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GoUnderstanding fracture density and orientation is key to producing many carbonate reservoirs. Over the years many methods have been used to extract this information from seismic data. These methods include shear wave birefringence and velocity variations of P-wave data resulting from the anisotropy due to the fracturing. We want to investigate the class of geometrical attributes and the class of spectral attributes as a means of detecting fractures. This will avoid the time consuming process of picking P-wave data and the expense of multi-component data for shear wave analysis. To conduct a controlled experiment, we constructed a fracture model and acquired a various sets of data employing differing offsets and azimuths. Some of the geometrical attributes were able to identify the fractures while others were not. Of the spectral attributes, the dominant frequency was used to identify the fractures.

acquisition, amplitude, analysis, azimuth, carbonate reservoir, coherence, component, curvature, data, event, Figure, fracture, frequency, meeting, model, orientation, principle component, shear wave, spectral, velocity

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Most land seismic data volumes suffer from acquisition footprint which can often be confused with faults, fractures, and karst. Geometric attributes such as coherence and curvature enhance the appearance of subtle geologic features, but also exacerbate footprint artifacts. We use this "negative" feature of geometric attributes as a means of characterizing short-wavelength footprint components. Once characterized, we subtract the footprint from the migrated seismic data volume using an adaptive subtraction technique and recompute attributes from these filtered data volumes. We demonstrate the effectiveness and pitfalls of this approach on a data volume exhibiting karst from the Central Basin Platform of west Texas,

Seismic data are plagued with various kinds of noise. While there are a wide array of preprocessing steps to suppress the noise before the data are stacked these steps are not perfect such that some noise will always contaminate our data. One such type of noise is acquisition footprint. Marfurt et al. (1998) define acquisition footprint as "any pattern of noise that is highly correlated to the geometric distribution of sources and receivers on the earth''s surface." Causes of acquisition footprint include inaccurate velocity models, inaccurate statics, migration operator aliasing, leakage of aliased coherent noise (i.e. surface waves), irregular patterns of varying fold and azimuthal distribution, and incomplete data due to obstacles.

Acquisition footprint is vexing to interpreters since it can be confused on time slices with faults, fractures, and karst features of geologic interest. Avoiding acquisition footprint is nearly impossible with 3D data due to suboptimal acquisition geometries arising from limited exploration budgets and limited field access.

Seismic attributes allow interpreters to extract subtle geologic features in the seismic data that may otherwise be difficult to see. Unfortunately, some of these geologic features have forms that appear similar to acquisition footprint. As an example, the objective in Figure 1a is to map subtle karst features at the San Andres level that may form an updip seal for oil production to the east. If we look at a slightly shallower time slice in Figure 1b, we note that the "karst" features at the San Andres level may actually be a footprint artifact. In general, small defects in the seismic data will be exacerbated by seismic attributes. Attributes such as coherence are sensitive to lateral changes in wave form. Attributes such as the Sobel filter edge detector (Luo et al., 1996), the generalized Hilbert transform (Luo et al., 2004), and coherent amplitude gradients (Marfurt, 2006) are sensitive to lateral changes in amplitude along a dipping reflector. Curvature (al Dossary and Marfurt, 2006) is sensitive to lateral changes in reflector dip.

Different acquisition and processing errors can give rise to changes in waveform, amplitude, and dip (Hill, 1999). Migration and DMO artifacts due to data and/or operator aliasing give rise to organized ellipses that result in changes in both amplitude and apparent dip. Systematic errors in velocity analysis can also give rise to organized "acquisition footprint" (Hedke et al., 2007). In general, attributes are sensitive to relatively short wavelength components of acquisition footprint, making attributes an excellent tool in footprint characterization.

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

We report on a field comparison of different seismic motion sensors. The CREWES Project at the University of Calgary acquired a 3C-2D seismic line in the Spring Coulee area of Southern Alberta in January 2008. This was a unique opportunity to compare two types of multicomponent sensors with acquisition occurring at the same time and with the same receiver parameters. This 6.52 km 2D acquisition was laid out with a digital MEMS accelerometer: the DSU3-428 and the accompanying Sercel 428XL recording system; as well as an analog 3C geophone: the SM-7 high resolution geophone element placed in a modified PE-6/S nail type case co-developed by Sensor Nederland (A Division of ION Geophysical) and ARAM Systems with the accompanying ARAM Aries MC recording system. There have been limited acquisition comparison tests performed and/or published with MEMS accelerometers and analog geophones in the past; the purpose of this study is to compare data acquired with single-point 3C receivers laid out side-side in a commercial recording environment.

accelerometer, acquisition, Comparison, coupling, data, data acquisition, field, Figure, geophone, line, meeting, MEM, mem accelerometer, mem sensor, receiver, recording, sensor, Station, system, test

Oilfield Places:

- North America > United States > Wyoming > Green River Basin (0.99)
- North America > United States > Utah > Green River Basin (0.99)

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Stable and accurate numerical modeling of seismic wave propagation in the vicinity of high-contrast interfaces is achieved with straightforward modifications to the conventional, rectangular-staggered-grid, finite-difference (FD) method. Improvements in material parameter averaging and spatial differencing of wavefield variables yield high-quality synthetic seismic data.

America, boundary, Bulletin, geophysics, media, meeting, menu, modeling, reference, reference list, Research Letter, SEG, seg las vegas, Simulation, society, Wave, wave propagation

Several method exist to invert for a velocity model from seismic data. Most commonly used methods are defined in the image domain. However, some methods that are defined in the data domain also exist. In this paper we review and compare a few methods on synthetic data, using the convolutional model and NMO traveltimes to model the data. Special attention will be given to the behavior of the different methods in the presence of multiples. In traditional MVA methods these usually pose a problem since multiples are not flattened or focused for the correct velocity model. In the data domain multiples do focus for the correct velocity model if they are correctly modeled. This is illustrated with a simple example. Another simple example illustrates how ideas from waveform inversion and data-domain velocity analysis can be combined to obtain the correct velocity model and reflectivity from synthetic data with multiples.

In waveform inversion one tries to infer a set of model parameters, in particular velocity, from seismic data by fitting the data in a leastsquares sense (Tarantola, 1984). While intuitively pleasing, this approach has several drawbacks. First, in order to get reliable results, the modeling used has to be accurate. Second, because of the absence of low frequencies in the data, it is difficult to obtain information about the slowly-varying components of the velocity model. The latter problem is addressed by Migration Velocity Analysis (MVA). However, this approach is usually based on high-frequency and/or one-way approximations of the wave equation. This prevents MVA to yield good results in the presence of strong multiples, which are not accounted for by these approximations. However, there are that try to use the multiples by removing them or transforming them into primaries (Verschuur and Berkhout, 2007).

Velocity analysis can also be performed in the data domain by generating data for the estimated reflectivity and comparing them to the observed data (Chavent et al., 1994; Plessix et al., 1999). Recently a method has been proposed that uses the correlation of observed and predicted data (van Leeuwen and Mulder, 2007a). This correlation will ''focus'' for the correct velocity model. By automatically updating the velocity model to optimize the amount of focusing it is indeed possible to obtain a good NMO velocity model (van Leeuwen and Mulder, 2007b). We will refer to this velocity analysis method as DVA in the rest of the paper.

In principle, it should be possible to obtain a good velocity model with DVA in the presence of multiples, if the multiples are modeled correctly. But to model the multiples correctly, the migrated image, from which the synthetic data are modeled, has to be correct. In general the migrated image will contain spurious events due to multiples in the data. However, by iteratively updating the migrated image, it should be possible to obtain the correct reflectivity if the velocity model is correct. This leads to two extremes: (1) given the correct velocity model, we can obtain the correct reflectivity with least-squares inversion; (2) given the correct reflectivity, we can obtain the correct velocity model with the data-correlation method.

Reverse-time migration (RTM) provides superior images in areas where there are steep salt flanks or other complex geologic structures. However, the high cost of running RTM with regard to memory requirement and computation time makes it difficult to use RTM for routine large volume production. By dividing the subsurface into two or three regions in depth according to the structures of the velocity model and applying RTM from top to bottom sequentially in each region, we are able to make RTM very cost effective for production usage. Furthermore, Kirchhoff migration or one-way wave equation migration may be used to replace RTM in a region where the velocity model is relatively simple and RTM may not help to generate a better image. This hybrid approach may further improve the computation efficiency and the quality of migration images.

approach, Computation, computation time, Figure, image, meeting, memory, memory requirement, method, migration, region, region II, RTM, structure, surface, velocity, velocity model, Wave

It is a rare and exhilarating moment when an emerging technology develops so rapidly that it warrants a special session at the SEG Annual Meeting, while, at the same time, it remains so compact that most of what is known on the topic will be presented at that session. We find ourselves in that situation in this special session on the use of simultaneous sources in the marine environment. In this talk I will give an introduction to the session including some history of the problem, both within and outside of the geophysical community, as well as motivation and current issues that are stimulating research and commercial efforts.

acquisition, approach, communication, data, development, effect, geophysics, Industry, information, meeting, not, result, SEG, session, shot, signal, source, survey, technology, time

amplitude, approach, attenuation, data, difference, Figure, frequency, FX prediction, input, iteration, meeting, noise, noise attenuation, noisy, output, result, signal, swell, trace

SPE Disciplines:

Seismic attributes form an integral part of most interpretation projects completed today. For doing an effective job or for extracting accurate information from seismic attributes, the input seismic data needs to be optimally processed. The term ''optimally'' essentially means that any or all distortion effects, whether nearsurface, or amplitude/phase related, or others are taken care of during processing if not totally eliminated. When such pre-stack or poststack data are loaded on workstations, they may still show a certain amount of noise level. This noise could be of various sorts - acquisition related, processing artifacts or random. In this presentation we focus our attention on conditioning of such data for derivation of attributes from them. Besides this, we also discuss the use of some of the procedural steps for noise filtering and dip-steering options for computation of some geometric attributes like coherence and curvature. Finally in this context, we also discuss the impact the choice of algorithm can have on the final results. All these factors ensure that the seismic attributes yield more accurate information for interpretation.

As dip-steered mean or median filters and alphatrimmed mean filters work on seismic data, they smear fault information, besides marginally lowering the frequency content of the data.

Hoecker and Fehmers (2002) address this problem through the use of an ''anisotropic diffusion'' smoothing algorithm. The diffusion part of the name implies that the filter is applied iteratively, much as an interpreter would apply iterative smoothing to a time-structure map. Most important, no smoothing takes place if a discontinuity is detected, thereby preserving the appearance of major faults and stratigraphic edges.

We describe a velocity estimation technique which uses the time-shift imaging condition for wave equation prestack depth migration. The time-shift parameter is converted to a perturbation in RMS velocity, which we convert to interval velocity. Our approach can resolve velocity errors in the presence of subsurface complexity that would hamper velocity analysis with surface data. We demonstrate the method on a realistic 2D synthetic land example.

Thank you!