution probability - Google Patents

Microseismic event detection method and system based on Dirac distribution probability Download PDF

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CN110146921B
CN110146921B CN201910570681.8A CN201910570681A CN110146921B CN 110146921 B CN110146921 B CN 110146921B CN 201910570681 A CN201910570681 A CN 201910570681A CN 110146921 B CN110146921 B CN 110146921B
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microseismic
sequence
dirac distribution
microseismic signal
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CN110146921A (en
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翟明岳
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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. analysis, for interpretation, for correction
    • 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. analysis, for interpretation, for correction
    • G01V1/30—Analysis
    • G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity

Abstract

The embodiment of the invention discloses a method and a system for detecting microseismic events based on a Dirac distribution function, wherein the method comprises the following steps: step 1, inputting actual measurementThe microseismic signal sequence S; step 2, detecting a microseismic event according to the Dirac distribution probability; the method specifically comprises the following steps: probability of dirac distribution
Figure DDA0002110718340000011
Detecting a microseismic event at the Kth point of the microseismic signal sequence S; otherwise, no microseismic event is detected; wherein sigmaiK is the mean square error of the ith microseismic signal difference sequence; mu.siK is the mean value of the ith microseismic signal differential sequence; delta SKThe Kth element in the Kth microseismic signal differential sequence; omegaiAnd i is 1,2, …, and K is the weight of the ith differential sequence of microseismic signals.

Description

Microseismic event detection method and system based on Dirac distribution probability
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.
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.
Disclosure of Invention
The invention aims to provide a method and a system for detecting a microseismic event based on Dirac distribution probability. The method has the advantages of good robustness and simple calculation.
In order to achieve the purpose, the invention provides the following scheme:
the method for detecting the microseismic event based on the Dirac distribution probability comprises the following steps:
step 1, inputting an actually measured microseismic signal sequence S;
step 2, detecting a microseismic event according to the Dirac distribution probability; the method specifically comprises the following steps: probability of dirac distribution
Figure BDA0002110718320000031
Detecting a microseismic event at the Kth point of the microseismic signal sequence S; otherwise, no microseismic event is detected; wherein sigmaiK is the mean square error of the ith microseismic signal difference sequence; mu.siK is the mean value of the ith microseismic signal differential sequence; delta SKThe Kth element in the Kth microseismic signal differential sequence; omegaiAnd i is 1,2, …, and K is the weight of the ith differential sequence of microseismic signals.
The microseismic event detection system based on the Dirac distribution probability comprises the following components:
the acquisition module inputs an actually measured microseismic signal sequence S;
the judging module is used for detecting the microseismic event according to the Dirac distribution probability; the method specifically comprises the following steps: probability of dirac distribution
Figure BDA0002110718320000032
Detecting a microseismic event at the Kth point of the microseismic signal sequence S; otherwise, no microseismic event is detected; wherein sigmaiK is the mean square error of the ith microseismic signal difference sequence; mu.siK is the mean value of the ith microseismic signal differential sequence; delta SKThe Kth element in the Kth microseismic signal differential sequence; omegaiAnd i is 1,2, …, and K is the weight of the ith differential sequence of microseismic signals.
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 method and a system for detecting a microseismic event based on Dirac distribution probability. The method has the advantages of good robustness and simple calculation.
Drawings
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 present invention;
FIG. 2 is a schematic structural view 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 chart of a method for detecting a microseismic event based on a Dirac distribution probability
Fig. 1 is a schematic flow chart of a method for detecting a microseismic event based on dirac distribution probability according to the present invention. As shown in fig. 1, the method for detecting a microseismic event based on dirac distribution probability specifically includes the following steps:
step 1, inputting an actually measured microseismic signal sequence S;
step 2, detecting a microseismic event according to the Dirac distribution probability; the method specifically comprises the following steps: probability of dirac distribution
Figure BDA0002110718320000041
Detecting a microseismic event at the Kth point of the microseismic signal sequence S; otherwise, no microseismic event is detected; wherein sigmaiK is the mean square error of the ith microseismic signal difference sequence; mu.siK is the mean value of the ith microseismic signal differential sequence; delta SKThe Kth element in the Kth microseismic signal differential sequence; omegaiAnd i is 1,2, …, and K is the weight of the ith differential sequence of microseismic signals.
Before the step 2, the method further comprises:
step 3, calculating the Dirac distribution probability PK。
The step 3 comprises the following steps:
step 301, generating a differential sequence of microseismic signals, specifically:
ΔS＝[ΔS1,ΔS2,…,ΔSN-1]＝[S2-S1,S3-S2,…,SN-SN-1]
wherein
Δ S: the length of the vibration and sound signal differential sequence is N-1
ΔSnN is 1,2, …, N-1, the nth element in the vibration and sound signal differential sequence Delta S
SnN is 1,2, …, N: the nth element in the vibro-acoustic signal sequence S
S＝[S1，S2，…，SN]The length of the vibration and sound signal sequence is N
Then the ith microseismic signal differential sequence is
Δsi＝[ΔS1,ΔS2,…,ΔSi],i＝1,2,…,N-1
Step 302, calculating dirac distribution weight, specifically:
Figure BDA0002110718320000051
wherein:
ωkk is 1,2, …, N is dirac distribution weight
Figure BDA0002110718320000052
Is a first parameter
Figure BDA0002110718320000053
Is a second parameter
Γ [ ]: gamma function with argument x
max(|ΔS1|,|ΔS2|,…,|ΔSiExpressed as | Delta S1|,|ΔS2|,…,|ΔSiMaximum value in |
Step 303, calculating the Dirac distribution probability PKThe method specifically comprises the following steps:
Figure BDA0002110718320000061
wherein
Figure BDA0002110718320000062
Is a parameter
FIG. 2 structural intent of a Dirac distribution probability based microseismic event detection system
Fig. 2 is a schematic structural diagram of a microseismic event detection system based on dirac distribution probability according to the present invention. As shown in fig. 2, the system for detecting microseismic events based on dirac distribution probability includes the following structure:
the acquisition module 401 inputs an actually measured microseismic signal sequence S;
a determining module 402, for detecting a microseismic event according to the dirac distribution probability; the method specifically comprises the following steps: probability of dirac distribution
Figure BDA0002110718320000063
Detecting a microseismic event at the Kth point of the microseismic signal sequence S; otherwise, no microseismic event is detected; wherein sigmaiK is the mean square error of the ith microseismic signal difference sequence; mu.siK is the mean value of the ith microseismic signal differential sequence; delta SKThe Kth element in the Kth microseismic signal differential sequence; omegaiAnd i is 1,2, …, and K is the weight of the ith differential sequence of microseismic signals.
The system further comprises:
a calculation module 403 for calculating the probability P of the Dirac distributionK。
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:
1. inputting measured microseismic signal sequence
S＝[s1,s2,…,sN-1,sN]
Wherein:
s: measured microseismic signal sequence of length N
siI is 1,2, …, N is measured microseismic signal with serial number i
2. Generating microseismic signal differential sequences
ΔS＝[ΔS1,ΔS2,…,ΔSN-1]＝[S2-S1,S3-S2,…,SN-SN-1]
Wherein
Δ S: the length of the vibration and sound signal differential sequence is N-1
ΔSnN is 1,2, …, N-1, the nth element in the vibration and sound signal differential sequence Delta S
SnN is 1,2, …, N: the nth element in the vibro-acoustic signal sequence S
S＝[S1，S2，…，SN]The length of the vibration and sound signal sequence is N
Then the ith microseismic signal differential sequence is
Δsi＝[ΔS1,ΔS2,…,ΔSi],i＝1,2,…,N-1
3. Computing dirac distribution weights
Figure BDA0002110718320000071
Wherein:
ωkk is 1,2, …, N is dirac distribution weight
Figure BDA0002110718320000072
Is a first parameter
Figure BDA0002110718320000073
Is a second parameter
Γ [ ]: gamma function with argument x
max(|ΔS1|,|ΔS2|,…,|ΔSiExpressed as | Delta S1|,|ΔS2|,…,|ΔSiMaximum value in |
4. Calculating dirac distribution probability
Figure BDA0002110718320000081
Wherein
Figure BDA0002110718320000082
Is a parameter
5. Determining microseismic events
Detecting a microseismic event according to the dirac distribution probability; the method specifically comprises the following steps: probability of dirac distribution
Figure BDA0002110718320000083
Detecting a microseismic event at the Kth point of the microseismic signal sequence S; otherwise, no microseismic event is detected; wherein sigmaiK is the mean square error of the ith microseismic signal difference sequence; mu.siK is the mean value of the ith microseismic signal differential sequence; delta SKThe Kth element in the Kth microseismic signal differential sequence; omegaiAnd i is 1,2, …, and K is the weight of the ith differential sequence of microseismic signals.
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. The method for detecting the microseismic event based on the Dirac distribution probability is characterized by comprising the following steps:
step 1, inputting an actually measured microseismic signal sequence S;
step 2, generating the i-th microseismic signal differential sequence deltasiThe method specifically comprises the following steps: the ith microseismic signal differential sequence deltasiThe generation formula of (1) is: Δ si＝[s2-s1,s3-s2,…,si+1-si](ii) a Wherein s is1Is the 1 st element in the microseismic signal sequence S; s2Is the 2 nd element in the microseismic signal sequence S; siIs the ith element in the microseismic signal sequence S; si+1Is the (i + 1) th element in the microseismic signal sequence S; i is an element serial number, and the value range of the element serial number i is 1,2, … and N-1; n is the length of the microseismic signal sequence S;
step 3, calculating the kth Dirac distribution weight omegakThe method specifically comprises the following steps: the kth Dirac distribution weight ωkThe calculation formula of (2) is as follows:
Figure FDA0002783295510000011
wherein:
Figure FDA0002783295510000012
is a first parameter;
Figure FDA0002783295510000013
is a second parameter; Γ [ xi ]k]: the independent variable is xikA gamma function of (a);
max(|s2-s1|,|s3-s2|,…,|si+1-sii) obtaining s2-s1|,|s3-s2|,…,|si+1-siMaximum in |; k is the dirac distribution weight sequence number, and the distribution range of the dirac distribution weight sequence number k is: k is 1,2, …, N-1;
step 4, solving the k-th Dirac distribution probability PkThe method specifically comprises the following steps: the k-th dirac distribution probability PkThe formula of (1) is:
Figure FDA0002783295510000014
step 5, detecting a microseismic event according to the Dirac distribution probability; the method specifically comprises the following steps: if the k-th dirac distribution probability
Figure FDA0002783295510000015
Detecting a microseismic event at the kth point of the microseismic signal sequence S; otherwise, no microseismic event is detected; wherein sigmaiFor the i-th microseismic signal differential sequence deltasiThe mean square error of (d); mu.siFor the i-th microseismic signal differential sequence deltasiIs measured.
2. The microseismic event detection system based on the dirac distribution probability is characterized by comprising the following components:
the acquisition module inputs an actually measured microseismic signal sequence S;
a calculation module for generating the i-th microseismic signal differential sequence deltasiThe method specifically comprises the following steps: the ith microseismic signal differential sequence deltasiThe generation formula of (1) is: Δ si＝[s2-s1,s3-s2,…,si+1-si](ii) a Wherein s is1Is the 1 st element in the microseismic signal sequence S; s2Is the 2 nd element in the microseismic signal sequence S; siIs the ith element in the microseismic signal sequence S; si+1Is the (i + 1) th element in the microseismic signal sequence S; i is an element serial number, the value range of the element serial number i is 1,2,…, N-1; n is the length of the microseismic signal sequence S; calculating the kth Dirac distribution weight omegakThe method specifically comprises the following steps: the kth Dirac distribution weight ωkThe calculation formula of (2) is as follows:
Figure FDA0002783295510000021
wherein:
Figure FDA0002783295510000022
is a first parameter;
Figure FDA0002783295510000023
is a second parameter; Γ [ xi ]k]: the independent variable is xikA gamma function of (a);
max(|s2-s1|,|s3-s2|,…,|si+1-sii) obtaining s2-s1|,|s3-s2|,…,|si+1-siMaximum in |; k is the dirac distribution weight sequence number, and the distribution range of the dirac distribution weight sequence number k is: k is 1,2, …, N-1; solving the k-th Dirac distribution probability PkThe method specifically comprises the following steps:
the k-th dirac distribution probability PkThe formula of (1) is:
Figure FDA0002783295510000024
the judging module is used for detecting the microseismic event according to the Dirac distribution probability; the method specifically comprises the following steps: if the k-th dirac distribution probability
Figure FDA0002783295510000025
Detecting a microseismic event at the kth point of the microseismic signal sequence S; otherwise, no microseismic event is detected; wherein sigmaiIs the ith microseismic messageNumber difference sequence Δ siThe mean square error of (d); mu.siFor the i-th microseismic signal differential sequence deltasiIs measured.
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