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- Volume 46, Issue 3, 0198
Geophysical Prospecting - Volume 46, Issue 3, 0198
Volume 46, Issue 3, 0198
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Integrated geophysics for mineral exploration in drift‐covered volcanic terrains: examples from northern Vancouver Island, Canada[Link]
Authors C. Lowe, M.E. Best, P.T. Bobrowsky and D.A. SeemannKnown mineral occurrences in northern Vancouver Island are typically hosted in volcanic units of the Bonanza Group. At a local scale, the mineralization is associated with advanced argillic bedrock alteration and is often intimately related to porphyry intrusions. On a larger scale, faults are thought to exert the most significant control on the distribution of mineralized host rocks. Poor exposures and a complex glacial history limit the use of traditional methods of geological mapping and mineral exploration in this region and to date geophysical methods have been under‐utilized. Here we present findings from four standard geophysical (gravity, magnetics, electromagnetics and seismic refraction) methods, recently deployed here to elucidate the subsurface geology, as well as to identify new targets for base metal exploration.
Results at two different sites show that the integrated interpretation of geophysical data, constrained by physical rock property measurements, yields detailed images of the subsurface at a fraction of the cost of drilling. At one site, east of Rupert Inlet, the final subsurface model shows that the Bonanza Group is not nearly as extensive as previously presumed. An extension of the Holberg Fault is identified some 50 km east of the visibly mapped outcrop and an extensive zone of alteration around the fault is recognized. Furthermore, a number of the methods provide support for the existence of a porphyry dike at this site. At the second site, north of Rupert Inlet, magnetic and electromagnetic data prove effective at mapping alteration and locating shear zones beneath a relatively thin drift cover. Together, these results help outline a strategy for exploration in drift‐covered terrains and show that a redirection of exploration effort is warranted in the case of northern Vancouver Island.
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Robust multiple suppression using adaptive beamforming[Link]
Authors T. Hu and R.E. WhiteMinimum variance unbiased (MVU) beamforming is a type of multichannel filtering which extracts coherent signals without distortion, whilst minimizing residual noise power. Adaptive beamforming estimates signal and noise characteristics as part of the extraction process. The adaptive beamformer used here is designed from models of primary and multiple reflection signals having parametrically specified moveout and amplitude variation with offset (MVO and AVO). Phase variation with offset (PVO) can also be included but it is not usually justified in practice. The resulting analysis provides data for input into AVO and PVO schemes for obtaining lithological information. Synthetic data examples illustrate details of implementation of parametric adaptive MVU beamforming and the response characteristics of the resultant design. Real data examples show that data‐adaptive beamforming is more flexible and more effective in attenuating multiples in prestack common‐midpoint seismic data than Radon transform methods. In common with other prestack multichannel processes, the advantages of beamforming are shown to best effect in data with a good signal‐to‐noise ratio.
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Viscoelastic effective rheologies for modelling wave propagation in porous media[Link]
More LessBiot's poroelastic differential equations are modified for including matrix–fluid interaction mechanisms. The description is phenomenological and assumes a solid–fluid relaxation function coupling coefficient. The model satisfies basic physical properties such as, for instance, that P‐wave velocities at low frequencies are lower than those predicted by Biot's theory. In many cases, the results obtained with the Biot (two‐phase) modelling are equal to those obtained with single‐phase elastic modelling, mainly at seismic frequencies. However, a correct equivalence is obtained with a viscoelastic rheology, which requires one relaxation peak for each Biot (P and S) mechanism. The standard viscoelastic model, which generalizes compressibility and shear modulus to relaxation functions, is not appropriate for modelling the Biot complex moduli, since Biot's attenuation is of a kinetic nature (i.e. it is not related to bulk deformations). The problem is solved by associating relaxation functions with each wave modulus. The equivalence between the two modelling approaches is investigated for a homogeneous water‐filled sandstone and a periodically layered poroelastic medium, alternately filled with gas and water. The simulations indicate that, in the homogeneous case, particle velocities in the solid skeleton, caused by a source applied to the matrix, are equivalent to viscoelastic particle velocities. In a finely layered medium, viscoelastic modelling is not, in principle, equivalent to porous modelling, due to substantial mode conversion from fast wave to slow static mode. However, this effect, caused by local fluid‐flow motion, can be simulated by including an additional relaxation mechanism similar to the squirt‐flow.
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A semi‐empirical velocity‐porosity‐clay model for petrophysical interpretation of P‐ and S‐velocities[Link]
Authors Igor Goldberg and Boris GurevichWe design a velocity–porosity model for sand‐shale environments with the emphasis on its application to petrophysical interpretation of compressional and shear velocities. In order to achieve this objective, we extend the velocity–porosity model proposed by Krief et al., to account for the effect of clay content in sandstones, using the published laboratory experiments on rocks and well log data in a wide range of porosities and clay contents.
The model of Krief et al. works well for clean compacted rocks. It assumes that compressional and shear velocities in a porous fluid‐saturated rock obey Gassmann formulae with the Biot compliance coefficient. In order to use this model for clay‐rich rocks, we assume that the bulk and shear moduli of the grain material, and the dependence of the compliance on porosity, are functions of the clay content.
Statistical analysis of published laboratory data shows that the moduli of the matrix grain material are best defined by low Hashin–Shtrikman bounds. The parameters of the model include the bulk and shear moduli of the sand and clay mineral components as well as coefficients which define the dependence of the bulk and shear compliance on porosity and clay content. The constants of the model are determined by a multivariate non‐linear regression fit for P‐ and S‐velocities as functions of porosity and clay content using the data acquired in the area of interest.
In order to demonstrate the potential application of the proposed model to petrophysical interpretation, we design an inversion procedure, which allows us to estimate porosity, saturation and/or clay content from compressional and shear velocities.
Testing of the model on laboratory data and a set of well logs from Carnarvon Basin, Australia, shows good agreement between predictions and measurements. This simple velocity‐porosity‐clay semi‐empirical model could be used for more reliable petrophysical interpretation of compressional and shear velocities obtained from well logs or surface seismic data.
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Frequency–wavenumber modelling and migration of 2D GPR data in moderately heterogeneous dispersive media[Link]
Authors Adnand Bitri and Gilles GrandjeanAn algorithm for modelling and migrating ground penetrating radar (GPR) data in moderately heterogeneous dispersive media is presented. The method is based on wavefield extrapolation in the frequency–wavenumber (f–k) domain, from the solution of the 2D Maxwell's equations. The wavefield is extrapolated by a phase‐shift technique using a constant relative permittivity K and a quality factor Q. It is then modified by a correction term to handle the lateral K and Q variations. The spatial distribution of the K and Q‐factor values, representing the given model parameters, is introduced into the algorithm by a regular grid parametrization. The radar wave dispersion and attenuation, induced by relaxation processes, are taken into account by a linear frequency‐dependent Q model, and expressed by a complex wavenumber in the propagation equation.
A synthetic case and a field data set illustrate the potential of the method for frequencies of 300, 500 and 900 MHz. In the first case, a typical civil engineering problem is considered. The frequency dependence of the wave velocity and attenuation is well illustrated. The synthetic data are afterwards migrated using the initial model parameters. The results show the importance of using spatially varying model parameters in the migration processes. The second case concerns an application of the method to a real data set. In order to adjust the model parameters, a forward modelling sequence is performed until the best match between the measured and the synthetic data is achieved. A depth migration is then applied to the data, and the result is compared with the initial model parameters.
In conclusion, we assess the contributions of the method to industrial applications, by discussing the performance of the algorithm compared with its limitations.
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Filtering vibroseis data in the precorrelation domain[Link]
Authors Shuang Qin and David K. SmytheVibroseis data recorded at short source–receiver offsets can be swamped by direct waves from the source. The signal‐to‐noise ratio, where primary reflections are the signal and correlation side lobes are the noise, decreases with time and late reflection events are overwhelmed. This leads to low seismic resolution on the vibroseis correlogram. A new precorrelation filtering approach is proposed to suppress correlation noise. It is the ‘squeeze‐filter‐unsqueeze’ (SFU) process, a combination of ‘squeeze’ and ‘unsqueeze’ (S and U) transformations, together with the application of either an optimum least‐squares filter or a linear recursive notch filter. SFU processing provides excellent direct wave removal if the onset time of the direct wave is known precisely, but when the correlation recognition method used to search for the first arrival fails, the SFU filtering will also fail. If the tapers of the source sweeps are badly distorted, a harmonic distortion will be introduced into the SFU‐filtered trace. SFU appears to be more suitable for low‐noise vibroseis data, and more effective when we know the sweep tapers exactly. SFU requires uncorrelated data, and is thus cpu intensive, but since it is automatic, it is not labour intensive. With non‐linear sweeps, there are two approaches to the S,U transformations in SFU. The first requires the non‐linear analytical sweep formula, and the second is to search and pick the zero nodes on the recorded pilot trace and then carry out the S,U transformations directly without requiring the algorithm or formula by which the sweep was generated. The latter method is also valid for vibroseis data with a linear sweep. SFU may be applied to the removal of any undesired signal, as long as the exact onset time of the unwanted signal in the precorrelation domain is known or determinable.
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Well tie, fluid substitution and AVO modelling: a North Sea example[Link]
More LessAccurate well ties are essential to practical seismic lithological interpretation. As long as the geology in the vicinity of the reservoir is not unduly complex, the main factors controlling this accuracy are the processing of the seismic data and the construction of the seismic model from well logs. This case study illustrates how seismic data processing to a near‐offset stack, quality control of logs and petrophysical modelling improved a well tie at an oil reservoir. We demonstrate the application of a predictive petrophysical model in the preparation and integration of the logs before building the seismic model and we quantify our improvements in well‐tie accuracy. The data for the study consisted of seismic field data from a 3D sail line through a well in a North Sea oilfield and a suite of standard logs at the well. A swathe of fully processed 3D data through the well was available for comparison. The well tie in the shallow section from first‐pass seismic data processing and a routinely edited sonic log was excellent. The tie in a deeper interval containing the reservoir was less satisfactory: the phase errors within the bandwidth of the seismic wavelet were of the order of 20°, which we consider too large for subsequent transformation of the data to seismic impedance. Reprocessing the seismic data and revision of the well‐log model reduced these phase errors to less than 10° and improved the consistency of the deep and shallow well ties. The reprocessing included densely picked iterative velocity analysis, prestack migration, beam‐forming multiple attenuation, stacking the near‐offset traces and demigration and remigration of the near‐offset data. The petrophysical model was used to monitor and, where necessary, replace the P‐wave sonic log with predictions consistent with other logs and to correct the sonic log for mud‐filtrate invasion in the hydrocarbon‐bearing sand. This editing and correction of the P‐wave transit times improved the normal‐incidence well tie significantly. The recordings from a monopole source severely underestimated the S‐wave transit times in soft shale formations, including the reservoir seal, where the S‐wave velocity was lower than the P‐wave velocity in the drilling mud. The petrophysical model predicted an S‐wave log that matched the valid recordings and interpolated between them. The subsequent seismic modelling from the predicted S‐wave log produced a class II AVO anomaly seen on the CDP gathers around the well.
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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 18 (1970 - 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 40 (1992)
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Volume 39 (1991)
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Volume 37 (1989)
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Volume 4 (1956)
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Volume 1 (1953)