CN114442156A - Microseism seismic facies identification and first arrival picking method based on time-arrival curve fitting - Google Patents

Microseism seismic facies identification and first arrival picking method based on time-arrival curve fitting Download PDF

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CN114442156A
CN114442156A CN202011121655.6A CN202011121655A CN114442156A CN 114442156 A CN114442156 A CN 114442156A CN 202011121655 A CN202011121655 A CN 202011121655A CN 114442156 A CN114442156 A CN 114442156A
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李爱山
张子麟
张燎源
赵丽
王昊
魏亚峰
陈磊
刘伟
李潇菲
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China Petroleum and Chemical Corp
Sinopec Research Institute of Petroleum Engineering Shengli Co
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Sinopec Research Institute of Petroleum Engineering Shengli Co
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    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
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Abstract

The invention provides a microseism seismic facies identification and first arrival picking method based on time-of-arrival curve fitting, which comprises the following steps: step 1, establishing a time-of-arrival curve fitting formula by utilizing the arrival time of seismic phases recorded by perforating; step 2, selecting a sliding window along the fitting curve to calculate a similarity coefficient and stacking channel energy, and taking an energy weighting similarity coefficient value as a basis for the existence of the microseism signal; step 3, judging whether the residual time difference correction value of each channel is smaller than the size of a given time window; and 4, superposing the waveform records after the residual time difference correction meeting the conditions, and obtaining the accurate first arrival time of each track on the basis of the first arrival time of the superposed tracks. The microseism seismic facies identification and first arrival picking method based on time-of-arrival curve fitting can identify microseism events from actual data, achieve seismic facies first arrival picking, locate seismic sources by means of first arrival information of the identified events to obtain fracture parameters, and help people to know the development process of fracturing fractures in more detail.

Description

Microseism seismic facies identification and first arrival picking method based on time-arrival curve fitting
Technical Field
The invention relates to the technical field of oilfield development, in particular to a microseism seismic facies identification and first arrival picking method based on time-of-arrival curve fitting.
Background
The permeability of unconventional oil and gas reservoirs is extremely low, and a horizontal well and staged fracturing are required to be adopted for reservoir reconstruction in the process of exploitation. The microseism monitoring technology can monitor the hydraulic fracturing process in real time, utilizes monitoring data to position and induce microseism events, describes the geometrical morphology and the spatial distribution of the fracturing fracture development according to the distribution characteristics of the fracture event points, and has important significance for evaluating the fracturing modification effect and optimizing the later fracturing design and well position layout. In the microseism monitoring data processing process, microseism event identification and first arrival picking are the basis of microseism seismic source positioning, and the event positioning, seismic source mechanism analysis and crack imaging effects are directly influenced by the first arrival picking precision.
Microseismic event identification is based on differential identification of effective signals and environmental noise, and the attribute features commonly used for signal identification comprise energy, polarization, frequency spectrum, statistics, waveform similarity and the like. The long-time window energy ratio (STA/LTA) method based on the energy attribute is simple in principle and easy to implement, can meet the requirement of real-time processing, is a common method for processing microseism monitoring data, and has the defect that a satisfactory effect cannot be obtained in the identification of low signal-to-noise ratio events based on single-channel energy characteristics. The microseism event identification method based on the waveform similarity characteristics comprises a method of utilizing inter-event channel waveform similarity and a template matching identification method, can improve the identification capability of low signal-to-noise ratio event signals, and is an important means for identifying the microseism events at present. The micro-seismic event identification method based on template matching depends on the selection of a waveform template, and compared with a method based on inter-event-channel waveform similarity, the method has the characteristic of universality, but has the problems of low calculation efficiency and high rule interference false picking rate.
The microseismic first arrival pickup method can be classified into a single-track recording-based method and a multi-track recording-based method. The first arrival picking method based on single-channel recording can be realized by calculating and analyzing time-frequency distribution, instantaneous property, energy property, polarization property and the like of signals, and the error of individual channels is often larger when the first arrival picking method is applied to actual data with low signal-to-noise ratio. The cross-correlation method based on multi-channel recording fully utilizes the similar characteristics of the inter-channel recording, and can improve the quality of the first arrival pickup result. However, when the signal-to-noise ratio of the microseism record is low or a multi-seismic-phase signal exists, the peak value of the cross-correlation function is not obvious or a plurality of peak values with similar sizes continuously appear, the time corresponding to the maximum value cannot reliably represent the true target seismic phase position, and the corrected first arrival result is not ideal.
In the application No.: CN201910912330.0, chinese patent application, relates to a method, device and storage medium for picking up first arrival of microseism seismic facies identification, the method calculates energy weighting similarity coefficient along fitting curve to perform signal identification, can quickly and effectively identify microseism signal, calculates residual time difference correction value of target seismic facies on the basis of known seismic facies first arrival trend, sets constraint time window to effectively avoid large error condition of low signal-to-noise ratio trace record first arrival picking, and improves efficiency and accuracy of first arrival picking. Under the condition that an actual stratum velocity model is complex, an ideal result cannot be achieved by adopting a simple linear fitting method, and in addition, the application effect of the method can be influenced when the waveforms among the channels are different under the influence of the velocity model, the propagation path and a seismic source mechanism.
Aiming at the problems existing in the conventional micro-seismic facies identification and first arrival picking method, a novel micro-seismic facies identification and first arrival picking method is invented by combining the arrival time curve rule and inter-lane similarity characteristics of micro-seismic records, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide a microseism earthquake phase identification and first arrival picking method based on time-of-arrival curve fitting, which can identify microseism events from actual data, realize earthquake phase first arrival picking, position a seismic source by utilizing first arrival information of the identified events to obtain fracture parameters and help people to know the development process of a fracture in more detail.
The object of the invention can be achieved by the following technical measures: the microseism seismic facies identification and first arrival picking method based on time-of-arrival curve fitting comprises the following steps: step 1, establishing a time-of-arrival curve fitting formula by utilizing the arrival time of seismic phases recorded by perforating; step 2, selecting a sliding window along the fitting curve to calculate a similarity coefficient and stacking channel energy, and taking an energy weighting similarity coefficient value as a basis for the existence of the microseism signal; step 3, judging whether the residual time difference correction value of each channel is smaller than the size of a given time window; and 4, superposing the waveform records after the residual time difference correction meeting the conditions, and obtaining the accurate first arrival time of each track on the basis of the first arrival time of the superposed tracks.
The object of the invention can also be achieved by the following technical measures:
in step 1, a fitting formula of a time-of-arrival curve is established by a one-time curve fitting method according to the first-arrival travel time rule of the perforation record, wherein the fitting formula comprises the following steps:
t＝t0+K×dT×m.
wherein t is0For relative recording time, m is the detector number, K is the fitted travel time curve parameter, dT is the sampling time interval, and K × dT can be regarded as the inter-track time difference.
In step 2, defining the energy weighting similarity coefficient I calculation formula as
I＝Eq·S.
Wherein the index q is 0-1, and S is the total multichannel similarity coefficient
Figure BDA0002730038380000031
The calculation formula of the multichannel similarity coefficient of each component is as follows:
Figure BDA0002730038380000032
Sx,Sy,Szmultichannel similarity coefficient recorded for three components respectively
And E is the energy of the superposed trace obtained along the fitted curve, and the calculation formula is expressed as
Figure BDA0002730038380000033
Wherein M is the channel number, P is the fitting parameter,
Figure BDA0002730038380000034
respectively as the starting point and the end point of each time window,
Figure BDA0002730038380000035
for the length of the time window, Ax,Ay,AzThe amplitudes, t, of three components of the seismic wave in the time windowmFitting a curve to obtain a first arrival time, wherein n is a sampling time;
a threshold value of an energy weighting similarity coefficient is given in advance, if the obtained energy weighting similarity coefficient is larger than the threshold value, a valid micro-seismic signal exists near the corresponding time of the micro-seismic record, and the fitted curve reflects the trend of a real first arrival curve.
In step 3, the residual time difference recorded by each channel after the time difference correction is calculated based on the superposed channel obtained by the optimal fitting curve, a constraint time window is set, and whether the residual time difference correction value of each channel is smaller than the size of the given time window is judged.
In step 3, restricting first arrival picking by a certain time window on the basis of prior information of a arrival time curve to avoid mistaken picking of the first arrival of the low signal-to-noise ratio channel, and the steps comprise: obtaining time difference correction records and superposed tracks based on the optimal fitting curve; calculating the residual time difference of the superposed channel and each channel after the time difference correction; and setting a constraint time window, and judging whether the residual time difference correction value of each channel is smaller than the size of the given time window.
In step 4, each track which does not satisfy the constraint condition is obtained by interpolation when being recorded.
In step 4, the waveform recording after the residual time difference correction satisfying the condition, i.e., i ═ m, is superimposed, and the relative first arrival time T of the superimposed trace is obtained by the long-short energy ratio method0And obtaining the accurate first arrival time of each track on the basis of the first arrival time of the superposed tracks:
T(m)＝T(m)+ΔT(m)+T0
＝t+Kmax·dT·m+ΔT(m)+T0.
wherein t is the time for identifying the earthquake phase of the microseism, KmaxFor the fitting parameters, Δ T is the time difference correction for each pass, T0Is a relative first arrival time.
According to the microseism seismic facies identification and first arrival picking method based on time-of-arrival curve fitting, the energy weighting similarity coefficient is calculated along the fitting curve for signal identification, microseism signals (P wave S wave seismic facies) can be quickly and effectively identified, the residual time difference correction value of the target seismic facies is calculated on the basis of the known seismic facies first arrival trend, the constraint time window is set, the situation that the large error occurs in the first arrival picking of the trace records with low signal-to-noise ratio can be effectively avoided, the first arrival picking efficiency and accuracy are improved, and the method has important significance for improving the fracturing construction efficiency and improving the success rate of oil and gas reservoir reconstruction.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for microseismic seismic facies identification and first arrival picking based on time-of-arrival curve fitting of the present invention;
FIG. 2 is a schematic representation of a perforation record and its facies arrival time fit in an embodiment of the present invention;
FIG. 3 is a schematic representation of microseismic signal identification based on time-of-arrival curve fitting in an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a first arrival optimization of prior information based on arrival time curve trend in an embodiment of the present invention;
FIG. 5 is a graph comparing the first arrival results obtained by the method of the present invention with STA/LTA results in accordance with an embodiment 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.
The invention discloses a microseism seismic facies identification and first arrival picking method based on time-of-arrival curve fitting, which comprises the following steps of:
step 1, establishing a time-of-arrival curve fitting formula by utilizing the arrival time of seismic phases recorded by perforating;
the fitting formula of the arrival time curve established by a one-time curve fitting method according to the travel time rule of the first arrival of the perforation record is as follows:
t＝t0+K×dT×m.
wherein t is0For relative recording time, m is the detector number, K is the fitted travel time curve parameter, dT is the sampling time interval, and K × dT can be regarded as the inter-track time difference.
Step 2, selecting a sliding window along the fitting curve to calculate a similarity coefficient and stacking channel energy, and taking an energy weighting similarity coefficient value as a basis for the existence of the microseism signal;
defining the energy weighting similarity coefficient I as the calculation formula
I＝Eq·S.
Wherein the value of the index q is 0-1, and S is the total multichannel similarity coefficient
Figure BDA0002730038380000051
The calculation formula of the multichannel similarity coefficient of each component is as follows:
Figure BDA0002730038380000052
Sx,Sy,Szmultichannel similarity coefficient recorded for three components respectively
And E is the energy of the superposed trace obtained along the fitted curve, and the calculation formula is expressed as
Figure BDA0002730038380000053
Wherein M is the channel number, P is the fitting parameter,
Figure BDA0002730038380000061
respectively as the starting point and the end point of each time window,
Figure BDA0002730038380000062
for the length of the time window, Ax,Ay,AzThe amplitudes, t, of three components of the seismic wave in the time windowmFitting a curve to obtain a first arrival time, wherein n is a sampling time;
and presetting a threshold value of the energy weighted similarity coefficient, and if the obtained energy weighted similarity coefficient is larger than the threshold value, determining that a valid micro-seismic signal exists near the corresponding time of the micro-seismic record and the fitted curve reflects the true first-arrival curve trend.
Step 3, judging whether the residual time difference correction value of each channel is smaller than the size of a given time window;
and calculating the residual time difference recorded by each channel after the time difference correction based on the superposed channels obtained by the optimal fitting curve, setting a constraint time window, and judging whether the residual time difference correction value of each channel is smaller than the size of the given time window.
On the basis of prior information of a time-of-arrival curve, restricting first arrival pickup by a certain time window to avoid mistaken pickup of the first arrival of a low signal-to-noise ratio channel, and the method comprises the following steps of: obtaining time difference correction records and superposed tracks based on the optimal fitting curve; calculating the residual time difference of the superposed channel and each channel after the time difference correction; and setting a constraint time window, and judging whether the residual time difference correction value of each channel is smaller than the size of the given time window.
And 4, superposing the waveform records after the residual time difference correction meeting the conditions, and obtaining the accurate first arrival time of each track on the basis of the first arrival time of the superposed tracks. And when the tracks which do not meet the constraint condition are recorded, obtaining the tracks by interpolation.
Superposing the waveform records after the residual time difference correction meeting the conditions, namely i is m, and obtaining the relative first arrival time T of the superposed tracks by a long-short energy ratio method0And obtaining the accurate first arrival time of each track on the basis of the first arrival time of the superposed tracks:
T(m)＝T(m)+ΔT(m)+T0
＝t+Kmax·dT·m+ΔT(m)+T0.
wherein t is the time for identifying the earthquake phase of the microseism, KmaxFor the fitting parameters, Δ T is the time difference correction for each pass, T0Is a relative first arrival time.
In an embodiment of the invention, as shown in fig. 1, fig. 1 is a flowchart of a microseismic seismic facies identification and first arrival picking method based on a time-of-arrival curve fitting according to the invention.
At step 110, a time-of-arrival curve fitting formula is established using the perforation log or the arrival time of the seismic phase of the strong energy event signal.
For a uniform velocity model and a vertically arranged observation system, a first-arrival curve of seismic waves received by the geophones can be expressed as a parabola, and when the monitoring distance r is far greater than the spacing delta z of the geophones and the geophone groups are arranged on one side of the fracturing section, the first-arrival curve can be approximated to be a primary curve
tm＝km+d.
Where k, d are first order curve fitting parameters and d is relative time. The curve obtained by adopting the method of first-time curve fitting can approximately reflect the real trend of the time curve.
The microseism monitoring instruments in the well are adjacent in spatial position, and the geophone receives microseism seismic phases which show similar travel time characteristics on the record. The perforation first arrival information can represent the microseismic event seismic phase curve trend of the fracturing section to a certain extent. As shown in FIG. 2, the results of perforation recording and first arrival fitting are an example, the fluctuation of the formation in the monitored region is relatively small, the monitoring distance is far greater than the distance between the detectors, and the time difference between the same axial lines in the recording is not large. And obtaining an approximate first arrival curve by a primary curve fitting method according to the travel time rule of the first arrival of the perforation record. The fitting formula is
t＝t0+K×dT×m.
Wherein t is0For relative recording time, m is the detector number, K is the fitted travel time curve parameter, dT is the sampling time interval, and K × dT can be regarded as the inter-track time difference. The circles and the triangles are P wave S wave first arrival information respectively, and the solid lines and the dotted lines are corresponding linear fitting results.
At step 120, selecting a sliding window along the fitting curve to calculate the similarity coefficient and the energy of the superimposed trace may include the following steps: selecting the length of a sliding time window; and moving sampling points one by one on the continuous seismic records along a time axis by using the fitted curve, and continuously modifying the curve through the fitted parameters.
And moving sampling points one by one on the continuous seismic records along a time axis by using the fitted curve, and continuously modifying the curve through the fitted parameters. Calculating a plurality of similar coefficients within a certain time window range by taking different travel time curves as starting points in the moving process, wherein the calculation formula of the plurality of similar coefficients is as follows:
Figure BDA0002730038380000071
wherein M is the channel number, P is the fitting parameter,
Figure BDA0002730038380000072
respectively as the starting point and the end point of each time window,
Figure BDA0002730038380000081
for the length of the time window, Ax,Ay,AzThe amplitudes, S, of three components of the seismic wave in the time windowx,Sy,SzA plurality of channels of similarity coefficients recorded for the three components, respectively. When defining the total multi-channel similarity coefficient as
Figure BDA0002730038380000082
When the fitting curve is close to the real first arrival, the waveform energy of the superposed trace obtained by the effective microseismic signal along the fitting curve is enhanced, the random noise energy is suppressed, and the obtained superposed trace can be expressed as
Figure BDA0002730038380000083
Wherein t ismThe first arrival time of the fitted curve is obtained. The multichannel similarity coefficient does not consider the energy characteristics of the signal, so the multichannel similarity coefficient is sensitive to some correlated environment noises with weak energy. To avoid false picking of the non-microseismic signals, an energy weighting similarity coefficient I is defined as
I＝Eq·S.
Wherein the value of the index q is 0-1, E is the energy of a superposed channel obtained along a fitting curve, and a calculation formula can be expressed as
Figure BDA0002730038380000084
A threshold value of the energy weighting similarity coefficient is given in advance, if the obtained energy weighting similarity coefficient is larger than the threshold value, a valid micro-seismic signal exists nearby the corresponding time of the micro-seismic record, and the fitted curve reflects the real first-arrival curve trend. As shown in fig. 3, it can be seen from fig. 3 that an effective signal exists in the time window, because the effective signal has a certain inter-trace similarity, the multi-trace similarity coefficient in the time window is large, and the energy of the stacked trace waveform obtained by the effective micro-seismic signal along the fitted curve is enhanced, and the random noise energy is suppressed. It is clear from the energy weighted similarity coefficient profile in FIG. 3 that there are 1 local poleA large value area can identify the microseism seismic phase moment t and the position K corresponding to the fitting parametermax.
At step 130, since the time and the event trend curve of the identified seismograph can only approximately reflect the first arrival position of the seismic wave, further optimization of the first arrival of the seismograph is still required. And the false picking of the first arrivals of the low signal-to-noise ratio channel can be avoided by restricting the first arrivals with a certain time window on the basis of the prior information of the arrival time curve. The method comprises the following steps: obtaining a time difference correction record and a superposition channel based on the optimal fitting curve; calculating the residual time difference of the superposed channel and each channel after the time difference correction; and setting a constraint time window, and judging whether the residual time difference correction value of each channel is smaller than the size of the given time window. The microseism signal identification time and the corresponding fitting parameters are substituted into a formula to calculate the arrival time trend curve of the microseism event, and in order to obtain more accurate first arrival information, the residual time difference correction value of the first arrival time of each channel needs to be obtained by using the superposed channel, as shown in fig. 4. The recording after the preliminary time difference correction is as shown in fig. 4a, and fig. 4a shows the time difference correction recording of the P-wave seismic phase Z component recording, and then the relative residual time difference of each channel is calculated by using the superposed channel to obtain the recording after the time difference correction and the waveform of the final superposed channel. Setting a constraint time window delta T, and judging whether each time difference correction quantity | delta T (m) | is smaller than delta T. If yes, keeping the track i-m; otherwise j ═ m.
In step 140, the waveform records after the residual time difference correction (i.e., i ═ m) satisfying the condition are superimposed, and the relative first arrival time T of the superimposed trace can be obtained by the long-short energy ratio method0Obtaining the accurate first arrival time of each track on the basis of the first arrival time of the superposed tracks
T(m)＝T(m)+ΔT(m)+T0
＝t+Kmax·dT·m+ΔT(m)+T0.
And obtaining the recorded time of each track which does not meet the constraint condition through interpolation.
In order to compare with the application effect of the method, the STA/LTA method is also adopted to carry out first arrival picking processing on the actual data. Fig. 5a shows the first-arrival result obtained by the method of the present invention, and the recording with the time difference corrected by the first-arrival result is shown in fig. 5c, and fig. 5b shows the first-arrival result obtained by the STA/LTA method, and the recording with the time difference corrected by the first-arrival result is shown in fig. 5 d. The waveform alignment in the record after time difference correction can explain the effect of first arrival picking, and the first arrival results obtained by comparing the two methods can show that certain errors exist in the low signal-to-noise ratio channel of the first arrival time obtained by using the energy ratio method.

Claims (7)

1. The microseism seismic facies identification and first arrival picking method based on time-of-arrival curve fitting is characterized by comprising the following steps of:
step 1, establishing a time-of-arrival curve fitting formula by utilizing the arrival time of seismic phases recorded by perforating;
step 2, selecting a sliding window along the fitting curve to calculate a similarity coefficient and stacking channel energy, and taking an energy weighting similarity coefficient value as a basis for the existence of the microseism signal;
step 3, judging whether the residual time difference correction value of each channel is smaller than the size of a given time window;
and 4, superposing the waveform records after the residual time difference correction meeting the conditions, and obtaining the accurate first arrival time of each track on the basis of the first arrival time of the superposed tracks.
2. The microseism seismic facies identification and first arrival picking method based on the arrival time curve fitting as claimed in claim 1, wherein in step 1, the fitting formula of the arrival time curve is established by a first curve fitting method according to the travel time rule of the first arrival of the perforation record as follows:
t＝t0+K×dT×m.
wherein t is0For relative recording time, m is the detector number, K is the fitted travel time curve parameter, dT is the sampling time interval, and K × dT can be regarded as the inter-track time difference.
3. The method for microseism seismic facies identification and first arrival picking based on arrival time curve fitting as claimed in claim 1, wherein in step 2, an energy weighted similarity coefficient I calculation formula is defined as
I＝Eq·S.
Wherein the index q is 0-1, and S is the total multichannel similarity coefficient
Figure FDA0002730038370000011
The calculation formula of the multichannel similarity coefficient of each component is as follows:
Figure FDA0002730038370000012
Sx,Sy,Sza plurality of channel similarity coefficients recorded for the three components respectively;
e is the energy of the superimposed trace obtained along the fitted curve, and the calculation formula is expressed as
Figure FDA0002730038370000021
Wherein M is the channel number, P is the fitting parameter,
Figure FDA0002730038370000022
respectively as the starting point and the end point of each time window,
Figure FDA0002730038370000023
for the length of the time window, Ax,Ay,AzThe amplitudes, t, of three components of the seismic wave in the time windowmFitting a curve to obtain a first arrival time, wherein n is a sampling time;
and presetting a threshold value of the energy weighted similarity coefficient, and if the obtained energy weighted similarity coefficient is larger than the threshold value, determining that a valid micro-seismic signal exists near the corresponding time of the micro-seismic record and the fitted curve reflects the true first-arrival curve trend.
4. The method for microseism seismographic identification and first arrival picking based on arrival time curve fitting as claimed in claim 1, wherein in step 3, the residual time difference recorded in each channel after time difference correction is calculated based on the superposed channel obtained by the best fit curve, a constraint time window is set, and whether the residual time difference correction value of each channel is smaller than the size of the given time window is judged.
5. The method for microseism seismic facies identification and first arrival picking based on arrival time curve fitting of claim 4, wherein in step 3, the first arrival picking is constrained by a timing window based on the prior information of the arrival time curve to avoid the false picking of the first arrival of the low signal-to-noise ratio trace, the steps include: obtaining a time difference correction record and a superposition channel based on the optimal fitting curve; calculating the residual time difference of the superposed channel and each channel after the time difference correction; and setting a constraint time window, and judging whether the residual time difference correction value of each channel is smaller than the size of the given time window.
6. The method for microseism seismic facies recognition and first arrival picking based on arrival time curve fitting as claimed in claim 1, wherein in step 4, the recorded arrival times of each track which do not meet the constraint condition are obtained by interpolation.
7. The method for microseism seismic facies recognition and first arrival picking based on arrival time curve fitting as claimed in claim 1, wherein in step 4, the waveform records i-m after residual moveout correction satisfying the condition are stacked, and the relative first arrival time T of the stacked channel is obtained by a long-short energy ratio method0And obtaining the accurate first arrival time of each track on the basis of the first arrival time of the superposed tracks:
T(m)＝T(m)+ΔT(m)+T0
＝t+Kmax·dT·m+ΔT(m)+T0.
wherein t is the time for identifying the earthquake phase of the microseism, KmaxAs fitting parametersΔ T is the time difference correction value of each track, T0Is a relative first arrival time.
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