CN110333530A - A new microseismic event detection method and system - Google Patents

Abstract

The embodiment of the present invention discloses a new microseismic event detection method and system, the method includes: step 1, input the measured microseismic signal sequence S; step 2, determine the shear percentage p, the first shear coefficient The first shear amount ν ₁ and the second shear amount ν ₂ . Step 3, detect microseismic events according to the objective function, specifically: if Then there is a microseismic event at the K point of the microseismic signal sequence, otherwise there is no microseismic event; wherein is the objective function, and Wherein _si is the i-th data in the microseismic signal sequence; ν ₁ is the first shear amount; ν ₂ is the second shear amount; is the first shear coefficient; Ω(p) is the shear set; p is the shear percentage.

Description

A new microseismic event detection method and system

technical field

The invention relates to the petroleum field, in particular to a method and system for detecting microseismic events.

Background technique

Hydraulic fracturing microseismic monitoring technology is an important new technology developed in the fields of low permeability reservoir fracturing, reservoir driving and water flooding front in recent years, and it is also an important supporting technology for shale gas development. This technology arranges a multi-level three-component geophone arrangement in adjacent wells to monitor the microseismic events generated in the hydraulic fracturing process of the target interval of the fracturing well, and inverts the microseismic events to obtain parameters such as the source position, so as to describe the hydraulic fracturing The geometry and spatial distribution of fracture growth during the process provide real-time information on the length, height, width and orientation of fractures produced by hydraulic fracturing, realizing the industrialized development of shale gas. Microseismic detection of hydraulic fracturing is a hot and difficult point in scientific research in the field of shale gas development. Considering the needs of the society and the country, it is very important to carry out research on the microseismic monitoring system, which has great social and economic value.

An important task in the microseismic monitoring system is the location of microseismic events. Positioning accuracy is the most important factor affecting the application effect of the microseismic monitoring system, while the accuracy of microseismic event positioning mainly depends on the accuracy of reading the first arrival (also called the first arrival) of fluctuations and other related factors.

But the problem is that picking up the first arrival is not as simple as imagined. Affected by the mining of ground instruments and geological structures, rock fracture forms are very complex, and then produce microseismic fluctuations of various forms and energies. There are differences, and there is a huge difference in the shape of the waveform near the position of the first arrival. This inconsistency of the waveform characteristics makes it very difficult to pick up the first arrival. Further research also shows that the focal mechanism of microseisms will also affect the characteristics of the first arrival point: most of the microseismic fluctuations generated by the shearing action of hard rock have large energy, high main frequency, short delay, and the maximum peak position closely follows the initial first arrival. The first arrival point of the wave is clear, the take-off delay is short, and it is easier to pick up; but most of the microseismic fluctuations generated by the stretching effect have low energy, low main frequency, long delay, slow take-off, and relatively uniform energy distribution. The amplitude at the location is small, and it is easy to be submerged by interference signals. The characteristics of the first arrival point are inconsistent, and it is not easy to pick up the first arrival. However, the microseismic fluctuations generated by soft rocks have concentrated energy distribution, blurred initial first arrival points, and indistinct boundaries. Obviously different from hard rock, the first arrival is also more difficult to pick up. At the same time, according to foreign research, because the P wave velocity is greater than the S wave velocity, many algorithms take it for granted that the first arrival wave is a P wave, but the reality may be more complicated: the first arrival may be a P wave, or an S wave, or even an S wave. There may also be outliers. According to research, 41% of the first arrivals are S waves, and 10% of the first arrivals are caused by outliers. All these have brought considerable difficulty to the first arrival pick-up.

In addition to the complex characteristics of the first arrival point, the first arrival picking also faces another greater challenge: the microseismic records are massive data. For example, nearly 10,000 microseismic events were recorded in a test area in January 2005. At the same time, in order to meet the production demand, the microseismic monitoring system needs to record continuously 24 hours a day. Not only that, a large part of these data are noises and disturbances caused by human or mechanical activities, and have nothing to do with microseisms. The literature further divides noise into three basic types: high-frequency (>200Hz) noise, caused by various job-related activities; low-frequency noise (<10Hz), usually caused by machine activities far away from the recording site, and industrial current ( 50Hz). In addition, the microseismic signal itself is not pure. For example, Chinese scholar Professor Dou Linming believes that the microseismic signal includes multiple signals.

Therefore, how to identify microseismic events and pick up first arrivals from massive data is the basis of microseismic data processing. In contrast, manual methods are often used in production, which is time-consuming and labor-intensive, with poor accuracy and reliability. The picking quality cannot be guaranteed, and massive data cannot be processed. Automatic picking of first arrivals is one of the solutions. Automatic picking of first arrivals of microseismic fluctuations is one of the key technologies in microseismic monitoring data processing, and it is also a technical difficulty in realizing automatic positioning of microseismic sources.

In the common microseismic event detection methods, the determination of the judgment threshold is relatively random, and there is no unified criterion. There are great limitations in its universal applicability, especially when the signal-to-noise ratio is low, the performance of the algorithm is greatly affected.

Contents of the invention

The object of the present invention is to provide a new microseismic event detection method and system. The proposed method utilizes the difference in the shear space between the microseismic signal and background noise (including amplitude anomalies), and uses this difference to eliminate The influence of background noise (including amplitude anomalies) can be used to correctly determine the time of microseismic events. The proposed method is robust and computationally simple.

To achieve the above object, the present invention provides the following scheme:

A new microseismic event detection method comprising:

Step 1, input the measured microseismic signal sequence S;

Step 2, determine the shear percentage p, the first shear coefficient l _i ¹ , the first shear amount ν ₁ , and the second shear amount ν ₂ . Specifically:

Wherein s _i is the i-th data in the microseismic signal sequence.

Step 3, detect microseismic events according to the objective function, specifically: if Then there is a microseismic event at the K point of the microseismic signal sequence, otherwise there is no microseismic event; wherein is the objective function, and Wherein _si is the i-th data in the microseismic signal sequence; ν ₁ is the first shear amount; ν ₂ is the second shear amount; is the first shear coefficient; Ω(p) is the shear set; p is the shear percentage.

A new microseismic event detection system comprising:

The acquisition module inputs the measured microseismic signal sequence S;

The calculation module calculates the shear percentage p, the first shear coefficient l _i ¹ , the first shear amount ν ₁ , and the second shear amount ν ₂ . Specifically:

Wherein s _i is the i-th data in the microseismic signal sequence.

The judging module detects microseismic events according to the objective function, specifically: if Then there is a microseismic event at the K point of the microseismic signal sequence, otherwise there is no microseismic event; wherein is the objective function, and Wherein _si is the i-th data in the microseismic signal sequence; ν ₁ is the first shear amount; ν ₂ is the second shear amount; is the first shear coefficient; Ω(p) is the shear set; p is the shear percentage.

According to the specific embodiments provided by the invention, the invention discloses the following technical effects:

In the common microseismic event detection methods, the determination of the judgment threshold is relatively random, and there is no unified criterion. There are great limitations in its universal applicability, especially when the signal-to-noise ratio is low, the performance of the algorithm is greatly affected.

The object of the present invention is to provide a new microseismic event detection method and system. The proposed method utilizes the difference in the shear space between the microseismic signal and background noise (including amplitude anomalies), and uses this difference to eliminate The influence of background noise (including amplitude anomalies) can be used to correctly determine the time of microseismic events. The proposed method is robust and computationally simple. Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings that need to be used in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention, and those skilled in the art can also obtain other accompanying drawings according to these drawings without any creative work.

Fig. 1 is a schematic flow sheet of the present invention;

Fig. 2 is a structural representation of the present invention;

Fig. 3 is a schematic flow chart of a specific implementation case of the present invention.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

Figure 1 Schematic flow chart of a new microseismic event detection method

Fig. 1 is a schematic flow chart of the present invention. As shown in Figure 1, described a kind of new microseismic event detection method specifically comprises the following steps:

Step 1, input the measured microseismic signal sequence S;

Step 2, determine the shear percentage p, the first shear coefficient l _i ¹ , the first shear amount ν ₁ , and the second shear amount ν ₂ . Specifically:

Wherein s _i is the i-th data in the microseismic signal sequence.

Step 3, detect microseismic events according to the objective function, specifically: if Then there is a microseismic event at the K point of the microseismic signal sequence, otherwise there is no microseismic event; wherein is the objective function, and Wherein _si is the i-th data in the microseismic signal sequence; ν ₁ is the first shear amount; ν ₂ is the second shear amount; is the first shear coefficient; Ω(p) is the shear set; p is the shear percentage.

Before the step 3, the method also includes:

Step 4, obtain the cut set Ω(p).

Said step 4 includes:

Step 401, initialize the cut set, specifically:

Ω(p)={s _i ,i=1,2,…,N}

Step 402, iteratively obtain the cut set, specifically:

The first step is to update the first clipping component and the second clipping component, specifically:

in

k=1,2

The second step updates the first shear coefficient, specifically:

The third step is to update the cut collection, specifically:

Represents rounding down to *

Repeat these three steps until the number of elements in the cut set Ω(p) does not change, the iteration terminates, and the final cut set Ω(p) is obtained.

Figure 2 Schematic diagram of a new microseismic event detection system

Fig. 2 is a structural schematic diagram of the present invention. As shown in Figure 2, described a kind of new microseismic event detection system comprises following structure:

Acquisition module 501, input the measured microseismic signal sequence S;;

The calculation module 502 calculates the shear percentage p, the first shear coefficient l _i ¹ , the first shear amount ν ₁ , and the second shear amount ν ₂ . Specifically:

Wherein s _i is the i-th data in the microseismic signal sequence.

The judging module 503 detects microseismic events according to the objective function, specifically: if Then there is a microseismic event at the K point of the microseismic signal sequence, otherwise there is no microseismic event; wherein is the objective function, and Wherein _si is the i-th data in the microseismic signal sequence; ν ₁ is the first shear amount; ν ₂ is the second shear amount; is the first shear coefficient; Ω(p) is the shear set; p is the shear percentage.

The system also includes:

The iteration module 504 is used to obtain the cut set Ω(p).

A specific implementation case is provided below to further illustrate the solution of the present invention

Fig. 3 is a schematic flow chart of a specific implementation case of the present invention. As shown in Figure 3, it specifically includes the following steps:

1. Input the measured microseismic signal sequence S

S＝[s ₁ ,s ₂ ,…,s _N-1 ,s _N ]

in:

S: The measured microseismic signal sequence, the length is N

s _i ,i=1,2,…,N: measured microseismic signal with serial number i

2. Determine algorithm parameters

Determine the shear percentage p, the first shear coefficient The first shear amount ν ₁ and the second shear amount ν ₂ .

Specifically:

Wherein s _i is the i-th data in the microseismic signal sequence.

3. Initialize the clipping collection

Ω(p)={s _i , i=1, 2, . . . , N}.

4. Iteratively obtain the cut set

The first step is to update the first clipping component and the second clipping component, specifically:

in

k=1,2

The second step updates the first shear coefficient, specifically:

The third step is to update the cut collection, specifically:

Represents rounding down to *

Repeat these three steps until the number of elements in the cut set Ω(p) does not change, the iteration terminates, and the final cut set Ω(p) is obtained.

5. Judging microseismic events and their occurrence time

Detect microseismic events according to the objective function, specifically: if Then there is a microseismic event at the Kth point of the microseismic signal sequence, otherwise there is no microseismic event; where is the objective function, and Wherein _si is the i-th data in the microseismic signal sequence; ν ₁ is the first shear amount; ν ₂ is the second shear amount; is the first shear coefficient; Ω(p) is the shear set; p is the shear percentage.

Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for the related parts, please refer to the description of the method part.

In this paper, specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; meanwhile, for those of ordinary skill in the art, according to the present invention Thoughts, there will be changes in specific implementation methods and application ranges. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims (5)

1. a kind of new microseismic event detection method characterized by comprising

Step 1, the microseismic signals sequence S of actual measurement is inputted；

Step 2, shearing percentage p, the first shearing factor are determinedFirst shearing displacement ν₁, the second shearing displacement ν₂.Specifically:

Wherein s_iFor i-th of data in the microseismic signals sequence.

Step 3, microseismic event is detected according to objective function, specifically: ifSo believe in the microseism The K point of number sequence has microseismic event, otherwise without microseismic event；WhereinIt is the objective function, andWherein s_iFor i-th of data in the microseismic signals sequence；ν₁It is described First shearing displacement；ν₂For second shearing displacement；For first shearing factor；Ω (p) is shearing set；P is described Shear percentage.

2. the method according to claim 1, wherein before the step 3, the method also includes:

Step 4, the shearing set omega (p) is sought.

3. according to the method described in claim 2, it is characterized in that, the step 4 includes:

Step 401, initialization shearing set, specifically:

Ω (p)={ s_i, i=1,2 ..., N }

Step 402, iteration seeks the shearing set, specifically:

The first step updates first shear component and second shear component, specifically:

Wherein

K=1,2

Second step updates first shearing factor, specifically:

Third step updates the shearing set, specifically:

It indicates to being rounded under *

These three steps are constantly repeated, until the element number inside shearing set omega (p) no longer changes, iteration is whole Only, and final shearing set omega (p) is obtained.

4. a kind of new microseismic event detection system characterized by comprising

Module is obtained, the microseismic signals sequence S of actual measurement is inputted；；

Computing module calculates shearing percentage p, the first shearing factorFirst shearing displacement ν₁, the second shearing displacement ν₂.Specifically:

Wherein s_iFor i-th of data in the microseismic signals sequence.

Judgment module detects microseismic event according to objective function, specifically: ifSo described micro- The K point of shake signal sequence has microseismic event, otherwise without microseismic event；WhereinIt is the objective function, and AndWherein s_iFor i-th of data in the microseismic signals sequence；ν₁It is One shearing displacement；ν₂For the second shearing displacement；For the first shearing factor；Ω (p) is shearing set；P is shearing percentage.

5. system according to claim 4, which is characterized in that further include:

Iteration module seeks the shearing set omega (p).