Comparison of hypocenter locations from the reprocessing of a downhole microseismic data set

We have evaluated arrival-time picking algorithms for downhole microseismic data. The picking algorithms that we considered may be classified as window-based single-level methods (e.g., energy-ratio [ER] methods), nonwindow-based single-level methods (e.g., Akaike information criterion), multilevel- or array-based methods (e.g., crosscorrelation approaches), and hybrid methods that combine a number of single-level methods (e.g., Akazawa’s method). We have determined the key parameters for each algorithm and developed recommendations for optimal parameter selection based on our analysis and experience. We evaluated the performance of these algorithms with the use of field examples from a downhole microseismic data set recorded in western Canada as well as with pseudo-synthetic microseismic data generated by adding 100 realizations of Gaussian noise to high signal-to-noise ratio microseismic waveforms. ER-based algorithms were found to be more efficient in terms of computational speed and were therefore recommended for real-time microseismic data processing. Based on the performance on pseudo-synthetic and field data sets, we found statistical, hybrid, and multilevel crosscorrelation methods to be more efficient in terms of accuracy and precision. Pick errors for S-waves are reduced significantly when data are preconditioned by applying a transformation into ray-centered coordinates.

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An effective noise-suppression technique for surface microseismic data

Geophysics ◽

10.1190/geo2012-0502.1 ◽

2013 ◽

Vol 78 (6) ◽

pp. KS85-KS95 ◽

Cited By ~ 32

Author(s):

Farnoush Forghani-Arani ◽

Mark Willis ◽

Seth S. Haines ◽

Mike Batzle ◽

Jyoti Behura ◽

...

Keyword(s):

Surface Wave ◽

Hydraulic Fracturing ◽

Noise Suppression ◽

Signal To Noise ◽

Data Set ◽

Microseismic Data ◽

Receiver Array ◽

Fracturing Process ◽

Suppression Technique ◽

Microseismic Events

The presence of strong surface-wave noise in surface microseismic data may decrease the utility of these data. We implement a technique, based on the distinct characteristics that microseismic signal and noise show in the [Formula: see text] domain, to suppress surface-wave noise in microseismic data. Because most microseismic source mechanisms are deviatoric, preprocessing is necessary to correct for the nonuniform radiation pattern prior to transforming the data to the [Formula: see text] domain. We employ a scanning approach, similar to semblance analysis, to test all possible double-couple orientations to determine an estimated orientation that best accounts for the polarity pattern of any microseismic events. We then correct the polarity of the data traces according to this pattern, prior to conducting signal-noise separation in the [Formula: see text] domain. We apply our noise-suppression technique to two surface passive-seismic data sets from different acquisition surveys. The first data set includes a synthetic microseismic event added to field passive noise recorded by an areal receiver array distributed over a Barnett Formation reservoir undergoing hydraulic fracturing. The second data set is field microseismic data recorded by receivers arranged in a star-shaped array, over a Bakken Shale reservoir during a hydraulic-fracturing process. Our technique significantly improves the signal-to-noise ratios of the microseismic events and preserves the waveforms at the individual traces. We illustrate that the enhancement in signal-to-noise ratio also results in improved imaging of the microseismic hypocenter.

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Sequence stratigraphic principles applied to the analysis of borehole microseismic data

Interpretation ◽

10.1190/int-2013-0151.1 ◽

2014 ◽

Vol 2 (3) ◽

pp. SG15-SG23

Author(s):

Carlos Cabarcas ◽

Roger Slatt

Keyword(s):

Hydraulic Fracturing ◽

Gamma Ray ◽

Sensor Arrays ◽

Data Set ◽

Field Development ◽

Sequence Stratigraphic ◽

Microseismic Data ◽

Stratigraphic Framework ◽

Triple Array ◽

Stacking Patterns

Based on a sequence stratigraphic framework developed using gamma ray stacking patterns, we have identified brittle-ductile couplets, which allow us to better interpret the microseismic response recorded during a single-stage hydraulic fracture stimulation treatment monitored from three strategically located observation wells. We have analyzed and compared hydraulic fracturing results inferred by individual processing of microseismic data acquired from horizontal and vertical sensor arrays, as well as the results from simultaneously processing the signals recorded by all three sensors. Ultimately, we have decided in favor of the triple array simultaneous solution as the most useful data set to interpret the stimulation treatment due to the location of the microseismic events coupled with the theoretical expectation from our sequence stratigraphic framework. The final data set has not only allowed us to better interpret the hydraulic fracturing results, but also helped us improve recommendations in support of the field development campaign.

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Azimuthal rotation-dependent spatial distributional patterns of small repeaters estimated from microseismic data-set of Gokova Gulf (SW-Turkey)

10.3997/2214-4609.201902655 ◽

2019 ◽

Author(s):

M. Toker

Keyword(s):

Data Set ◽

Microseismic Data ◽

Sw Turkey ◽

Distributional Patterns

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A method for low-frequency noise suppression based on mathematical morphology in microseismic monitoring

Geophysics ◽

10.1190/geo2015-0222.1 ◽

2016 ◽

Vol 81 (3) ◽

pp. V159-V167 ◽

Cited By ~ 82

Author(s):

Huijian Li ◽

Runqiu Wang ◽

Siyuan Cao ◽

Yangkang Chen ◽

Weilin Huang

Keyword(s):

Mathematical Morphology ◽

Large Scale ◽

Noise Suppression ◽

Low Frequency ◽

Original Data ◽

Small Scale ◽

Frequency Noise ◽

Low Frequency Noise ◽

Data Set ◽

Microseismic Data

The frequency of microseismic data is higher than that of conventional seismic data. The range of effective frequency is usually from 100 to 500 Hz, and low-frequency noise is a common disturbance in downhole monitoring. Conventional signal analysis techniques, such as band-pass filters, have their limitation in microseismic data processing when the useful signals and noise share the same frequency band. We have developed a novel method to suppress low-frequency noise in microseismic data based on mathematical morphology theory that aims at distinguishing useful signals and noise according to their tiny differences of waveform. By choosing suitable structure elements, we have extracted low-frequency noise from a original data set. We first developed the fundamental principle of mathematical morphology and the formulation of our approach. Then, we used a synthetic data example that was composed of a Ricker wavelet and low-frequency noise to test the feasibility and performance of the proposed approach. Our results from the synthetic example indicate that the proposed approach can effectively suppress large-scale low-frequency noise while slightly decreasing the small-scale signals. Finally, we have applied the proposed approach to field microseismic data and obtained very encouraging results.

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Azimuthal anisotropy in microseismic monitoring: A Bakken case study

Geophysics ◽

10.1190/geo2013-0211.1 ◽

2014 ◽

Vol 79 (1) ◽

pp. KS1-KS12 ◽

Cited By ~ 66

Author(s):

Vladimir Grechka ◽

Sergey Yaskevich

Keyword(s):

Oil And Gas ◽

Joint Inversion ◽

Velocity Model ◽

Microseismic Monitoring ◽

Azimuthal Anisotropy ◽

Gas Reservoirs ◽

Data Set ◽

Microseismic Data ◽

Unconventional Oil And Gas

Hydraulic fracturing, routinely applied for enhancing the permeability of unconventional oil and gas reservoirs, is one of the possible causes for azimuthal anisotropy of the treated formations. Accounting for both naturally occurring and completion induced azimuthal anisotropy leads to marked improvements in the results of microseismic data processing. As illustrated on a data set acquired in the Bakken Field, North Dakota, USA, those improvements include the possibility of modeling the observed shear-wave splitting, reduction of misfit between the picked and modeled traveltimes of microseismic events, and relocation and tightening of the spatial distribution of the event hypocenters. In addition and perhaps most importantly for the development of microseismic technology, the feasibility of joint inversion of field microseismic data for the event locations and azimuthally anisotropic velocity model containing triclinic layers is demonstrated.

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Convolutional neural networks for microseismic waveform classification and arrival picking

Geophysics ◽

10.1190/geo2019-0267.1 ◽

2020 ◽

Vol 85 (4) ◽

pp. WA227-WA240 ◽

Cited By ~ 3

Author(s):

Guoyin Zhang ◽

Chengyan Lin ◽

Yangkang Chen

Keyword(s):

Neural Network ◽

Network Architecture ◽

Signal To Noise Ratio ◽

Data Sets ◽

Data Set ◽

Frequency Spectra ◽

Time Frequency ◽

Microseismic Data ◽

Frequency Components ◽

Specific Frequency

Microseismic data have a low signal-to-noise ratio (S/N). Existing waveform classification and arrival-picking methods are not effective enough for noisy microseismic data with low S/N. We have adopted a novel antinoise classifier for waveform classification and arrival picking by combining the continuous wavelet transform (CWT) and the convolutional neural network (CNN). The proposed CWT-CNN classifier is applied to synthetic and field microseismic data sets. Results show that CWT-CNN classifier has much better performance than the basic deep feedforward neural network (DNN), especially for microseismic data with low S/N. The CWT-CNN classifier has a shallow network architecture and small learning data set, and it can be trained quickly for different data sets. We have determined why CWT-CNN has better performance for noisy microseismic data. CWT can decompose the microseismic data into time-frequency spectra, where effective signals and interfering noise are easier to distinguish. With the help of CWT, CNN can focus on the specific frequency components to extract useful features and build a more effective classifier.

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Rotational Characteristics of the Green Solar Corona: 1947-1991

International Astronomical Union Colloquium ◽

10.1017/s0252921100025197 ◽

1994 ◽

Vol 144 ◽

pp. 139-141 ◽

Cited By ~ 2

Author(s):

J. Rybák ◽

V. Rušin ◽

M. Rybanský

Keyword(s):

Solar Corona ◽

World Wide ◽

Rotation Period ◽

Original Data ◽

Coronal Emission ◽

Time Intervals ◽

Data Set ◽

The World ◽

Coronal Emission Line ◽

Homogeneous Data

AbstractFe XIV 530.3 nm coronal emission line observations have been used for the estimation of the green solar corona rotation. A homogeneous data set, created from measurements of the world-wide coronagraphic network, has been examined with a help of correlation analysis to reveal the averaged synodic rotation period as a function of latitude and time over the epoch from 1947 to 1991.The values of the synodic rotation period obtained for this epoch for the whole range of latitudes and a latitude band ±30° are 27.52±0.12 days and 26.95±0.21 days, resp. A differential rotation of green solar corona, with local period maxima around ±60° and minimum of the rotation period at the equator, was confirmed. No clear cyclic variation of the rotation has been found for examinated epoch but some monotonic trends for some time intervals are presented.A detailed investigation of the original data and their correlation functions has shown that an existence of sufficiently reliable tracers is not evident for the whole set of examinated data. This should be taken into account in future more precise estimations of the green corona rotation period.

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Difference Fourier Analysis of Glucose Embedded and Frozen Hydrated Purple Membrane

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100053164 ◽

1982 ◽

Vol 40 ◽

pp. 74-75

Author(s):

Jules S. Jaffe ◽

Robert M. Glaeser

Keyword(s):

Purple Membrane ◽

Data Set ◽

High Resolution Data ◽

X Ray ◽

X Ray Crystallography ◽

Fourier Techniques ◽

Versus Protein ◽

The Difference ◽

Difference Fourier ◽

Ideal Method

Although difference Fourier techniques are standard in X-ray crystallography it has only been very recently that electron crystallographers have been able to take advantage of this method. We have combined a high resolution data set for frozen glucose embedded Purple Membrane (PM) with a data set collected from PM prepared in the frozen hydrated state in order to visualize any differences in structure due to the different methods of preparation. The increased contrast between protein-ice versus protein-glucose may prove to be an advantage of the frozen hydrated technique for visualizing those parts of bacteriorhodopsin that are embedded in glucose. In addition, surface groups of the protein may be disordered in glucose and ordered in the frozen state. The sensitivity of the difference Fourier technique to small changes in structure provides an ideal method for testing this hypothesis.

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