CN110703319B - Microseismic event detection method and system based on Khichin-Einstein theorem - Google Patents

Abstract

The embodiment of the invention discloses a method and a system for detecting microseismic events based on a Khichin-Einstein theorem, wherein the method comprises the following steps: step 1, inputting an actually measured microseismic signal sequence S; step 2, detecting a microseismic event according to the Khinchn-Einstein theorem; the method specifically comprises the following steps: if the logarithmized window energy E at the K point_K≤2.595log[1+f_MIN]Detecting a microseismic event at the Kth point of the signal sequence S; otherwise, no microseismic event is detected. Wherein f is_MINIs the minimum frequency of the signal sequence S.

Description

Microseismic event detection method and system based on Khichin-Einstein theorem

Technical Field

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

Background

The hydraulic fracturing microseismic monitoring technology is an important new technology developed in the fields of low-permeability reservoir fracturing, reservoir driving, water-drive leading edges and the like in recent years, and is also an important supporting technology for shale gas development. According to the technology, a multistage three-component detector array is arranged in an adjacent well, a microseismic event generated in a target interval of a fractured well in a hydraulic fracturing process is monitored, and the microseismic event is inverted to obtain parameters such as a seismic source position, so that the geometrical shape and the spatial distribution of crack growth in the hydraulic fracturing process are described, the length, the height, the width and the direction of the crack generated by hydraulic fracturing are provided in real time, and the industrial development of shale gas is realized. The hydraulic fracturing microseismic detection is a hotspot and difficulty of scientific research in the field of current shale gas development. From the social and national demand perspective, the development of the research on the aspect of the microseismic monitoring system is very important, and the microseismic monitoring system has great social and economic values.

An important task in microseismic monitoring systems is the localization of microseismic events. The positioning accuracy is the most important factor affecting the application effect of the microseismic monitoring system, and the accuracy of positioning the microseismic event mainly depends on the related factors such as the accuracy of the fluctuation first-arrival (also called first-arrival) reading.

But the problem is that the first arrival pick-up is not as simple as it is imagined. The rock fracture form is very complex under the influence of the mining of ground instruments and geological structures, and then microseismic fluctuation with various forms and energy is generated, the form can be dozens or even hundreds, not only are the dominant frequency, the delay, the energy and the like different, but also the waveform form difference near the first arrival position is huge, and the non-uniformity of the waveform characteristics makes the first arrival picking very difficult. Further studies have also shown that the microseismic source mechanism also affects the first arrival point characteristics: most microseismic fluctuations generated by the shearing action of hard rock have large energy, higher main frequency, short time delay and the position of the maximum peak value closely follows the initial first arrival, and the first arrival point of the waves is clear, the jump-off time delay is short, and the waves are easy to pick up; however, most microseismic fluctuations generated by the stretching action have small energy, low main frequency, long delay time, slow take-off and uniform energy distribution, the amplitude of the waves at the first arrival point is small and is easily submerged by interference signals, the characteristic expressions of the first arrival point are inconsistent, and the first arrival pickup is not easy; the microseismic fluctuation generated by soft rock has concentrated energy distribution, fuzzy initial first arrival points, unobvious boundary lines, is obviously different from hard rock, and is difficult to pick up the first arrival. Meanwhile, according to foreign research, it is found that many algorithms want to certainly consider the first arrival wave as a P wave because the P wave velocity is greater than the S wave velocity, but the fact may be more complicated: the first arrivals may be P-waves, S-waves, and even outliers (outliers). According to the study, 41% of the first arrivals are S-waves, and 10% of the first arrivals are caused by outliers. These all present considerable difficulties for first arrival pick-ups.

In addition to the complexity of first arrival point features, first arrival picking faces another greater challenge: microseismic recordings are mass data. For example, approximately 1 million microseismic events were recorded in a test area of month 1 of 2005. Meanwhile, in order to meet production requirements, the microseismic monitoring system needs to continuously record 24 hours a day. Not only is a significant portion of this data a noise and interference caused by human or mechanical activity, independent of microseisms. The literature further classifies noise into three basic types: high frequency (>200Hz) noise, caused by various job related activities; low frequency noise (<10Hz), typically caused by machine activity far from the recording site, and commercial current (50 Hz). In addition, the microseismic signals themselves are not pure, for example, the professor of sinus name in China considers that the microseismic signals include various signals.

Therefore, how to identify microseismic events and pick up first arrivals from mass data is the basis of microseismic data processing. Compared with the prior art, the production method mostly adopts a manual method, wastes time and labor, has poor precision and reliability, cannot ensure the picking quality, and cannot process mass data. The automatic first arrival pickup is one of the solutions, and the automatic first arrival pickup of the micro-seismic fluctuation is one of the key technologies for processing the micro-seismic monitoring data and is also a technical difficulty for realizing the automatic positioning of the micro-seismic source.

In a common microseismic event detection method, the size of a judgment threshold is determined randomly, a uniform criterion is not provided, the general applicability of the method is greatly limited, and particularly when the signal-to-noise ratio is low, the performance of the algorithm is greatly influenced.

Disclosure of Invention

The invention aims to provide a microseismic event detection method based on the Khinchn-Einstein theorem, which utilizes the difference between microseismic signals and background noise in the aspect of power spectrum to detect microseismic events according to the Khinchn-Einstein theorem, eliminates the influence of the background noise on the event detection performance and improves the event detection precision. The method has the advantages of good robustness and simple calculation.

In order to achieve the purpose, the invention provides the following scheme:

a microseismic event detection method based on a Khichin-Einstein theorem comprises the following steps:

step 1, inputting an actually measured microseismic signal sequence S;

step 2, generating a microseismic signal difference original sequence, which specifically comprises the following steps:

ΔS^O＝[ΔS₁,ΔS₂,…,ΔS_N-1,ΔS_N]＝[S₂-S₁,S₃-S₂,…,S_N-S_N-1,S_N-S₁]；

wherein:

ΔS^O: the length of the signal difference original sequence is N;

ΔS_n: the signal difference original sequence Delta S^OWhere N is 1,2, …, N]；

S_n: the nth element in the microseismic signal sequence S;

S＝[S₁，S₂，…，S_N]the length of the microseismic signal sequence is N;

step 3, generating a differential secondary sequence of the microseismic signal, which specifically comprises the following steps:

ΔS^K＝[ΔS₁,ΔS₂,…,ΔS_K]；

wherein:

ΔS^K: the differential secondary sequence of microseismic signals at point K [ K ═ 1,2, …, N]；

Step 4, calculating a frequency domain window function h (f), specifically:

wherein:

Δf＝f_MAX-f_MIN: a frequency bandwidth;

f_MAX: the highest frequency of the signal sequence S;

f_MIN: the lowest frequency of the signal sequence S;

step 5, solving the logarithm window energy E at the K point_KThe method specifically comprises the following steps:

wherein:

P_K(f) the method comprises the following steps Sequence Δ S^O+ΔS^KThe power spectral density of (d);

step 6, detecting microseismic events according to the Khinchn-Einstein theorem; the method specifically comprises the following steps: if the logarithmized window energy E at the K point_K≤2.595log[1+f_MIN]Detecting a microseismic event at the Kth point of the signal sequence S; otherwise, no microseismic event is detected.

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

in a common microseismic event detection method, the size of a judgment threshold is determined randomly, a uniform criterion is not provided, the general applicability of the method is greatly limited, and particularly when the signal-to-noise ratio is low, the performance of the algorithm is greatly influenced.

The invention aims to provide a microseismic event detection method based on the Khinchn-Einstein theorem, which utilizes the difference between microseismic signals and background noise in the aspect of power spectrum to detect microseismic events according to the Khinchn-Einstein theorem, eliminates the influence of the background noise on the event detection performance and improves the event detection precision. The method has the advantages of good robustness and simple calculation.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

FIG. 1 is a schematic flow diagram of the process of the present invention;

Detailed Description

The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

FIG. 1 is a schematic flow chart of a microseismic event detection method based on Khichin-Einstein theorem

FIG. 1 is a schematic flow chart of a method for microseismic event detection based on the Khichin-Einstein theorem according to the present invention. As shown in fig. 1, the method for detecting microseismic events based on Khinchin-Einstein theorem specifically includes the following steps:

step 1, inputting an actually measured microseismic signal sequence S;

step 2, generating a microseismic signal difference original sequence, which specifically comprises the following steps:

ΔS^O＝[ΔS₁,ΔS₂,…,ΔS_N-1,ΔS_N]＝[S₂-S₁,S₃-S₂,…,S_N-S_N-1,S_N-S₁]；

wherein:

ΔS^O: the length of the signal difference original sequence is N;

ΔS_n: the signal difference original sequence Delta S^OWhere N is 1,2, …, N]；

S_n: the nth element in the microseismic signal sequence S;

S＝[S₁，S₂，…，S_N]the length of the microseismic signal sequence is N;

step 3, generating a differential secondary sequence of the microseismic signal, which specifically comprises the following steps:

ΔS^K＝[ΔS₁,ΔS₂,…,ΔS_K]；

wherein:

ΔS^K: the differential secondary sequence of microseismic signals at point K [ K ═ 1,2, …, N]；

Step 4, calculating a frequency domain window function h (f), specifically:

wherein:

Δf＝f_MAX-f_MIN: a frequency bandwidth;

f_MAX: the highest frequency of the signal sequence S;

f_MIN: the lowest frequency of the signal sequence S;

step 5, solving the logarithm window energy E at the K point_KThe method specifically comprises the following steps:

wherein:

P_K(f) the method comprises the following steps Sequence Δ S^O+ΔS^KThe power spectral density of (d);

step 6, detecting microseismic events according to the Khinchn-Einstein theorem; the method specifically comprises the following steps: if the logarithmized window energy E at the K point_K≤2.595log[1+f_MIN]Detecting a microseismic event at the Kth point of the signal sequence S; otherwise, no microseismic event is detected.

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.

The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (1)

1. A microseismic event detection method based on Khichin-Einstein theorem is characterized by comprising the following steps:

step 1, inputting an actually measured microseismic signal sequence S;

step 2, generating a microseismic signal difference original sequence, which specifically comprises the following steps:

ΔS^O＝[ΔS₁,ΔS₂,…,ΔS_N-1,ΔS_N]＝[S₂-S₁,S₃-S₂,…,S_N-S_N-1,S_N-S₁]；

wherein:

ΔS^O: the length of the signal difference original sequence is N;

ΔS_n: the signal difference original sequence Delta S^OWhere N is 1,2, …, N]；

S_n: the first in the microseismic signal sequence Sn elements;

S＝[S₁，S₂，…，S_N]the length of the microseismic signal sequence is N;

step 3, generating a differential secondary sequence of the microseismic signal, which specifically comprises the following steps:

ΔS^K＝[ΔS₁,ΔS₂,…,ΔS_K]；

wherein:

ΔS^K: the differential secondary sequence of microseismic signals at point K [ K ═ 1,2, …, N]；

Step 4, calculating a frequency domain window function h (f), specifically:

wherein:

Δf＝f_MAX-f_MIN: a frequency bandwidth;

f_MAX: the highest frequency of the signal sequence S;

f_MIN: the lowest frequency of the signal sequence S;

step 5, solving the logarithm window energy E at the K point_KThe method specifically comprises the following steps:

wherein:

P_K(f) the method comprises the following steps Sequence Δ S^O+ΔS^KThe power spectral density of (d);

step 6, detecting microseismic events according to the Khinchn-Einstein theorem; the method specifically comprises the following steps: if the logarithmized window energy E at the K point_K≤2.595log[1+f_MIN]Detecting a microseismic event at the Kth point of the signal sequence S; otherwise, no microseismic event is detected.

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