Azimuthal anisotropy using shear dipole sonic: insights from the AIG 10 well, Corinth Rift Laboratory

Prioul, Romain; Plona, Thomas J.; Kane, M.; Sinha, Bikash K.; Kaufman, Peter; Signer, C.

doi:10.1016/j.crte.2003.11.008

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…For example, the analysis of the Stoneley-mode reflections and attenuation allows the identification of open fractures in the borehole and an estimation of their apertures ͑Hornby et al ., 1989;Tezuka et al, 1997͒. In addition, the interpretation of borehole images ͑electrical and ultrasonic͒ can be used to identify either natural or stress-induced fractures ͑Luthi, 2001͒, and extract geometrical properties. Qualitative comparison between observations of fractures on image logs and with sonic anisotropy data is common ͑Mueller et al, 1994;Esmersoy et al, 1995;Prioul et al, 2004b;Donald and Bratton, 2006͒. However, quantitative interpretation is rare.…”

Section: Introductionmentioning

confidence: 93%

“…Dipole sonic azimuthal anisotropy is interpreted using a combination of factors ͑Esmersoy et al, 1994; Prioul et al, 2004b͒: small minimum and large maximum energy in the cross-components of Alford rotation ͑green shaded area, track 1͒, stable FSA ͑ meas FSA ͒ with small uncertainty ͑track 3͒, arrival-time difference observed on waveforms ͑tracks 4 and 5͒, and difference between fast-and slowshear slownesses ͑DTs meas fast and DTs meas slow , track 4͒. The Alford-rotation processing uses 11 receivers ͑i.e., a 5-ft array͒ and provides the FSA of waves that have traveled through 10-15 ft ͑3-5 m͒ of formation.…”

Section: Analysis Of Sonic-and Image-log Datamentioning

confidence: 99%

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Forward modeling of fracture-induced sonic anisotropy using a combination of borehole image and sonic logs

et al. 2007

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We develop a methodology to model and interpret borehole dipole sonic anisotropy related to the effect of geologic fractures, using a forward-modeling approach. We use a classical excesscompliance fracture model that relies on the orientation of the individual fractures, the elastic properties of the host rock, and the normal and tangential fracture-compliance parameters. Orientations of individual fractures are extracted from borehole-image log analysis. The model is validated using borehole-resistivity image and sonic logs in a gas-sand reservoir over a 160-ft ͑50 m͒ vertical interval of a well. Significant amounts of sonic anisotropy are observed at three zones, with a fast-shear azimuth ͑FSA͒ exhibiting 60°of variation and slowness difference between 2% and 16%. Numerous quasivertical fractures with varying dip azimuths are identified on the image log at the locations of strong sonic anisotropy. The maximum horizontal-stress direction, given by breakouts and drilling-induced fractures, is shown not to be aligned with the strike of natural fractures. We show that using just two adjustable fracture-compliance parameters, one fornatural fractures and one for drilling-induced fractures, is an excellent first-order approximation to explain the fracture-induced anisotropy response over a depth interval of 130 ft. Given the presence of gas and the absence of clay filling within the fractures, we assumed equal normal and tangential compliances. The two inverted normal compliances are Z N Ј f NAT = 1.7ϫ 10 −12 Pa −1 and Z N Ј f DI = 0.8ϫ 10 −12 Pa −1 . Predicted FSA matches measured FSA over 130 ft ͑40 m͒ of the 160-ft ͑50 m͒ studied interval. Predicted slowness anisotropy matches the overall variation and measured values of anisotropy for two of the three strong anisotropy zones. Analysis of the symmetries of the modeled anisotropic response shows that the medium is mostly a horizontal transverse isotropic medium, with small azimuthal variation of the symmetry axis. Analysis of each independent fracture type shows that the anisotropy is mainly driven by open or partially healed fractures, but also consistent with stress-related, drilling-induced fractures. Therefore, the measured sonic anisotropy is caused by the combination of stress and fracture effects where the predominance of one mechanism over the other is depth-dependent. This method provides a consistent approach to data interpretation by integrating borehole image and sonic logs that probe the formation at different depths of investigation around the borehole.

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Section: Introductionmentioning

confidence: 93%

Section: Analysis Of Sonic-and Image-log Datamentioning

confidence: 99%

Forward modeling of fracture-induced sonic anisotropy using a combination of borehole image and sonic logs

et al. 2007

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“…Several previous works (e.g., Crampin and Lovell, 1991, Prioul et al, 2004, Sayers and Kachanov, 1995 performed in the laboratory and in reservoir conditions highlighted that ultrasonic velocity anisotropy is partly (even entirely) due to the three-dimensional stress state applied to a rockmass or to individual fractures (Pyrak-Nolte, 1996). This stress opens existing cracks or results in newly formed micro-cracks (< 0.1 mm width) aligned parallel to the principal stress direction (Sayers and Vanmunster, 1991).…”

Section: Discussion On the Processes Inducing Anisotropy And Their Timingmentioning

confidence: 99%

P-wave velocity anisotropy related to sealed fractures reactivation tracing the structural diagenesis in carbonates

et al. 2017

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“…The estimation of the S H orientation in wells using a sonic logging tool requires measurement of the acoustic stress sensitivity of the formation, as well as the shear velocities in stress-induced anisotropic formation . The shear wave travels at different velocities with different propagating directions and polarizations in an anisotropic formation.…”

Section: In Situ Stress Orientation From Anisotropy Analysis For Well Amentioning

confidence: 99%

Cleat Orientation from Ground Mapping and Image Log Studies for In Situ Stress Analysis: Coal Bed Methane Exploration in South Karanpura Coalfield, India

Ali

Paul²,

Chatterjee

2017

Energy Fuels

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Conventional well logs from three wells in South Karanpura coalfield are analyzed to correlate 10 major coal seams belonging to the Barakar formation for coalbed methane (CBM) exploration. Face cleat orientations are studied for the Simana and Argada coal seams from the Sirka and Argada mines. It varies from 130 to 140°N for a total 349 face cleats observed from Simana and Argada coal seams. Cleat identification and its orientation have also been analyzed from simultaneous acoustic and resistivity (STAR) log from two CBM wells located 20–22 km away from the Sirka and Argada mines. Total number of fractures is found to be 1118 from two wells. Face cleats are not distinguishable from STAR image. Fracture density in coal is varying from 2.48 fractures per meter to 23.8 fractures per meter with a maximum density in the Banasgarah seam. Fracture orientations observed in Saunda, Sayal, Balkudra, Kurse, Hathidari, Banasgarah, and Argada vary from 10°N to 115°N. The in situ maximum horizontal stress (S H) is computed from azimuthal anisotropy data, using a cross-multipole array acoustic (XMAC) log from a well. Azimuthal anisotropy ranges between 3.5% and 7.6%. The S H orientation varies from 110°N to 115°N. It is observed from the XMAC azimuthal anisotropy analysis that the orientation of S H in the Argada seams varies from 110°N to 113°N. The 163 face cleats in Argada seams at Argada are oriented toward 135°N–140°N. This indicates that the face cleats are aligned parallel to subparallel with the S H direction. Good fracture density, open fractures/face cleats toward S H are to be useful for CBM extraction from these wells.

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Azimuthal anisotropy using shear dipole sonic: insights from the AIG 10 well, Corinth Rift Laboratory

Cited by 8 publications

References 14 publications

Forward modeling of fracture-induced sonic anisotropy using a combination of borehole image and sonic logs

Forward modeling of fracture-induced sonic anisotropy using a combination of borehole image and sonic logs

P-wave velocity anisotropy related to sealed fractures reactivation tracing the structural diagenesis in carbonates

Cleat Orientation from Ground Mapping and Image Log Studies for In Situ Stress Analysis: Coal Bed Methane Exploration in South Karanpura Coalfield, India

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