CN107870359A - Micro-seismic event recognition methods and device - Google Patents

Micro-seismic event recognition methods and device Download PDF

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CN107870359A
CN107870359A CN201610862629.6A CN201610862629A CN107870359A CN 107870359 A CN107870359 A CN 107870359A CN 201610862629 A CN201610862629 A CN 201610862629A CN 107870359 A CN107870359 A CN 107870359A
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event
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time
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CN107870359B (en
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杨心超
崔树果
郭全仕
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China Petroleum and Chemical Corp
Sinopec Geophysical Research Institute
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China Petroleum and Chemical Corp
Sinopec Geophysical Research Institute
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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
    • G01V1/30—Analysis
    • G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/24—Recording seismic data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/61—Analysis by combining or comparing a seismic data set with other data
    • G01V2210/612—Previously recorded data, e.g. time-lapse or 4D

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Acoustics & Sound (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Geophysics (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

The invention provides a kind of micro-seismic event recognition methods and device.This method may comprise steps of：Input micro-seismic monitoring record；Waveform similar curves R (t) is obtained by calculating the waveform similarity factor between each road signal of each moment；Adaptive threshold Ht (t) is calculated according to the waveform similar curves R (t)；And in time span it is TeventDetection window in, will meet R (t) be more than Ht (t) maximum Rmax(tl) it is identified as the event in the detection window, and by the time t corresponding to the eventlThe shake time is played as the event.The micro-seismic event recognition methods of the present invention and the time accuracy of identification of device are not influenceed by noise energy, can reduce false drop rate, and ensure that the recognition capability to weak micro-seismic event.

Description

Micro-seismic event recognition methods and device
Technical field
The present invention relates to micro-seismic monitoring technical field of data processing, in particular it relates to a kind of micro-seismic event identification side Method and device.
Background technology
It is mainly caused in hydraulic fracturing process by monitoring in field of petroleum exploitation, the application of microseismic Microseismic signals, fracturing process can be monitored, evaluate fracturing effect, and then instruct optimization engineering parameter.In north America region, microseism Monitoring technology is widely used to hydraulically created fracture monitoring, high pressure injects the business such as deposit dynamic monitoring neck caused by operation Domain, obtain the highly recognition of petroleum industrial circle, turn into an abundant information during oil and gas development, it is accurate, timely monitor Technology, it is one of important means that progress hydraulically created fracture monitors in real time in unconventional development of resources.
Because micro-seismic monitoring is the long-time continuous monitoring in fracturing process, micro-seismic event possibly be present at monitoring note Any time in record, it is big to depend merely on manual identified event workload, it is therefore desirable to the event automatic identifying method of efficiently and accurately.It is long Short time-window energy ratio function is micro-seismic event recognition methods the most frequently used at present, when this method is by calculating long in window and short time-window The energy ratio of record, think to detect micro-seismic event when energy ratio exceedes given threshold value.Inventor has found, uses letter Number energy has the defects of recognition result is had a great influence by noise energy in recording as criterion of identification, is readily detected " false Event ", false drop rate are higher.Therefore, it is necessary to develop a kind of accuracy of identification high micro-seismic event automatic identifying method and device.
The information for being disclosed in background of invention part is merely intended to deepen the reason of the general background technology to the present invention Solution, and be not construed as recognizing or imply known to those skilled in the art existing of the information structure in any form Technology.
The content of the invention
The purpose of the present invention is that a kind of waveform similarity based on multiple tracks microseism signal of research carries out micro-seismic event certainly It is dynamic to know method for distinguishing, improve the precision of event recognition.
A kind of according to an aspect of the invention, it is proposed that micro-seismic event recognition methods.This method may comprise steps of： Micro-seismic monitoring record is inputted, the micro-seismic monitoring record includes the signal of m roads wave detector record, wherein, per i roads wave detector The signal of record is si(t), i ∈ [1, m], t=[t1,t2,...tj,...tn], to record the time, j ∈ [1, n], n adopts for the time Sampling point number；Waveform similar curves R (t) is obtained by calculating the waveform similarity factor between each road signal of each moment；Root Adaptive threshold Ht (t) is calculated according to the waveform similar curves R (t)；It is T in time spaneventDetection window in, will meet R (t) is more than Ht (t) maximum Rmax(tl) be identified as the event in the detection window, and by corresponding to the event when Between tlThe shake time is played as the event.
Preferably, methods described further comprises exporting the event and the time corresponding to the event.
Preferably, tjThe calculation formula of waveform similarity factor between each road signal of moment record is：
Wherein, nwin is selected time window length.
Preferably, the waveform similarity factor recorded at each moment can be calculated by sliding window, so as to obtain Waveform similar curves R (t).
Preferably, calculating adaptive threshold Ht (t) according to the waveform similar curves R (t) includes：It is similar to the waveform Curve R (t) carries out Hilbert transform, obtains R (t) envelope H (t), based on the desired value E (t) including H (t) and mark Quasi- variance δ (t) calculates adaptive threshold Ht (t), and the calculation formula of the adaptive threshold Ht (t) is：
Ht (t)=E (t)+α δ (t)
Wherein, α is the weight coefficient of standard variance.
A kind of according to another aspect of the invention, it is proposed that micro-seismic event identification device.The device can include：For defeated Enter the unit of micro-seismic monitoring record, the micro-seismic monitoring record includes the signal s of wave detector recordi(t), wherein, i ∈ [1, m], m be ground micro-seismic monitoring record in road number, t=[t1,t2,...,tn], to record the time, n is time sampling point Number；For obtaining waveform similar curves R (t) list by calculating the waveform similarity factor between each road signal of each moment Member；For calculating adaptive threshold Ht (t) unit according to the waveform similar curves R (t)；It is for recognition time length TeventDetection window event unit, it is T in time spaneventDetection window in, will meet that R (t) is more than Ht (t) maximum Rmax(tj) it is identified as the event in the detection window, and by the time t corresponding to the eventjAs described Event plays the shake time.
Preferably, described device further comprises the list for exporting the event and the time corresponding to the event Member.
Preferably, tjThe calculation formula of waveform similarity factor between each road signal of moment record is：
Wherein, nwin is selected time window length.
Preferably, the waveform similarity factor recorded at each moment can be calculated by sliding window, so as to obtain Waveform similar curves R (t).
Preferably, calculating adaptive threshold Ht (t) according to the waveform similar curves R (t) includes：It is similar to the waveform Curve R (t) carries out Hilbert transform, obtains R (t) envelope H (t), based on the desired value E (t) including H (t) and mark Quasi- variance δ (t) calculates adaptive threshold Ht (t), and the calculation formula of the adaptive threshold Ht (t) is：
Ht (t)=E (t)+α δ (t)
Wherein, α is the weight coefficient of standard variance.
Affinity information and combining adaptive threshold value calculation method of the invention by introducing multiple tracks microseism signal waveform Carry out the automatic identification of micro-seismic event.As a result of multiple tracks waveform similarity criterion, the event recognition precision of this method is not Influenceed by noise energy, the false drop rate of micro-seismic event automatic identification can be substantially reduced.Simultaneously as introduce adaptive Threshold value criterion, relative to the fixed threshold method of routine, this method ensure that the recognition capability to weak micro-seismic event.
Methods and apparatus of the present invention has other characteristics and advantage, and these characteristics and advantage are attached from what is be incorporated herein It will be apparent in figure and subsequent specific embodiment, or by the accompanying drawing and subsequent specific implementation being incorporated herein Stated in detail in example, these the drawings and specific embodiments are provided commonly for explaining the certain principles of the present invention.
Brief description of the drawings
Disclosure illustrative embodiments are described in more detail in conjunction with the accompanying drawings, the disclosure above-mentioned and its Its purpose, feature and advantage will be apparent, wherein, in disclosure illustrative embodiments, identical reference number Typically represent same parts.
Fig. 1 shows micro-seismic event recognition methods according to an embodiment of the invention.
Fig. 2 is synthesis ground micro-seismic detection record.
Fig. 3 is that micro-seismic event identifies curve.
Embodiment
The preferred embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although the disclosure is shown in accompanying drawing Preferred embodiment, however, it is to be appreciated that may be realized in various forms the disclosure without the embodiment party that should be illustrated here Formula is limited.On the contrary, these embodiments are provided so that the disclosure is more thorough and complete, and can be by the disclosure Scope is intactly communicated to those skilled in the art.
Fig. 1 shows the flow chart of micro-seismic event recognition methods according to an embodiment of the invention.
In the present embodiment, this method specifically includes following steps：
1) micro-seismic monitoring record is inputted, the micro-seismic monitoring record includes the signal s of wave detector recordi(t), its In, i ∈ [1, m], m be ground micro-seismic monitoring record in road number, t=[t1,t2,...,tn], to record the time, n is the time Sampled point number.
The micro-seismic monitoring record inputted can be the data of multiple tracks ground micro-seismic signal, and micro-seismic event may go out Any time in present monitoring record.
2) waveform similar curves R (t) is obtained by calculating the waveform similarity factor between each road signal of each moment.
The waveform similar curves R (t) can be obtained by the following method：
T is calculated firstjWaveform similarity factor between each road signal of moment record.The calculating of the waveform similarity factor Formula is：
Wherein, nwin is selected time window length, i.e. R (tj) for each road with tjNwin time spans centered on moment Similarity factor between interior waveform.
Afterwards, continuous sliding window is passed through, you can to calculate t1,t2,...tj,...tnThe waveform of each moment record Similarity factor, so as to obtain waveform similar curves R (t).
3) adaptive threshold Ht (t) is calculated according to the waveform similar curves R (t).
Hilbert transform is carried out to R (t) first, obtains R (t) envelope H (t).Counted afterwards in a sliding window Calculating each moment includes H (t) desired value E (t) and standard variance δ (t).
Adaptive threshold Ht (t) calculation formula is：
Ht (t)=E (t)+α δ (t) (2)
Wherein α is the weight coefficient of standard variance.
4) it is T in time spaneventDetection window in, will meet R (t) be more than Ht (t) maximum Rmax(tl) identification For the event in the detection window, and by the time t corresponding to the eventlThe shake time is played as the event.
The record time of micro-seismic monitoring can be divided into several detection windows, having for each detection window can It can detect to meet the maximum R that R (t) is more than Ht (t)max(tl).The number of detected maximum in the entirely record time Mesh is then the number of the micro-seismic event identified based on micro-seismic monitoring record, and the time corresponding to each maximum is then The micro-seismic event plays the shake time.
In one example, further comprise exporting the event and institute according to the micro-seismic event recognition methods of the present invention State the time corresponding to event.Identified micro-seismic event and institute can be shown in a manner of micro-seismic event identifies curve That states event plays the shake time.
In one example, tjThe calculation formula of waveform similarity factor between each road signal of moment record is：
Wherein, nwin is selected time window length.
In one example, the waveform similarity factor recorded at each moment can be calculated by sliding window, from And obtain waveform similar curves R (t).
In one example, calculating adaptive threshold Ht (t) according to the waveform similar curves R (t) includes：To the ripple Shape similar curves R (t) carries out Hilbert transform, R (t) envelope H (t) is obtained, based on the desired value E including H (t) (t) and standard variance δ (t) calculates adaptive threshold Ht (t), and the calculation formula of the adaptive threshold Ht (t) is：
Ht (t)=E (t)+α δ (t)
Wherein, α is the weight coefficient of standard variance.
The invention also provides a kind of microseism identification device.The device can include：For inputting micro-seismic monitoring note The unit of record, the micro-seismic monitoring record include the signal of m roads wave detector record, wherein, per the signal of i roads wave detector record For si(t), i ∈ [1, m], t=[t1,t2,...tj,...tn], to record the time, j ∈ [1, n], n are time sampling point number； For obtaining waveform similar curves R (t) unit by calculating the waveform similarity factor between each road signal of each moment；With In the unit that adaptive threshold Ht (t) is calculated according to the waveform similar curves R (t)；It is T for recognition time lengthevent's The unit of the event of detection window, it is T in time spaneventDetection window in, will meet R (t) be more than Ht (t) maximum Rmax(tl) it is identified as the event in the detection window, and by the time t corresponding to the eventlShake is played as the event Time.
In one example, further comprise being used to export the event according to the micro-seismic event identification device of the present invention And the unit of the time corresponding to the event.
In one example, tjThe calculation formula of waveform similarity factor between each road signal of moment record is：
Wherein, nwin is selected time window length.
In one example, the waveform similarity factor recorded at each moment can be calculated by sliding window, from And obtain waveform similar curves R (t).
In one example, calculating adaptive threshold Ht (t) according to the waveform similar curves R (t) includes：To the ripple Shape similar curves R (t) carries out Hilbert transform, R (t) envelope H (t) is obtained, based on the desired value E including H (t) (t) and standard variance δ (t) calculates adaptive threshold Ht (t), and the calculation formula of the adaptive threshold Ht (t) is：
Ht (t)=E (t)+α δ (t)
Wherein, α is the weight coefficient of standard variance.
Using example
For ease of understanding the scheme of the embodiment of the present invention and its effect, a concrete application example given below.This area It should be understood to the one skilled in the art that the example, only for the purposes of understanding the present invention, its any detail is not intended to be limited in any way The system present invention.
Fig. 2 shows synthesis ground micro-seismic monitoring record, wherein being respectively present at the time of 200 milliseconds and 500 milliseconds One weak micro-seismic event and a strong micro-seismic event.
Input synthesis ground micro-seismic detection record illustrated in fig. 2；It is calculated according to formula (1) between each road signal Waveform similarity curve R (t), by Fig. 3 solid black lines represent；Then, R (t) envelope H is obtained by Hilbert transform (t), represented by the grey filled lines in Fig. 3；Finally, the self adaptive threshold curve Ht (t) is calculated according to formula (2), by Fig. 3 Black dotted lines represent.It is T by the way that the record time of micro-seismic monitoring is divided into N number of time spaneventDetection window, every The maximum R for meeting that R (t) values are more than Ht (t) values is found in individual detection window respectivelymax(tl).As shown in figure 3, by five jiaos of black The position of two maximums of star representation is the position of two micro-seismic events in composite traces, and the corresponding time 200 Millisecond and 500 milliseconds of respectively two rising for micro-seismic event shake the time.
It is described above the presently disclosed embodiments, described above is exemplary, and non-exclusive, and It is not limited to disclosed each embodiment.In the case of without departing from the scope and spirit of illustrated each embodiment, for this skill Many modifications and changes will be apparent from for the those of ordinary skill in art field.The selection of term used herein, purport The principle of each embodiment, practical application or improvement to the technology in market are best being explained, or is making the art Other those of ordinary skill are understood that each embodiment disclosed herein.

Claims (10)

1. a kind of micro-seismic event recognition methods, it is characterised in that the described method comprises the following steps：
Micro-seismic monitoring record is inputted, the micro-seismic monitoring record includes the signal of m roads wave detector record, wherein, the i-th inspection The signal of ripple device record is si(t), i ∈ [1, m], t=[t1,t2,…tj,…tn], to record the time, j ∈ [1, n], n are the time Sampled point number；
Waveform similar curves R (t) is obtained by calculating the waveform similarity factor between each road signal of each moment；
Adaptive threshold Ht (t) is calculated according to the waveform similar curves R (t)；And
It is T in time spaneventDetection window in, will meet R (t) be more than Ht (t) maximum Rmax(tl) be identified as it is described Event in detection window, and by the time t corresponding to the eventlThe shake time is played as the event.
2. micro-seismic event recognition methods according to claim 1, further comprise exporting the event and the event The corresponding time.
3. micro-seismic event recognition methods according to claim 1, wherein, tjRipple between each road signal of moment record The calculation formula of shape similarity factor is：
<mrow> <mi>R</mi> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <msup> <mrow> <mo>(</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mi>w</mi> <mi>i</mi> <mi>n</mi> </mrow> </munderover> <msub> <mi>s</mi> <mi>i</mi> </msub> <mo>(</mo> <msub> <mi>t</mi> <mrow> <mi>j</mi> <mo>-</mo> <mi>n</mi> <mi>w</mi> <mi>i</mi> <mi>n</mi> <mo>/</mo> <mn>2</mn> <mo>+</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mi>n</mi> <mo>&amp;CenterDot;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msup> <mrow> <mo>(</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mi>w</mi> <mi>i</mi> <mi>n</mi> </mrow> </munderover> <msub> <mi>s</mi> <mi>i</mi> </msub> <mo>(</mo> <msub> <mi>t</mi> <mrow> <mi>j</mi> <mo>-</mo> <mi>n</mi> <mi>w</mi> <mi>i</mi> <mi>n</mi> <mo>/</mo> <mn>2</mn> <mo>+</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow>
Wherein, nwin is selected time window length.
4. micro-seismic event recognition methods according to claim 3, wherein, each moment is calculated by sliding window Waveform similarity factor, so as to obtain waveform similar curves R (t).
5. micro-seismic event recognition methods according to claim 1, wherein, calculated according to the waveform similar curves R (t) Adaptive threshold Ht (t) includes：
Hilbert transform is carried out to the waveform similar curves R (t), R (t) envelope H (t) is obtained, based on the envelope H (t) desired value E (t) and standard variance δ (t) calculates adaptive threshold Ht (t), and the calculating of the adaptive threshold Ht (t) is public Formula is：
Ht (t)=E (t)+α δ (t)
Wherein, α is the weight coefficient of standard variance.
6. a kind of micro-seismic event identification device, it is characterised in that described device includes：
For inputting the unit of micro-seismic monitoring record, the micro-seismic monitoring record includes the signal of m roads wave detector record, its In, the signal per i roads wave detector record is si(t), i ∈ [1, m], t=[t1,t2,...tj,...tn], to record time, j ∈ [1, n], n are time sampling point number；
For obtaining waveform similar curves R (t) list by calculating the waveform similarity factor between each road signal of each moment Member；
For calculating adaptive threshold Ht (t) unit according to the waveform similar curves R (t)；And
It is T for recognition time lengtheventDetection window event unit, be T in time spaneventDetection window It is interior, it will meet that R (t) is more than Ht (t) maximum Rmax(tl) it is identified as the event in the detection window, and by the event Corresponding time tlThe shake time is played as the event.
7. micro-seismic event identification device according to claim 6, further comprise being used for exporting the event and described The unit of time corresponding to event.
8. micro-seismic event identification device according to claim 6, wherein, tjRipple between each road signal of moment record The calculation formula of shape similarity factor is：
<mrow> <mi>R</mi> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <msup> <mrow> <mo>(</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mi>w</mi> <mi>i</mi> <mi>n</mi> </mrow> </munderover> <msub> <mi>s</mi> <mi>i</mi> </msub> <mo>(</mo> <msub> <mi>t</mi> <mrow> <mi>j</mi> <mo>-</mo> <mi>n</mi> <mi>w</mi> <mi>i</mi> <mi>n</mi> <mo>/</mo> <mn>2</mn> <mo>+</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mi>n</mi> <mo>&amp;CenterDot;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msup> <mrow> <mo>(</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mi>w</mi> <mi>i</mi> <mi>n</mi> </mrow> </munderover> <msub> <mi>s</mi> <mi>i</mi> </msub> <mo>(</mo> <msub> <mi>t</mi> <mrow> <mi>j</mi> <mo>-</mo> <mi>n</mi> <mi>w</mi> <mi>i</mi> <mi>n</mi> <mo>/</mo> <mn>2</mn> <mo>+</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow>
Wherein, nwin is selected time window length.
9. micro-seismic event identification device according to claim 8, wherein, calculated by sliding window when each The waveform similarity factor of record is engraved, so as to obtain waveform similar curves R (t).
10. micro-seismic event identification device according to claim 6, wherein, counted according to the waveform similar curves R (t) Calculating adaptive threshold Ht (t) includes：
Hilbert transform is carried out to the waveform similar curves R (t), R (t) envelope H (t) is obtained, based on described including H (t) desired value E (t) and standard variance δ (t) calculates adaptive threshold Ht (t), and the calculating of the adaptive threshold Ht (t) is public Formula is：
Ht (t)=E (t)+α δ (t)
Wherein, α is the weight coefficient of standard variance.
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