Method for quickly and accurately identifying microseism event and accurately picking up first arrival

Abstract

The invention discloses a microseism event rapid and accurate identification and first arrival accurate pickup method, which comprises the following steps of: calculating an energy ratio of a rear time window to a front time window at a current time point through an energy ratio method, and judging whether the energy ratio is greater than a given hard threshold value C or not; identifying whether partial signals behind the time point are microseismic event signals, if so, entering a next pickup module, and if not, circulating the next moment; when the signal behind the point is judged to belong to the microseismic event signal, calculating the AIC of the signal in a time window around the point, detecting the time corresponding to the minimum AIC value, namely the wave arrival time of the microseismic event signal, outputting a microseismic event, moving the cycle time by one time window in sequence, and carrying out subsequent microseismic signal identification and wave arrival time pickup; according to the invention, the time window energy ratio and AIC two-step method is combined to identify the micro-seismic event and pick up the wave arrival time, so that the rapid identification and detection of the micro-seismic event can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of microseismic monitoring, and in particular to a method for rapid and accurate identification and first-arrival precise picking of microseismic events. Background Art

[0002] Microseismic monitoring technology is an important new technology in the field of fracturing transformation of low permeability oil and gas reservoirs in recent years. This technology uses detectors in adjacent wells to monitor the microseismic waves induced by the fracturing wells during the fracturing process to describe the geometry and spatial distribution of the fracture growth during the fracturing process. It can provide real-time information on the height, length and azimuth of the fractures generated by the fracturing construction. This information can be used to optimize the fracturing design, well pattern or other oilfield development measures, thereby improving the recovery rate.

[0003] The current method for identifying microseismic events is the time window energy ratio method. The time window energy ratio method has the advantages of being simple, direct, and effective in identifying microseismic events, but the selection of the time window has a great influence on the wave arrival time picking; in addition, a local minimum value will appear at the AIC position corresponding to each microseismic event in the microseismic record. By picking the time corresponding to these local minimum values, the wave arrival time of the microseismic event can be obtained. However, the position where the AIC value has a local minimum value is not necessarily a microseismic event. For example, in a pure noise record without microseismic events, there will always be a minimum AIC value. Therefore, the AIC method cannot be used to identify microseismic events and can only be used to pick the wave arrival time of microseismic events.

[0004] In order to solve the above problems, the present invention provides a method for quickly and accurately identifying microseismic events and accurately picking up the first arrival, so as to solve the problem of low efficiency in identifying microseismic events in the past. Summary of the invention

[0005] The purpose of the present invention is to provide a method for quickly and accurately identifying microseismic events and accurately picking up the first arrival of microseismic events, so as to achieve the purpose of quickly identifying and detecting microseismic events.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for quickly and accurately identifying microseismic events and accurately picking up the first arrival includes the following steps:

[0008] The energy ratio of the rear window to the front window at the current time point is calculated by the energy ratio method, and it is determined whether the energy ratio is greater than a given hard threshold C;

[0009] Identify whether the signal after this time point is a microseismic event signal. If so, enter the next step of the picking module, otherwise loop to the next moment;

[0010] When it is determined that the signal behind the point belongs to a microseismic event signal, the AIC of the signal in a time window around the point is calculated, and the time corresponding to the minimum AIC value is detected, which is the arrival time of the microseismic event signal. The microseismic event is output, and the cycle time is moved by a time window for subsequent microseismic signal identification and the picking of its arrival time.

[0011] Preferably, the basis for identifying whether it is a microseismic event signal is that the signal energy is greater than the background noise signal energy.

[0012] Preferably, for a given time series [xn] of length N, it can be expressed as follows using the AR model:

[0013]

[0014] Where M represents the order of the AR model, ε n It means that the mean is zero and the variance is A stationary white noise sequence, a m is the coefficient;

[0015] The AIC value of this model is:

[0016]

[0017] Where K represents the order of the AR model, Represents the variance of the series.

[0018] Preferably, in the microseismic data record, since the noise component and the microseismic signal component have different statistical properties, the noise component and the microseismic signal component are respectively regarded as a stationary process and represented by an AR model. Assuming that the kth point is the best boundary between the noise component and the microseismic signal component, the signal is divided into two segments at the kth point, and the corresponding AIC value can be expressed as:

[0019] AIC(k)=(kM)lg(σ1 2 )+(NkM)lg(σ2 2 )+C

[0020] In the above formula, σ1 and σ2 represent the variance of noise data and microseismic signal data respectively, N represents the length of the time series, and M represents the order of the AR model.

[0021] Preferably, when M is much smaller than the number of points, the corresponding AIC value can be expressed as:

[0022] AIC(k)=klg(var(x[1,k]))+(Nk-1)lg(var(x[k+1,N]))+C

[0023] Here, var represents the variance of the data series.

[0024] Compared with the prior art, the present invention has achieved the following technical effects:

[0025] 1. The present invention combines the time window energy ratio and the AIC two-step method to identify microseismic events and pick up wave arrival times, which can realize rapid identification and detection of microseismic events. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0027] Figure 1 is a flow chart of the present invention;

[0028] Figure 2 (a) Signals containing microseismic events;

[0029] Figure 2 (b) The first step is to use the back-front time window energy ratio method to identify microseismic events and the initial picked wave arrival time results;

[0030] Figure 2 (c) The second step uses the local AIC method to accurately pick the microseismic event wave arrival time diagram and picking results. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] The purpose of the present invention is to provide a method for quickly and accurately identifying microseismic events and accurately picking up the first arrival of microseismic events, so as to achieve the purpose of quickly identifying and detecting microseismic events.

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] refer to Figure 1 to Figure 2 A method for quickly and accurately identifying microseismic events and accurately picking up the first arrival includes the following steps:

[0035] The energy ratio of the rear window to the front window at the current time point is calculated by the energy ratio method, and it is determined whether the energy ratio is greater than a given hard threshold C;

[0036] Identify whether the signal after this time point is a microseismic event signal. If so, enter the next step of the picking module, otherwise loop to the next moment;

[0037] When it is determined that the signal behind the point belongs to a microseismic event signal, the AIC of the signal in a time window around the point is calculated, and the time corresponding to the minimum AIC value is detected, which is the arrival time of the microseismic event signal. The microseismic event is output, and the cycle time is moved by a time window for subsequent microseismic signal identification and the picking of its arrival time.

[0038] Furthermore, the basis for identifying whether it is a microseismic event signal is that the signal energy is greater than the background noise signal energy.

[0039] Furthermore, for a given time series [xn] of length N, the AR model can be expressed as:

[0040]

[0041] Where M represents the order of the AR model, ε n It means that the mean is zero and the variance is A stationary white noise sequence, a m is the coefficient;

[0042] The AIC value of this model is:

[0043]

[0044] Where K represents the order of the AR model, Represents the variance of the series.

[0045] Furthermore, in the microseismic data record, since the noise component and the microseismic signal component have different statistical properties, the noise component and the microseismic signal component are regarded as a stationary process respectively and represented by the AR model. Assuming that the kth point is the best boundary between the noise component and the microseismic signal component, the signal is divided into two segments at the kth point, and the corresponding AIC value can be expressed as:

[0046] AIC(k)=(kM)lg(σ1 2 )+(NkM)lg(σ2 2 )+C

[0047] In the above formula, σ1 and σ2 represent the variance of noise data and microseismic signal data respectively, N represents the length of the time series, and M represents the order of the AR model.

[0048] Furthermore, when M is much smaller than the number of points, the corresponding AIC value can be expressed as:

[0049] AIC(k)=klg(var(x[1,k]))+(Nk-1)lg(var(x[k+1,N]))+C

[0050] Among them, var represents the variance of the data sequence. At the actual arrival time of the microseismic event, due to the large difference in the statistical properties of the noise signal and the microseismic signal, the fit of the two signals is the worst in the least square sense, and the corresponding AIC value is the smallest. Based on this, many researchers calculate AIC and select the point corresponding to the minimum AIC value as the dividing point between two different stationary sequences.

[0051] A segment of signal containing microseismic events was intercepted from the actual field data of this fracturing microseismic monitoring to conduct method testing and comparative analysis. The specific process is as follows: Figure 2 shown.

[0052] Figure 2 (a) is a segment of the signal containing a microseismic event. The artificially picked-up arrival time of the microseismic event is 493ms ( Figure 2 (a) The line corresponding to the amplitude is marked). After testing, the signal-to-noise ratio of this signal is as high as 16dB, which is a high signal-to-noise ratio signal. Therefore, the manually picked wave arrival time is relatively accurate. Figure 2 (b) is the first step of the two-step method, using the back-to-front time window energy ratio method to identify microseismic events (the threshold in this case is 5, that is, a microseismic event is identified when the back-to-front time window energy ratio is greater than 5), and roughly determining the arrival time of the microseismic event (in this case, the arrival time is 487ms when the time window length is 100ms); Figure 2 (c) The second step is to use the local AIC method to accurately pick up the wave arrival time. After the microseismic event is identified in the first step and the wave arrival time is roughly determined, a time window is taken around this time point and the AIC value of the signal in the time window is calculated. The time corresponding to the minimum AIC value is the wave arrival time (the wave arrival time picked in this example is 493ms, which is consistent with the manually picked wave arrival time).

[0053] Table 1 Test analysis of the effect of the time window length of the original microseismic event signal on the picking results

[0054]

[0055] Table 1 shows the test and analysis results of the effect of the time window length of microseismic event signals on the picking results. From the picking results, it can be seen that the time window energy ratio method is greatly affected by the time window size, and the picking time is relatively small; the two-step method is not affected by the time window size, and the picked wave arrival time is almost the same as the manually picked wave arrival time, with high accuracy. For microseismic signal data with high signal-to-noise ratio, the two-step method is not affected by the time window size, and the picked wave arrival time has high accuracy; in the case of low signal-to-noise ratio, the picking accuracy is reduced, the time window length has almost no effect, and the performance is relatively stable.

[0056] Adaptive changes made according to actual needs are all within the protection scope of the present invention.

[0057] It should be noted that it is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-restrictive from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the invention. Any reference numeral in a claim should not be regarded as limiting the claim to which it relates.

Claims

1. A method for rapid and accurate identification of microseismic events and accurate first arrival picking, characterized in that: The following steps are involved: The energy ratio of the rear window to the front window at the current time point is calculated by the energy ratio method, and it is determined whether the energy ratio is greater than a given hard threshold C; Identify whether the signal after this time point is a microseismic event signal. If so, enter the next step of the picking module, otherwise loop to the next moment; When it is determined that the signal behind the point belongs to a microseismic event signal, the AIC of the signal in a time window around the point is calculated, and the time corresponding to the minimum AIC value is detected, which is the arrival time of the microseismic event signal. The microseismic event is output, and the cycle time is moved by a time window for subsequent microseismic signal identification and the picking of its arrival time.

2. A method for rapid and accurate identification and first arrival accurate picking of microseismic events according to claim 1, characterized in that: The basis for identifying whether it is a microseismic event signal is that the signal energy is greater than the background noise signal energy.

3. A method for rapid and accurate identification and first arrival accurate picking of microseismic events according to claim 1, characterized in that: For a given time series [xn] of length N, the AR model can be expressed as: Where M represents the order of the AR model, ε n It means that the mean is zero and the variance is A stationary white noise sequence, a m is the coefficient; The AIC value of this model is: Where K represents the order of the AR model, Represents the variance of the series.

4. A method for rapid and accurate identification and first arrival accurate picking of microseismic events according to claim 1, characterized in that: In microseismic data records, since the noise component and the microseismic signal component have different statistical properties, the noise component and the microseismic signal component are regarded as a stationary process respectively and represented by the AR model. Assuming that the kth point is the best boundary between the noise component and the microseismic signal component, the signal is divided into two segments at the kth point, and the corresponding AIC value can be expressed as: AIC(k)=(k-M)lg(σ1 2 )+(N-k-M)lg(σ2 2 )+C In the above formula, σ1 and σ2 represent the variance of noise data and microseismic signal data respectively, N represents the length of the time series, and M represents the order of the AR model.

5. A method for rapid and accurate identification and first arrival accurate picking of microseismic events according to claim 4, characterized in that: When M is much smaller than the number of points, the corresponding AIC value can be expressed as: AIC(k)=klg(var(x[1,k]))+(Nk-1)lg(var(x[k+1,N]))+C Here, var represents the variance of the data series.

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