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- Volume 47, Issue 5, 1999
Geophysical Prospecting - Volume 47, Issue 5, 1999
Volume 47, Issue 5, 1999
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Filter formulation and wavefield separation of cross‐well seismic data [Link]
Authors Jérôme Mars, James W. Rector Iii and Spyros K. LazaratosMultichannel filtering to obtain wavefield separation has been used in seismic processing for decades and has become an essential component in VSP and cross‐well reflection imaging. The need for good multichannel wavefield separation filters is acute in borehole seismic imaging techniques such as VSP and cross‐well reflection imaging, where strong interfering arrivals such as tube waves, shear conversions, multiples, direct arrivals and guided waves can overlap temporally with desired arrivals. We investigate the effects of preprocessing (alignment and equalization) on the quality of cross‐well reflection imaging wavefield separation and we show that the choice of the multichannel filter and filter parameters is critical to the wavefield separation of cross‐well data (median filters, f–k pie‐slice filters, eigenvector filters). We show that spatial aliasing creates situations where the application of purely spatial filters (median filters) will create notches in the frequency spectrum of the desired reflection arrival. Eigenvector filters allow us to work past the limits of aliasing, but these kinds of filter are strongly dependent on the ratio of undesired to desired signal amplitude. On the basis of these observations, we developed a new type of multichannel filter that combined the best characteristics of spatial filters and eigenvector filters. We call this filter a ‘constrained eigenvector filter’. We use two real data sets of cross‐well seismic experiments with small and large well spacing to evaluate the effects of these factors on the quality of cross‐well wavefield separation. We apply median filters, f–k pie‐slice filters and constrained eigenvector filters in multiple domains available for these data sets (common‐source, common‐receiver, common‐offset and common‐midpoint gathers). We show that the results of applying the constrained eigenvector filter to the entire cross‐well data set are superior to both the spatial and standard eigenvector filter results.
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The 3D shear experiment over the Natih field in Oman. Reservoir geology, data acquisition and anisotropy analysis [Link]
Authors Potters, Groenendaal, Oates, Hake and KaldenThis paper describes a large‐scale reservoir characterization experiment carried out in Oman in 1991 which comprised the acquisition, processing and interpretation of a 28.4 km2 3D multicomponent seismic experiment over the Natih field. The objective of the survey was to obtain information on the fracture network present in the Natih carbonates from shear‐wave anisotropy. Shear‐wave anisotropy in excess of 20% time splitting was encountered over a large part of the survey. The seismic results are confirmed by geological and well data but provide additional qualitative information on fracturing where this was not available before. Regions of stronger and weaker shear‐wave anisotropy appear to be fault‐bounded. The average well flow rates (which are fracture‐dominated) within such blocks correlate with the average anisotropy of the blocks. The further observation that the anisotropy is largest in the fracture gas cap of the reservoir suggests that shear waves can provide a direct hydrocarbon indicator for fractured rock.
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Stacked [Link]
By HendricksonAnalysis of prestack P‐wave seismic data yields information about both the P‐ and S wave properties of the earth. An anticipated advantage of having two measurements (P and S) is that they can be combined into a new measurement that is less sensitive to lithology variations and more sensitive to fluid effects. The amplitude‐variation‐with‐offset (AVO) gradient is one such measure that is often used qualitatively as a fluid indicator. The gradient always becomes softer (more negative) when hydrocarbon replaces brine in the pore spaces but the overall AVO response is dominated by the lithology. Fluid effects are expressed primarily by the normal‐incidence P‐wave response and only secondarily by the offset dependence. The gradient often does not function as an effective fluid indicator. This is partially due to the fact that the gradient is roughly twice as sensitive to S‐ than to P‐wave properties. More importantly, effective random noise in the CMP gathers introduces a strong correlation between the AVO intercept and gradient and, hence, between the measured P‐ and S‐wave properties. This correlation in the AVO attributes corresponds to a significant error in the estimation of the S‐wave properties and can dominate the measurements from many of the popular AVO techniques. A simple method to minimize the effect of this noise‐induced correlation is to stack the data. The stack corresponds to a coordinate rotation in elastic space with the stack amplitudes measured along one of the new axes and the other (unmeasured) axis naturally tending to line up with the noise and thus suppressing it. Fluid effects cause the data to move roughly perpendicular to this noise trend. The stack axis is then in the direction of the fluid effect. The stack thus combines both the P‐ and S‐wave (normal and oblique incidence) information into a single measurement which can be made to optimally suppress background noise and highlight fluid effects. A major consequence of this interpretation is the simplicity of both prospect identification and quantitative amplitude analysis.
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On the interference of man‐made EM fields in the magnetotelluric ‘dead band’[Link]
More LessDuring processing of magnetotelluric (MT) data, acquired in a survey carried out in southern Italy, a problem was encountered, connected with the so‐called ‘dead band’ of the MT signal (around 1 Hz). In the apparent resistivity curves of some MT soundings, a V‐shaped minimum appeared, centred on the dead‐band frequency. This phenomenon turned out to originate from a strong artificial source and was not due to a downward bias of the robust processing techniques adopted. The source distance from the MT sounding locations was such that the V‐shaped minimum fell precisely in the dead‐band frequency range. Theoretical considerations about fields generated by an electric dipole led us to the probable identification of the source as the d.c.‐powered railway between Naples and Bari.
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Crooked line, rough topography: advancing towards the correct seismic image [Link]
Authors Samuel H. Gray, Gary Maclean and Kurt J. MarfurtSeismic exploration in mountainous areas imposes serious compromises on both acquisition and processing. Access restrictions usually result in profiles that are not straight and are not recorded along the true dip direction (if there is a true dip direction!). Processing constraints often result in very poor approximate corrections for elevations and for deviations from a straight line. Most fundamentally, 2D acquisition and processing assumes that the earth is 2D; this assumption is often seriously violated in mountainous areas. While we cannot efficiently correct 2D seismic data for the effects of a fully 3D subsurface, we can improve the data quality in thrust areas where the assumption of 2D subsurface variation is reasonable. We do this in a series of small steps, which improves the accuracy of several approximations made in processing 2D land data.
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Reflection moveout inversion in azimuthally anisotropic media: accuracy, limitations and acquisition [Link]
Authors AbdulFattah Al‐Dajani, Tariq Alkhalifah and Frank Dale MorganParameter estimation from the elliptical variations in the normal‐moveout (NMO) velocity in azimuthally anisotropic media is sensitive to the angular separation between the survey lines in 2D, or equivalently, the source‐to‐receiver azimuth in 3D, and to the set of azimuths used in the inversion procedure. The accuracy in estimating the orientation of an NMO ellipse, in particular the parameter α, is also sensitive to the magnitude of anisotropy. On the other hand, the accuracy in estimating the semi‐axes of the NMO‐velocity ellipse is about the same for any magnitude of anisotropy. To invert for the NMO ellipse parameters at least three NMO‐velocity measurements along distinct azimuth directions are needed. In order to maximize the accuracy and stability in parameter estimation, it is best to have the azimuths for the three source‐to‐receiver directions 60° apart. Having more than three distinct source‐to‐receiver azimuths (e.g. full azimuthal coverage) provides a useful data redundancy that enhances the quality of the estimates. In order to maximize quality in the inversion process, it is recommended to design the seismic data acquisition such that it contains small sectors (≤10°) with adequate fold and offset distribution. Using three NMO‐velocity measurements, 60° apart, an azimuthally anisotropic layer overlain by an azimuthally isotropic overburden (as might occur for fractured reservoirs) should have a relative thickness (in time) with respect to the total thickness at least equal to the ratio of the error in the NMO (stacking) velocity to the interval anisotropy of the fractured layer. Coverage along more than three azimuths, however, improves this limitation, which is imposed by Dix differentiation, by at most 50%, depending on the number of observations (NMO velocities) that enter the inversion procedure.
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Anti‐aliased Kirchhoff–Helmholtz transformations [Link]
More LessNon‐aliased integral (Kirchhoff‐type) transformations for forward and downward wavefield extrapolations in inhomogeneous media with interfaces are described. Special weights are computed to compensate for operator aliasing and finite‐aperture effects, even when the data are spatially aliased and irregularly sampled. Basic components of the algorithm, such as Green's function computation, can be replaced by alternative solutions in conjunction with ray‐tracing methods. Applications of this algorithm to model and real data in both two and three dimensions are discussed in terms of its impact on seismic modelling, multiple prediction and prestack imaging.
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Model‐based shear‐wave velocity estimation versus empirical regressions [Link]
Authors Arild Jørstad, Tapan Mukerji and Gary MavkoModelling of AVO signatures for reservoir characterization requires VS estimation from other available logs when shear‐wave data are not available. We tested various models for predicting VS from P‐wave velocity, porosity and shale volume measured in well logs. Effective medium models which characterize the pore space in terms of ellipsoidal inclusions were compared with statistical VP–VS regressions. The inclusion models were calibrated by non‐linear minimization of the difference between model‐predicted velocities and actual measured velocities. The quality of the VS prediction was quantified in terms of the rms error by comparison with shear‐wave data in wells where both VP and VS were measured. The linear regressions were found to be more robust and the rms error in the prediction was comparable to effective medium model‐based predictions.
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Volumes & issues
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Volume 72 (2023 - 2024)
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Volume 71 (2022 - 2023)
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Volume 70 (2021 - 2022)
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Volume 69 (2021)
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Volume 68 (2020)
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Volume 67 (2019)
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Volume 66 (2018)
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Volume 65 (2017)
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Volume 64 (2015 - 2016)
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Volume 63 (2015)
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Volume 62 (2014)
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Volume 61 (2013)
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Volume 60 (2012)
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Volume 59 (2011)
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Volume 58 (2010)
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Volume 57 (2009)
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Volume 56 (2008)
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Volume 55 (2007)
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Volume 54 (2006)
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Volume 53 (2005)
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Volume 52 (2004)
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Volume 51 (2003)
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Volume 50 (2002)
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Volume 49 (2001)
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Volume 48 (2000)
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Volume 47 (1999)
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Volume 46 (1998)
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Volume 45 (1997)
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Volume 44 (1996)
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Volume 43 (1995)
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Volume 42 (1994)
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Volume 41 (1993)
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Volume 40 (1992)
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Volume 39 (1991)
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Volume 38 (1990)
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Volume 37 (1989)
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Volume 36 (1988)
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Volume 35 (1987)
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Volume 34 (1986)
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Volume 33 (1985)
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Volume 32 (1984)
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Volume 31 (1983)
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Volume 30 (1982)
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Volume 29 (1981)
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Volume 28 (1980)
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Volume 27 (1979)
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Volume 26 (1978)
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Volume 25 (1977)
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Volume 24 (1976)
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Volume 23 (1975)
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Volume 22 (1974)
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Volume 21 (1973)
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Volume 20 (1972)
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Volume 19 (1971)
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Volume 18 (1970)
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Volume 17 (1969)
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Volume 16 (1968)
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Volume 15 (1967)
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Volume 14 (1966)
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Volume 13 (1965)
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Volume 12 (1964)
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Volume 11 (1963)
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Volume 10 (1962)
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Volume 9 (1961)
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Volume 8 (1960)
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Volume 7 (1959)
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Volume 6 (1958)
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Volume 5 (1957)
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Volume 4 (1956)
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Volume 3 (1955)
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Volume 2 (1954)
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Volume 1 (1953)