Inversion for seismic anisotropy using genetic algorithms

Horne, Stephen; MacBeth, Colin

doi:10.3997/2214-4609.201411660

Cited by 16 publications

(20 citation statements)

References 6 publications

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“…The estimation routine predicts no apparent change in the qSl directions associated with the latter time-delay variation. The results for the first two stages are consistent with the results of Horne and MacBeth (1994) who interpret the anisotropy as a nearvertical saturated crack system with a crack density of 0.03 (3 % velocity anisotropy) for raypaths to receivers in the Tonkawa sandstone. The 5% zone appears to correlate with the start of the sandstone sequence (Horne, pers.…”

Section: Wave Type Separationsupporting

confidence: 90%

Processing of a nine‐component near‐offset VSP for seismic anisotropy

MacBeth

Zeng

et al. 1997

GEOPHYSICS

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A convolutional sequence of matrix operators is offered as a convenient deterministic scheme for processing a multicomponent vertical seismic profile (VSP). This sequence is applied to a nine-component near-offset VSP recorded at the Conoco borehole test facility, Kay County, Oklahoma. These data are corrected for tool spin and near-surface anisotropy together with source coupling or imbalance. After wave-field separation using a standard f -k filter, each source and receiver pair for the upgoing waves is adjusted to a common reference depth using a matrix operator based on the downgoing wavefield. The up-and downgoing waves are then processed for anisotropy by a similarity transformation, to separate the qS1 and qS2 waves, from which the anisotropic properties are estimated. These estimates reveal a strong (apparent) vertical birefringence in the near-surface, but weak or moderate values for the majority of the subsurface. The target zone consists of a thin sandstone and deeper shale layer, both of which possess a strong vertical birefringence. The sandstone corresponds to a zone of known fluid flow. An observed qS2 attenuation and polarization change in the shale suggest it contains large fractures.

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Section: Wave Type Separationsupporting

confidence: 90%

Processing of a nine‐component near‐offset VSP for seismic anisotropy

MacBeth

Zeng

et al. 1997

GEOPHYSICS

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“…This extends its ability to a wide range of applications. Recently this approach has been employed to many different optimization problems including non-linear geophysical inversion [16].…”

Section: Genetic Algorithmmentioning

confidence: 99%

An Intelligence Approach for Porosity and Permeability Prediction of Oil Reservoirs using Seismic Data

Joonaki¹,

Ghanaatian²,

Zargar³

2013

IJCA

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Nowadays the main challenge is to obtain a method for the estimation of key reservoir parameters with the lowest possible estimation error. Accurate reservoir characterization requires the integration of core and log data to understand the variation in hydraulic properties such as porosity, permeability and capillary pressure. Time-lapse seismic can be used as an important tool in reservoir characterization, monitoring and management. Reservoir parameters are converted to seismic parameters by using the rock physics models. This paper presents an analysis and explanation of an approach of developing rock physics model, and explains how the input data can be obtained to the model. And also this study presents an intelligence approach for the oil reservoir characterization by using seismic elastic properties and rock physics model together with minimum estimation error.

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“…The polarization data were modelled by automatic inversions using the genetic algorithm of Horne and MacBeth (1994), where 40 400 models were compared with the data, followed by a localized grid search about the best model found (as are incorporated in more recent versions of the automated inversion (Horne et al 1997)). The inversion used isotropic background P-and shear-wave velocities of V P 5.763 km/s and V S 3.376 km/s as measured in situ at the 420-level (Talebi and Young 1989) and a density r 2.63 g/cm 3 as determined from core samples (Read and Martin 1991).…”

Section: Inversion Of Polarizationsmentioning

confidence: 99%

“…The inversion used isotropic background P-and shear-wave velocities of V P 5.763 km/s and V S 3.376 km/s as measured in situ at the 420-level (Talebi and Young 1989) and a density r 2.63 g/cm 3 as determined from core samples (Read and Martin 1991). The genetic algorithm was designed to invert shear-wave data and has been successful in inverting several data sets (Horne and MacBeth 1994). The genetic algorithm was designed to invert shear-wave data and has been successful in inverting several data sets (Horne and MacBeth 1994).…”

Section: Inversion Of Polarizationsmentioning

confidence: 99%

Seismic anisotropy in granite at the Underground Research Laboratory, Manitoba

Holmes¹,

Crampin²,

Young³

2000

Geophysical Prospecting

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The Shear‐Wave Experiment at Atomic Energy of Canada Limited's Underground Research Laboratory was probably the first controlled‐source shear‐wave survey in a mine environment. Taking place in conjunction with the excavation of the Mine‐by test tunnel at 420 m depth, the shear‐wave experiment was designed to measure the in situ anisotropy of the rockmass and to use shear waves to observe excavation effects using the greatest variety of raypath directions of any in situ shear‐wave survey to date. Inversion of the shear‐wave polarizations shows that the anisotropy of the in situ rockmass is consistent with hexagonal symmetry with an approximate fabric orientation of strike 023° and dip 35°. The in situ anisotropy is probably due to microcracks with orientations governed by the in situ stress field and to mineral alignment within the weak gneissic layering. However, there is no unique interpretation as to the cause of the in situ anisotropy as the fabric orientation agrees approximately with both the orientation expected from extensive‐dilatancy anisotropy and that of the gneissic layering. Eight raypaths with shear waves propagating wholly or almost wholly through granodiorite, rather than granite, do not show the expected shear‐wave splitting and indicate a lower in situ anisotropy, which may be due to the finer grain size and/or the absence of gneissic layering within the granodiorite. These results suggest that shear waves may be used to determine crack and mineral orientations and for remote monitoring of a rockmass. This has potential applications in mining and waste monitoring.

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Inversion for seismic anisotropy using genetic algorithms

Cited by 16 publications

References 6 publications

Processing of a nine‐component near‐offset VSP for seismic anisotropy

Processing of a nine‐component near‐offset VSP for seismic anisotropy

An Intelligence Approach for Porosity and Permeability Prediction of Oil Reservoirs using Seismic Data

Seismic anisotropy in granite at the Underground Research Laboratory, Manitoba

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