Improving microseismic event location accuracy with head wave arrival time: Case study using Marcellus shale

Zhang, Zhishuai; Rector, James W.; Nava, Michael J.

doi:10.1190/segam2015-5919420.1

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Microseismic locations have been estimated by a contractor shown in Figure 1 as well. The detailed information about this project has been given by Zhang et al (2015).…”

Section: Microseismic Survey In Marcellus Shalementioning

confidence: 99%

“…Various factors, including recording-system factors and background noise, make the characterization of microseismic data a complex and challenging task. Among these factors, resonance due to the poor coupling of borehole geophones contributes significantly to waveform distortion (Gaiser et al, 1988;Maxwell, 2014;Nava et al, 2015;Zhang et al, 2015), but is often not treated properly. Gaiser et al (1988) studied the seismic response of a threecomponent geophone in a vertical borehole.…”

Section: Introductionmentioning

confidence: 99%

“…In downhole microseismic monitoring, this resonance is relatively common. The situa-tion is even worse for geophones deployed in horizontal wells, where the only coupling force between geophones and well-bore is usually the gravitational force of the geophones (Nava et al, 2015;Zhang et al, 2015). In these situations, we usu-ally cannot avoid the geophone resonance other than the axial component data.…”

Section: Introductionmentioning

confidence: 99%

“…This polarization direction uncertainty can be a major source of microseismic event location uncertainty in downhole moni-toring. Multiple arrivals can be used to improve location accu-racy in downhole microseismic survey (Pei et al, 2014;Zhang et al, 2015;Zimmer, 2011), however, the identification of some phases can be hindered by the resonance from their preced-ing phases. In addition, source parameters are useful for mi-croseismic characterization (Cipolla et al, 2012;Du et al, 2011;Eisner et al, 2007;Rutledge and Phillips, 2003;Urban-cic et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Resonance in downhole microseismic data and its removal

Zhang

Nava

Rector

2016

SEG Technical Program Expanded Abstracts 2016

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We identified resonance due to poor geophone-borehole coupling in downhole microseismic data and proposed to use spik-ing deconvolution and relative spectrum analysis to remove its effect. The resonance may hinder the arrival time picking and contaminate microseismic waveform spectrum. We designed a spiking deconvolution filter to recover the impulse response of the earth. Also, we proposed to use relative spectrum analysis to study microseismic source parameters. The application of deconvolution on Marcellus shale dataset improved the identifiability of the multiple arrivals. Additionally, the relative spectrum analysis is more effective in revealing the actual spectrum characteristics of microseismic waveforms.

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“…Microseismic locations have been estimated by a contractor shown in Figure 1 as well. The detailed information about this project has been given by Zhang et al (2015).…”

Section: Microseismic Survey In Marcellus Shalementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Resonance in downhole microseismic data and its removal

Zhang

Nava

Rector

2016

SEG Technical Program Expanded Abstracts 2016

View full text Add to dashboard Cite

show abstract

“…To assist in overcoming the aperture limitations imposed by the acquisition geometry, 3C geophones are deployed, which makes polarization analysis feasible Li, 2016, 2017). Moreover, multiple phase identification and full-waveform inversion of the microseismic signal are also possible in some environments (Song and Toksöz, 2011;Belayouni et al, 2015;Zhang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Microseismic hydraulic fracture imaging in the Marcellus Shale using head waves

2018

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We have studied microseismic data acquired from a geophone array deployed in the horizontal section of a well drilled in the Marcellus Shale near Susquehanna County, Pennsylvania. Head waves were used to improve event location accuracy as a substitution for the traditional P-wave polarization method. We identified that resonances due to poor geophone-toborehole coupling hinder arrival-time picking and contaminate the microseismic data spectrum. The traditional method had substantially greater uncertainty in our data due to the large uncertainty in P-wave polarization direction estimation. We also identified the existence of prominent head waves in some of the data. These head waves are refractions from the interface between the Marcellus Shale and the underlying Onondaga Formation. The source location accuracy of the microseismic events can be significantly improved by using the P-, S-wave direct arrival times and the head wave arrival times. Based on the improvement, we have developed a new acquisition geometry and strategy that uses head waves to improve event location accuracy and reduce acquisition cost in situations such as the one encountered in our study.

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Simultaneous inversion of multiple microseismic data for event locations and velocity model with Bayesian inference

2017

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We have applied Bayesian inference for simultaneous inversion of multiple microseismic data to obtain event locations along with the subsurface velocity model. The traditional method of using a predetermined velocity model for event location may be subject to large uncertainties, particularly if the prior velocity model is poor. Our study indicated that microseismic data can help to construct the velocity model, which is usually a major source of uncertainty in microseismic event locations. The simultaneous inversion eliminates the requirement for an accurate predetermined velocity model in microseismic event location estimation. We estimate the posterior probability density of the velocity model and microseismic event locations with the maximum a posteriori estimation, and the posterior covariance approximation under the Gaussian assumption. This provides an efficient and effective way to quantify the uncertainty of the microseismic location estimation and capture the correlation between the velocity model and microseismic event locations. We have developed successful applications on both synthetic examples and real data from the Newberry enhanced geothermal system. Comparisons with location results based on a traditional predetermined velocity model method demonstrated that we can construct a reliable effective velocity model using only microseismic data and determine microseismic event locations without prior knowledge of the velocity model.

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Improving microseismic event location accuracy with head wave arrival time: Case study using Marcellus shale

Cited by 6 publications

References 23 publications

Resonance in downhole microseismic data and its removal

Resonance in downhole microseismic data and its removal

Microseismic hydraulic fracture imaging in the Marcellus Shale using head waves

Simultaneous inversion of multiple microseismic data for event locations and velocity model with Bayesian inference

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