Determination of moment tensor and location of microseismic events under conditions of highly correlated noise based on the maximum likelihood method

Биряльцев, Е. В.; Demidov, Denis

doi:10.1111/1365-2478.12485

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…5 outlines the main processes of the workflow that leads to the location of a microseismic event. More broadly speaking, the relatively integrated procedures consist in identification of effective events on the microseismic records, selecting the arrival times of the P and S waves, deriving an approximate velocity model and source location that particularly is considered to be of paramount importance and a challenge in monitoring (Birialtsev et al, 2017;Soledad and Danilo, 2018). In other words, to better delineate fracture distribution, reliable locational algorithmic studies are extremely urgent.…”

Section: Microseismic Monitoring Techniquementioning

confidence: 99%

“…These events launch waves into the surrounding medium and can be recorded by geophones installed in a borehole or near the surface (Zhang et al, 2013;Zhang et al, 2016;Meng et al, 2018). Associated with hydraulic fracturing processes, microseismic monitoring helps in understanding the geometric shapes of the induced fractures as well as resource characterization and estimation of the process effect of fracturing technology (Birialtsev et al, 2017;Woo and Kang, 2017;Soledad and Danilo, 2018;Alfataierge et al, 2019). In addition, the downhole microseismic monitoring we focus on in this work more approaches reservoirs, gaining wider recognition because of its advantages of high signal-tonoise values (S/N ratio), well developed P and S waves, and so on (Maxwell et al, 2010;Jones et al, 2014;Yuan and Li, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Downhole Microseismic Source Location Based on a Multi‐Dimensional DIRECT Algorithm for Unconventional Oil and Gas Reservoir Exploration

Yin

Tao

Zheng

et al. 2019

Acta Geologica Sinica (Eng)

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Downhole microseismic data has the significant advantages of high signal-to-noise ratio and well-developed P and S waves and the core component of microseismic monitoring is microseismic event location associated with hydraulic fracturing in a relatively high confidence level and accuracy. In this study, we present a multidimensional DIRECT inversion method for microseismic locations and applicability tests over modeling data based on a downhole microseismic monitoring system. Synthetic tests inidcate that the objective function of locations can be defined as a multi-dimensional matrix space by employing the global optimization DIRECT algorithm, because it can be run without the initial value and objective function derivation, and the discretely scattered objective points lead to an expeditious contraction of objective functions in each dimension. This study shows that the DIRECT algorithm can be extensively applied in real downhole microseismic monitoring data from hydraulic fracturing completions. Therefore, the methodology, based on a multidimensional DIRECT algorithm, can provide significant high accuracy and convergent efficiency as well as robust computation for interpretable spatiotemporal microseismic evolution, which is more suitable for real-time processing of a large amount of downhole microseismic monitoring data.

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Section: Microseismic Monitoring Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Downhole Microseismic Source Location Based on a Multi‐Dimensional DIRECT Algorithm for Unconventional Oil and Gas Reservoir Exploration

Yin

Tao

Zheng

et al. 2019

Acta Geologica Sinica (Eng)

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“…For the moment tensor inversion, the contamination of noise can hardly be avoided [19], and the inversion results are inaccurate. To improve inversion accuracy, a channel selection approach [20][21][22] can be implemented, where the waveforms with insufficient signal-tonoise ratios (SNRs) are discarded to directly reduce the effect of noise on inversion accuracy. The channel selection approach is efficient, but a large number of sensors are needed for data recording.…”

Section: Introductionmentioning

confidence: 99%

An Inversion Method Based on Inherent Similarity between Signals for Retrieving Source Mechanisms of Cracks

et al. 2022

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The knowledge of cracking mechanisms is significant for evaluating the healthy condition of aircraft structures and can be retrieved by moment tensor inversion based on the acoustic emission (AE) phenomenon. For engineering applications, the inversion method cannot compute accurate results because the waveforms recorded by sensors are commonly contaminated by noise. Consequently, the correlation calculation of de-noising is introduced into the inversion and sufficient correlation functions are needed. In this paper, the correlation function of raw waveforms is proposed and based on the inherent similarity between the signals induced by one source and recorded by different sensors. According to the synthetic tests, the error of the inversion method based on the new correlation function is approximately 1/10 of that of the commonly used amplitude method. Although the inversion accuracy is influenced by the phase differences and the ratio of noise frequency to signal frequency, the influence is limited and the new correlation function is suitable for most practical cases. The inversion method based on the new correlation function does not require the knowledge of noise spectra or any complex calculation processes and provides a new way to improve the inversion accuracy of cracking mechanisms with little additional computation consumption.

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“…() also showed that statistically optimal algorithms (maximum likelihood method) perform better than SET in the presence of man‐made surface noise during hydraulic fracturing. Birialtsev, Demidov and Mokshin () proposed a similar approach to reconstruct the seismic moment tensor. Birialtsev et al .…”

Section: Introductionmentioning

confidence: 99%

Research Note: Frequency domain orthogonal projection filtration of surface microseismic monitoring data

Azarov

Serdyukov

Gapeev³

2019

Geophysical Prospecting

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We address the problem of increasing the signal‐to‐noise ratio during surface microseismic monitoring data processing. Interference from different seismic waves causes misleading results of microseismic event locations. Ground‐roll suppression is particularly necessary. The standard noise suppression techniques assume regular and dense acquisition geometries. Many pre‐processing noise suppression algorithms are designed for special types of noise or interference. To overcome these problems, we propose a novel general‐purpose filtration method. The goal of this method is to amplify only the seismic waves that are excited in the selected target area and suppress all other signals. We construct a linear projector onto a frequency domain data subspace, which corresponds to the seismic emission of the target area. The novel filtration method can be considered an extension of the standard frequency–wavenumber flat wave filtration method for non‐flat waves and arbitrary irregular receiver‐position geometries. To reduce the effect of the uncertainty of the velocity model, we suggest using additional active shot data (typically the perforation shots), which provide static travel time corrections for the target area. The promising prospects of the proposed method are confirmed by synthetic and semi‐synthetic data processing.

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Determination of moment tensor and location of microseismic events under conditions of highly correlated noise based on the maximum likelihood method

Cited by 6 publications

References 34 publications

Downhole Microseismic Source Location Based on a Multi‐Dimensional DIRECT Algorithm for Unconventional Oil and Gas Reservoir Exploration

Downhole Microseismic Source Location Based on a Multi‐Dimensional DIRECT Algorithm for Unconventional Oil and Gas Reservoir Exploration

An Inversion Method Based on Inherent Similarity between Signals for Retrieving Source Mechanisms of Cracks

Research Note: Frequency domain orthogonal projection filtration of surface microseismic monitoring data

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