Towards a Fully Automated Shear-wave Splitting Analysis of Microseismic Data

Al-Harrasi, O.; Wuestefeld, A.; Kendall, J.‐M.

doi:10.3997/2214-4609.201400498

Cited by 2 publications

(4 citation statements)

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“…We use an automated shear‐wave splitting approach to estimate the fast shear‐wave polarization direction (Φ) and delay time (δ t ). The approach is described in more detail in Al‐Harrasi, Wüstefeld and Kendall (2009) and Wüstefeld et al (2010). The choice of the S‐wave splitting window is very subjective as different S‐wave windows can give different splitting parameters.…”

Section: Data Set and Methodologymentioning

confidence: 99%

“…We further examine the success of the automation by visually checking the diagnostic plots from the splitting analysis. The automated quality control helps reduce the manual plot inspection to only measurements of the highest quality (Al‐Harrasi et al 2009). Splitting results are accepted if they satisfy the following requirements: 1) the S‐wave arrival must be clear and distinct from the P‐wave; 2) energy is minimized on the component transverse to the incoming S‐wave polarization direction; 3) the elliptical S‐wave particle motion must be linear after the splitting correction; 4) there must be a good match between the derived fast and slow shear‐waveforms; 5) there must be a unique 95% confidence interval in the F‐test plot without any cycle skipping.…”

Section: Data Set and Methodologymentioning

confidence: 99%

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Seismic anisotropy in a hydrocarbon field estimated from microseismic data

Al-Harrasi

Al-Anboori

Wüstefeld

et al. 2010

Geophysical Prospecting

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The study of seismic anisotropy in exploration seismology is gaining interest as it provides valuable information about reservoir properties and stress directions. In this study we estimate anisotropy in a petroleum field in Oman using observations of shear‐wave splitting from microseismic data. The data set was recorded by arrays of borehole geophones deployed in five wells. We analyse nearly 3400 microearthquakes, yielding around 8500 shear‐wave splitting measurements. Stringent quality control reduces the number of reliable measurements to 325. Shear‐wave splitting modelling in a range of rock models is then used to guide the interpretation. The difference between the fast and slow shear‐wave velocities along the raypath in the field ranges between 0–10% and it is controlled both by lithology and proximity to the NE‐SW trending graben fault system that cuts the field formations. The anisotropy is interpreted in terms of aligned fractures or cracks superimposed on an intrinsic vertical transversely isotropic (VTI) rock fabric. The highest magnitudes of anisotropy are within the highly fractured uppermost unit of the Natih carbonate reservoir. Anisotropy decreases with depth, with the lowest magnitudes found in the deep part of the Natih carbonate formation. Moderate amounts of anisotropy are found in the shale cap rock. Anisotropy also varies laterally with the highest anisotropy occurring either side of the south‐eastern graben fault. The predominant fracture strikes, inferred from the fast shear‐wave polarizations, are consistent with the trends of the main faults (NE‐SW and NW‐SE). The majority of observations indicate subvertical fracture dip (>70°). Cumulatively, these observations show how studies of shear‐wave splitting using microseismic data can be used to characterize fractures, important information for the exploitation of many reservoirs.

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Section: Data Set and Methodologymentioning

confidence: 99%

Section: Data Set and Methodologymentioning

confidence: 99%

Seismic anisotropy in a hydrocarbon field estimated from microseismic data

Al-Harrasi

Al-Anboori

Wüstefeld

et al. 2010

Geophysical Prospecting

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“…As a first step we recommend filtering out any electrical noise from the raw, unrotated seismograms (see ). The characteristic relationship between source‐receiver distance and S‐wave amplitude decay for the reservoir in question should then be determined (Al‐Harrasi et al 2009). This serves as a basis for the minimum length of the splitting window.…”

Section: Suggested Workflow For Fully Automated Shear‐wave Splittingmentioning

confidence: 99%

“…Recently, Crampin and Peacock (2008) compiled a review of microseismic shear‐wave splitting on a crustal scale. Teanby, Kendall and van der Baan (2004a) used shear‐wave splitting to image fracture networks in the North Sea Valhall Field, whilst Al‐Harrasi, Wuestefeld and Kendall (2009) and Al‐Anboori et al (2005) were able to characterize the different fracture networks pervading the cap‐rock and reservoir of an oilfield in Oman. Holmes, Crampin and Young (2000) used a controlled source shear‐wave experiment to image fractures in highly stressed granite and Elkibbi and Rial (2005) used splitting measurements recorded on surface seismometers to characterize the fracturing at The Geysers geothermal field in California.…”

Section: Introductionmentioning

confidence: 99%

A strategy for automated analysis of passive microseismic data to image seismic anisotropy and fracture characteristics

Wuestefeld

Al-Harrasi

Verdon

et al. 2010

Geophysical Prospecting

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Monitoring of induced seismicity is gaining importance in a broad range of industrial operations from hydrocarbon reservoirs to mining to geothermal fields. Such passive seismic monitoring mainly aims at identifying fractures, which is of special interest for safety and productivity reasons. By analysing shear‐wave splitting it is possible to determine the anisotropy of the rock, which may be caused by sedimentary layering and/or aligned fractures, which in turn offers insight into the state of stress in the reservoir. We present a workflow strategy for automatic and effective processing of passive microseismic data sets, which are ever increasing in size. The automation provides an objective quality control of the shear‐wave splitting measurements and is based on characteristic differences between the two independent eigenvalue and cross‐correlation splitting techniques. These differences are summarized in a quality index for each measurement, allowing identification of an appropriate quality threshold. Measurements above this threshold are considered to be of good quality and are used in further interpretation. We suggest an automated inversion scheme using rock physics theory to test for best correlation of the data with various combinations of fracture density, its strike and the background anisotropy. This fully automatic workflow is then tested on a synthetic and a real microseismic data set.

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Towards a Fully Automated Shear-wave Splitting Analysis of Microseismic Data

Cited by 2 publications

References 0 publications

Seismic anisotropy in a hydrocarbon field estimated from microseismic data

Seismic anisotropy in a hydrocarbon field estimated from microseismic data

A strategy for automated analysis of passive microseismic data to image seismic anisotropy and fracture characteristics

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