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1.
Microseismic monitoring in the oil and gas industry commonly uses migration‐based methods to locate very weak microseismic events. The objective of this study is to compare the most popular migration‐based methods on a synthetic dataset that simulates a strike‐slip source mechanism event with a low signal‐to‐noise ratio recorded by surface receivers (vertical components). The results show the significance of accounting for the known source mechanism in the event detection and location procedures. For detection and location without such a correction, the ability to detect weak events is reduced. We show both numerically and theoretically that neglecting the source mechanism by using only absolute values of the amplitudes reduces noise suppression during stacking and, consequently, limits the possibility to retrieve weak microseismic events. On the other hand, even a simple correction to the data polarization used with otherwise ineffective methods can significantly improve detections and locations. A simple stacking of the data with a polarization correction provided clear event detection and location, but even better results were obtained for those data combined with methods that are based on semblance and cross‐correlation.  相似文献   

2.
Comparison of surface and borehole locations of induced seismicity   总被引：1，自引：0，他引：1  
Monitoring of induced microseismic events has become an important tool in hydraulic fracture diagnostics and understanding fractured reservoirs in general. We compare microseismic event and their uncertainties using data sets obtained with surface and downhole arrays of receivers. We first model the uncertainties to understand the effect of different acquisition geometries on location accuracy. For a vertical array of receivers in a single monitoring borehole, we find that the largest part of the final location uncertainty is related to estimation of the backazimuth. This is followed by uncertainty in the vertical position and radial distance from the receivers. For surface monitoring, the largest uncertainty lies in the vertical position due to the use of only a single phase (usually P‐wave) in the estimation of the event location. In surface monitoring results, lateral positions are estimated robustly and are not sensitive to the velocity model. In this case study, we compare event location solutions from two catalogues of microseismic events; one from a downhole array and the second from a surface array of 1C geophone. Our results show that origin time can be reliably used to find matching events between the downhole and surface catalogues. The locations of the corresponding events display a systematic shift consistent with a poorly calibrated velocity model for downhole dataset. For this case study, locations derived from surface monitoring have less scatter in both vertical and horizontal directions.  相似文献   

3.
Like most other industrial activities that affect the subsurface, hydraulic fracturing carries the risk of reactivating pre‐existing faults and thereby causing induced seismicity. In some regions, regulators have responded to this risk by imposing traffic light scheme‐type regulations, where fracture stimulation programs must be amended or shut down if events larger than a given magnitude are induced. Some sites may be monitored with downhole arrays and/or dense near‐surface arrays, capable of detecting very small microseismic events. However, such monitoring arrangements will not be logistically or economically feasible at all sites. Instead, operators are using small, sparse arrays of surface seismometers to meet their monitoring obligations. The challenge we address in this paper is to maximise the detection thresholds of such small, sparse, surface arrays so that they are capable of robustly identifying small‐magnitude events whose signal‐to‐noise ratios may be close to 1. To do this, we develop a beamforming‐and‐stacking approach, computing running short‐term/long‐term average functions for each component of each recorded trace (P, SH, and SV), time‐shifting these functions by the expected travel times for a given location, and performing a stack. We assess the effectiveness of this approach with a case study using data from a small surface array that recorded a multi‐well, multi‐stage hydraulic fracture stimulation in Oklahoma over a period of 8 days. As a comparison, we initially used a conventional event‐detection algorithm to identify events, finding a total of 17 events. In contrast, the beamforming‐and‐stacking approach identified a total of 155 events during this period (including the 17 events detected by the conventional method). The events that were not detected by the conventional algorithm had low‐signal‐to‐noise ratios to the extent that, in some cases, they would be unlikely to be identified even by manual analysis of the seismograms. We conclude that this approach is capable of improving the detection thresholds of small, sparse arrays and thus can be used to maximise the information generated when deployed to monitor industrial sites.  相似文献   

4.
Distributed acoustic sensing is a growing technology that enables affordable downhole recording of strain wavefields from microseismic events with spatial sampling down to ∼1 m. Exploiting this high spatial information density motivates different detection approaches than typically used for downhole geophones. A new machine learning method using convolutional neural networks is described that operates on the full strain wavefield. The method is tested using data recorded in a horizontal observation well during hydraulic fracturing in the Eagle Ford Shale, Texas, and the results are compared to a surface geophone array that simultaneously recorded microseismic activity. The neural network was trained using synthetic microseismic events injected into real ambient noise, and it was applied to detect events in the remaining data. There were 535 detections found and no false positives. In general, the signal-to-noise ratio of events recorded by distributed acoustic sensing was lower than the surface array and 368 of 933 surface array events were found. Despite this, 167 new events were found in distributed acoustic sensing data that had no detected counterpart in the surface array. These differences can be attributed to the different detection threshold that depends on both magnitude and distance to the optical fibre. As distributed acoustic sensing data quality continues to improve, neural networks offer many advantages for automated, real-time microseismic event detection, including low computational cost, minimal data pre-processing, low false trigger rates and continuous performance improvement as more training data are acquired.  相似文献   

5.
Microseismic monitoring is an approach for mapping hydraulic fracturing. Detecting the accurate locations of microseismic events relies on an accurate velocity model. The one‐dimensional layered velocity model is generally obtained by model calibration from inverting perforation data. However, perforation shots may only illuminate the layers between the perforation shots and the recording receivers with limited raypath coverage in a downhole monitoring problem. Some of the microseismic events may occur outside of the depth range of these layers. To derive an accurate velocity model covering all of the microseismic events and locating events at the same time, we apply the cross double‐difference method for the simultaneous inversion of a velocity model and event locations using both perforation shots and microseismic data. The cross double‐difference method could provide accurate locations in both the relative and absolute sense, utilizing cross traveltime differences between P and S phases over different events. At the downhole monitoring scale, the number of cross traveltime differences is sufficiently large to constrain events locations and velocity model as well. In this study, we assume that the layer thickness is known, and velocities of P‐ and S‐wave are inverted. Different simultaneous inversion methods based on the Geiger's, double‐difference, and cross double‐difference algorithms have been compared with the same input data. Synthetic and field data experiments suggest that combining both perforation shots and microseismic data for the simultaneous cross double‐difference inversion of the velocity model and event locations is available for overcoming the trade‐offs in solutions and producing reliable results.  相似文献   

6.
Testing the ability of surface arrays to monitor microseismic activity   总被引：2，自引：0，他引：2  
Recently there has been much interest in the use of data from surface arrays in conjunction with migration‐based processing methods for passive seismic monitoring. In this study we use an example of this kind of data recorded whilst 18 perforation shots, with a variety of positions and propellant amounts, were detonated in the subsurface. As the perforation shots provide signals with known source positions and origin times, the analysis of these data is an invaluable opportunity to test the accuracy and ability of surface arrays to detect and locate seismic sources in the subsurface. In all but one case the signals from the perforation shots are not visible in the raw or preprocessed data. However, clear source images are produced for 12 of the perforation shots showing that arrays of surface sensors are capable of imaging microseismic events, even when the signals are not visible in individual traces. We find that point source locations are within typically 45 m (laterally) of the true shot location, however the depths are less well constrained (～150 m). We test the sensitivity of our imaging method to the signal‐to‐noise ratio in the data using signals embedded in realistic noise. We find that the position of the imaged shot location is quite insensitive to the level of added noise, the primary effect of increased noise being to defocus the source image. Given the migration approach, the array geometry and the nature of coherent noise during the experiment, signals embedded in noise with ratios ≥0.1 can be used to successfully image events. Furthermore, comparison of results from data and synthetic signals embedded in noise shows that, in this case, prestack corrections of traveltimes to account for near‐surface structure will not enhance event detectability. Although, the perforation shots have a largely isotropic radiation pattern the results presented here show the potential for the use of surface sensors in microseismic monitoring as a viable alternative to classical downhole methods.  相似文献   

7.
Noise suppression or signal‐to‐noise ratio enhancement is often desired for better processing results from a microseismic dataset. In this paper, a polarization–linearity and time–frequency‐thresholding‐based approach is used for denoising waveforms. A polarization–linearity filter is initially applied to preserve the signal intervals and suppress the noise amplitudes. This is followed by time–frequency thresholding for further signal‐to‐noise ratio enhancement in the S transform domain. The parameterisation for both polarization filter and time–frequency thresholding is also discussed. Finally, real microseismic data examples are shown to demonstrate the improvements in processing results when denoised waveforms are considered in the workflow. The results indicate that current denoising approach effectively suppresses the background noise and preserves the vector fidelity of signal waveform. Consequently, the quality of event detection, arrival‐time picking, and hypocenter location improves.  相似文献   

8.
In studies on heavy oil, shale reservoirs, tight gas and enhanced geothermal systems, the use of surface passive seismic data to monitor induced microseismicity due to the fluid flow in the subsurface is becoming more common. However, in most studies passive seismic records contain days and months of data and manually analysing the data can be expensive and inaccurate. Moreover, in the presence of noise, detecting the arrival of weak microseismic events becomes challenging. Hence, the use of an automated, accurate and computationally fast technique for event detection in passive seismic data is essential. The conventional automatic event identification algorithm computes a running‐window energy ratio of the short‐term average to the long‐term average of the passive seismic data for each trace. We show that for the common case of a low signal‐to‐noise ratio in surface passive records, the conventional method is not sufficiently effective at event identification. Here, we extend the conventional algorithm by introducing a technique that is based on the cross‐correlation of the energy ratios computed by the conventional method. With our technique we can measure the similarities amongst the computed energy ratios at different traces. Our approach is successful at improving the detectability of events with a low signal‐to‐noise ratio that are not detectable with the conventional algorithm. Also, our algorithm has the advantage to identify if an event is common to all stations (a regional event) or to a limited number of stations (a local event). We provide examples of applying our technique to synthetic data and a field surface passive data set recorded at a geothermal site.  相似文献   

9.
A challenge in microseismic monitoring is quantification of survey acquisition and processing errors, and how these errors jointly affect estimated locations. Quantifying acquisition and processing errors and uncertainty has multiple benefits, such as more accurate and precise estimation of locations, anisotropy, moment tensor inversion and, potentially, allowing for detection of 4D reservoir changes. Here, we quantify uncertainty due to acquisition, receiver orientation error, and hodogram analysis. Additionally, we illustrate the effects of signal to noise ratio variances upon event detection. We apply processing steps to a downhole microseismic dataset from Pouce Coupe, Alberta, Canada. We use a probabilistic location approach to identify the optimal bottom well location based upon known source locations. Probability density functions are utilized to quantify uncertainty and propagate it through processing, including in source location inversion to describe the three-dimensional event location likelihood. Event locations are calculated and an amplitude stacking approach is used to reduce the error associated with first break picking and the minimization with modelled travel times. Changes in the early processing steps have allowed for understanding of location uncertainty of the mapped microseismic events.  相似文献   

10.
李稳  刘伊克  刘保金 《地球物理学报》2016,59(10):3869-3882
井下微震监测获得的地震记录往往包含大量的噪声,记录信噪比很低.有效地震信号的识别与提取是进行后续地震定位等工作之前需要优先解决的问题.经过研究发现,井下水压裂微地震信号具有稀疏分布的特征,而井下环境噪声则具有更多的Gaussian分布特征.为此,本文提出将图像处理领域适宜于稀疏分布信号降噪处理的稀疏码收缩方法应用于井下微震监测数据处理.为解决需要利用与待处理数据中有效信号成分具有相似分布特征的无噪信号序列估算正交基以及计算效率等问题,将原方法与小波变换理论相结合.即通过优选小波基函数作为正交基进行小波变换将信号分解为不同级的小波系数,利用稀疏码收缩方法中对稀疏编码施加的非线性收缩方式作为阈值准则对小波系数进行改造.通过多方面的数值实验证明了该方法在处理地震子波及井下微地震信号方面准确可靠.含噪记录经过处理后有效地震信号的到时、波形、时频谱特征等均能得到良好的识别和恢复.并且该方法具有很强的抗噪能力,当信噪比低至-20~-30db时,仍然能够发挥作用.在处理大量实际井下微震监测数据的过程中,面对多种复杂情况,本方法展现出了计算效率高、计算结果可靠、应用简单等优势,证明了其本身具有实际应用价值,值得进一步的研究和推广.  相似文献   

11.
Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.  相似文献   

12.
微地震事件初至拾取是井下微地震监测数据处理的关键步骤之一.初至误差的存在会使微地震震源定位结果产生较大偏差,进而影响后续的压裂裂缝解释.通常初至拾取过程对所有的微地震事件选择相同的特征函数并采用一致的拾取参数进行统一处理,然而当事件的能量、震源机制、传播路径以及背景噪声等存在明显差异时,所得初至拾取结果差别显著.为了提高微地震事件初至拾取标准一致性,本文提出基于波形相似特征的初至拾取及全局校正方法.该方法首先利用互相关函数对每个事件内的各道记录进行时差校正,得到初始初至信息并形成叠加道,再对所有事件的叠加道进行全局互相关得到事件间初至相对校正量,最终初至结果可以通过各个事件的初始初至信息与其相对校正量相加得到.方法将所有微地震事件初至结果作为一个整体处理,从而能够克服常规方法初至拾取标准一致性差的缺陷.实际资料处理结果表明,相比于常规方法,该方法可以有效提高事件初至拾取和定位结果的一致性.  相似文献   

13.
—?Microseismic monitoring systems are generally installed in areas of induced seismicity caused by human activity. Induced seismicity results from changes in the state of stress which may occur as a result of excavation within the rock mass in mining (i.e., rockbursts), and changes in hydrostatic pressures and rock temperatures (e.g., during fluid injection or extraction) in oil exploitation, dam construction or fluid disposal. Microseismic monitoring systems determine event locations and important source parameters such as attenuation, seismic moment, source radius, static stress drop, peak particle velocity and seismic energy. An essential part of the operation of a microseismic monitoring system is the reliable detection of microseismic events. In the absence of reliable, automated picking techniques, operators rely upon manual picking. This is time-consuming, costly and, in the presence of background noise, very prone to error. The techniques described in this paper not only permit the reliable identification of events in cluttered signal environments they have also enabled the authors to develop reliable automated event picking procedures. This opens the way to use microseismic monitoring as a cost-effective production/operations procedure. It has been the experience of the authors that in certain noisy environments, the seismic monitoring system may trigger on and subsequently acquire substantial quantities of erroneous data, due to the high energy content of the ambient noise. Digital filtering techniques need to be applied on the microseismic data so that the ambient noise is removed and event detection simplified. The monitoring of seismic acoustic emissions is a continuous, real-time process and it is desirable to implement digital filters which can also be designed in the time domain and in real-time such as the Kalman Filter. This paper presents a real-time Kalman Filter which removes the statistically describable background noise from the recorded seismic traces.  相似文献   

14.
Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time‐delayed arrival from the event, we propose an autocorrelation‐based stacking method that designs a denoising filter from all the traces, as well as a multi‐channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace's autocorrelation is centred at zero in the lag domain. The effect of white noise is concentrated near zero lag; thus, the filter design requires a predictable adjustment of the zero‐lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with coloured noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.  相似文献   

15.
First-break picking of microseismic data is a significant step in microseismic monitoring. There is a great error in conventional first-break picking methods based on time domain analysis in low signal to noise ratio. S-transform may provide a novel approach, it can extract the time–frequency features of the signal and reduce the picking error because of its high time–frequency resolution and good time–frequency clustering; however, the S-transform is not well suited for microseismic data with high noise. For applications to array data where the weak signal has spatial coherency as well as some distinct temporal characteristics, we propose to combine the shearlet transform with a time–frequency transform. In the proposed method, the shearlet transform is used to capture spatial coherency features of the signal. The information of the signal and noise in shearlet domain is represented by shearlet coefficients. We use the correlation of signal coefficients at adjacent fine scales to give prominence to signal features to accurately discriminate the signal from noise. The prominent signal coefficients make the signal better gathered in time–frequency spectrum of the S-transform. Finally, we can get reliable and accurate first breaks based on the change of energy. The performance of the proposed method was tested on synthetic and field microseismic data. The experimental results indicated that our method is outstanding in terms of both picking precision and adaptability to noise.  相似文献   

16.
We present results of processed microseismic events induced by hydraulic fracturing and detected using dual downhole monitoring arrays. The results provide valuable insight into hydraulic fracturing. For our study, we detected and located microseismic events and determined their magnitudes, source mechanisms and inverted stress field orientation. Event locations formed a distinct linear trend above the stimulated intervals. Source mechanisms were only computed for high‐quality events detected on a sufficient number of receivers. All the detected source mechanisms were dip‐slip mechanisms with steep and nearly horizontal nodal planes. The source mechanisms represented shear events and the non‐double‐couple components were very small. Such small, non‐double‐couple components are consistent with a noise level in the data and velocity model uncertainties. Strikes of inverted mechanisms corresponding to the nearly vertical fault plane are (within the error of measurements) identical with the strike of the location trend. Ambient principal stress directions were inverted from the source mechanisms. The least principal stress, σ3, was determined perpendicular to the strike of the trend of the locations, indicating that the hydraulic fracture propagated in the direction of maximum horizontal stress. Our analysis indicated that the source mechanisms observed using downhole instruments are consistent with the source mechanisms observed in microseismic monitoring arrays in other locations. Furthermore, the orientation of the inverted principal components of the ambient stress field is in agreement with the orientation of the known regional stress, implying that microseismic events induced by hydraulic fracturing are controlled by the regional stress field.  相似文献   

17.
We examine the problem of localization of a single microseismic event and determination of its seismic moment tensor in the presence of strongly correlated noise. This is a typical problem occurring in monitoring of microseismic events from a daylight surface during producing or surface monitoring of hydraulic fracturing. We propose a solution to this problem based on the method of maximum likelihood. We discuss mathematical aspects of the problem, some features and weak points of the proposed approach, estimate the required computing resources, and present the results of numerical experiments. We show that the proposed approach is much more resistant to correlated noises than diffraction stacking methods and time reverse modeling.  相似文献   

18.
For years, severe rockburst problems at the Lucky Friday mine in northern Idaho have been a persistent safety hazard and an impediment to production. An MP250 based microseismic monitoring system, which uses simple voltage threshold picking of first arrivals, has been used in this mine since 1973 to provide source locations and energy estimates of seismic events. Recently, interest has been expressed in developing a whole waveform microseismic monitoring system for the mine to provide more accurate source locations and information about source characteristics. For this study, we have developed a prototype whole-waveform microseismic monitoring system based on a 80386 computer equipped with a 50 kHz analog-digital convertor board. The software developed includes a data collection program, a data analysis program, and an event detection program. Whole-waveform data collected and analyzed using this system during a three-day test have been employed to investigate sources of error in the hypocenter location process and to develop an automatic phase picker appropriate for microseismic events.Comparison of hypocenter estimates produced by the MP250 system to those produced by the whole-waveform system shows that significant timing errors are common in the MP250 system and that these errors caused a large part of the scatter evident in the daily activity plots produced at the mine. Simulations and analysis of blast data show that analytical control over the solutions is strongly influenced by the array geometry. Within the geophone array, large errors in the velocity model or moderate timing errors may result in small changes in the solution, but outside the array, the solution is very sensitive to small changes in the data.Our whole-waveform detection program picks event onset times and determines event durations by analysis of a segmented envelope function (SEF) derived from the microseismic signal. The detection program has been tested by comparing its arrival time picks to those generated by human analysis of the data set. The program picked 87% of the channels that were picked by hand with a standard error of 0.75 milliseconds. Source locations calculated using times provided by our entire waveform detection program were similar to those calculated using hand-picked arrival times. In particular, they show far less scatter than source locations calculated using arrival times based on simple voltage threshold picking of first arrivals.  相似文献   

19.
微震监测是直观评价压裂过程和压裂效果的有效手段.微震事件识别是微震监测的首要步骤.然而对于低信噪比微震监测数据,常规识别方法很难取得满意效果.基于微震事件在时频域中的稀疏性,本文提出利用Renyi熵值表示微震监测数据的时频稀疏程度,并以时频距离为约束条件,建立以低熵值的道数为判别阈值的目标函数.本文方法能在识别出微震事件的同时,恢复出较为清晰的微震事件.通过数值计算和对实际监测数据的测试,表明该方法对低信噪比的微震监测数据有较好的处理效果.  相似文献   

20.
李月  邵丹  张超  马海涛 《地球物理学报》2018,61(12):4997-5006
地面微地震监测采集到的微地震信号通常能量微弱,信噪比低,如何提高微震数据的信噪比是数据处理的难题.Shearlet变换是一种新型的多尺度几何分析方法,具有敏感的方向性和较强的稀疏表示特性,能起到很好的随机噪声压制效果.由于地面微震数据的有效信号大多被淹没在噪声中,基于传统阈值的Shearlet变换（the traditional threshold-based Shearlet transform TST）只考虑到尺度或方向的阈值,在去噪过程中会过度扼制有效信号系数,造成有效信号能量损失.因而,本文建立Context模型,得到基于Context模型的Shearlet变换（the Context-model-based Shearlet transform CMST）方法,改进传统Shearlet阈值方法的不足.我们通过所建立的Context模型将能量相近的各方向系数划分为同一组,并分组估计阈值,分别处理各部分系数,达到微弱同相轴有效恢复的目的.通过TST及CMST的模拟实验与实际地面微震记录处理结果对比可知,本文方法在低信噪比条件下比对比方法更加有效地恢复地面微震数据的微弱信号,随机噪声压制效果明显,在-10 dB条件下,提升信噪比18.3741 dB.  相似文献   

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