Satinder Chopra

Seismic attributes — A historical perspective

A seismic attribute is a quantitative measure of a seismic characteristic of interest. Analysis of attributes has been integral to reflection seismic interpretation since the 1930s when geophysicists started to pick traveltimes to coherent reflections on seismic field records. There are now more than 50 distinct seismic attributes calculated from seismic data and applied to the interpretation of geologic structure, stratigraphy, and rock/pore fluid properties. The evolution of seismic attributes is closely linked to advances in computer technology. As examples, the advent of digital recording in the 1960s produced improved measurements of seismic amplitude and pointed out the correlation between hydrocarbon pore fluids and strong amplitudes (“bright spots”). The introduction of color printers in the early 1970s allowed color displays of reflection strength, frequency, phase, and interval velocity to be overlain routinely on black-and-white seismic records. Interpretation workstations in the 1980s provided...

Emerging and future trends in seismic attributes

Seismic attributes extract information from seismic reflection data that can be used for quantitative and qualitative interpretation. Attributes are used by geologists, geophysicists, and petrophysicists to map features from basin to reservoir scale. Some attributes, such as seismic amplitude, envelope, rms amplitude, spectral magnitude, acoustic impedance, elastic impedance, and AVO are directly sensitive to changes in seismic impedance. Other attributes such as peak-to-trough thickness, peak frequency, and bandwidth are sensitive to layer thicknesses. Both classes of attributes can be quantitatively correlated to well control using multivariate analysis, geostatistics, or neural networks. Seismic attributes such as coherence, Sobel filter-based edge detectors, amplitude gradients, dip-azimuth, curvature, and gray-level co-occurrence matrix measures are directly sensitive to seismic textures and morphology. Geologic models of deposition and structural deformation coupled with seismic stratigraphy princip...

Volumetric curvature attributes add value to 3D seismic data interpretation

Horizon-based curvature attributes have been used in seismic data interpretation for predicting fractures since 1994 when Lisle demonstrated the correlation of curvature values to fractures measured on an outcrop. Different measures of curvature (such as Gaussian, strike, and dip) have been shown by different workers to be highly correlated with fractures, and many more applications are also possible. By definition, all such applications need the interpretation of a seismic horizon, which may be simple if data quality is good and the horizon of interest corresponds to a prominent impedance contrast.

Curvature attribute applications to 3D surface seismic data

Identifying subtle faults at or below the limits of seismic resolution and predicting fractures associated with folds and flexures is one of the major objectives of careful seismic interpretation. With the advent of common use of 3D seismic in the late 1980s, first-derivative-based horizon dip magnitude and dip azimuth were found to enhance faults that were otherwise difficult to see. More recently, second-derivative-based curvature maps have carried this process a step further. Horizon-based curvature computation is now available in the commercial workstation environment, putting these tools in the hands of geoscientists who do not have access to processing software and do not have time or inclination to program.

Volumetric curvature attributes for fault/fracture characterization

Seismic attributes have proliferated in the last three decades at a rapid rate and have helped interpreters in making accurate predictions in hydrocarbon exploration and development. Attributes sensitive to amplitude, such as impedance inversion and AVO, are widely used for lithological and petrophysical prediction of reservoir properties. Other attributes, such as coherence and curvature, are particularly useful in mapping the structure and shape of geological features of interest. It is these latter types of attributes that are of interest for fault/fracture characterization, and in this paper we discuss the applications of volumetric curvature attributes for this purpose. Horizon-based curvature attributes (Roberts, 2001) have been used in seismic data interpretation for predicting fractures ever since Lisle (1994) demonstrated the correlation of curvature values to fractures measured on an outcrop. Different measures of curvature (Gaussian, strike, dip, etc.) have been shown by different workers to be highly correlated with fractures (Hart, 2002; Ericsson et al., 1988; Sigismondi and Soldo, 2003; Massaferro et al., 2003); many more applications can be found in Chopra and Marfurt (2007a, 2007b). As the name implies, horizon-based curvature is computed directly from a picked seismic horizon which in general requires that the data quality be good and that the horizon of interest corresponds to a prominent impedance contrast. Horizons picked on noisy surface seismic data or when picked through regions where no continuous surface exists can produce misleading curvature measures. A common means of addressing such problems is to spatially filter the horizon picks, with the goal of removing the noise and retaining features of geologic interest (Bergbauer et al., 2003; Chopra et al. 2006). Once picked and filtered, a mathematical quadratic surface is fitted to the picked data within a user-defined aperture. The different measures of curvature are then computed analytically from the coefficients of the quadratic surface. Roberts (2001) demonstrated the application of different curvature attributes including minimum and maximum curvatures, mean curvature, dip curvature, strike curvature, mostpositive and most-negative curvature, and shape index. Of this list, we find the most-positive and most-negative curvature measures to be the easiest to directly relate to commonly encountered geologic structural and stratigraphic features.

Applications of texture attribute analysis to 3D seismic data

In this study, texture attribute analysis application to 3D surface seismic data is presented. This is done by choosing a cubic texel (texture element) from the seismic data to generate a gray-level occurrence matrix, which in turn is used to compute second-order statistical measures of textural characteristics. The cubic texel is then successively made to glide through the 3D seismic volume to transform it to a plurality of texture attributes. Application of texture attributes to two case studies from Alberta confirm that these attributes enhance understanding of the reservoir by providing a clearer picture of the distribution, volume, and connectivity of the hydrocarbon-bearing facies in the reservoir.

Velocity determination for pore-pressure prediction

Overpressured formations exhibit several of the following properties when compared with a normally pressured section at the same depth (Dutta, 2002): (1) higher porosities, (2) lower bulk densities, (3) lower effective stresses, (4) higher temperatures, (5) lower interval velocities, and (6) higher Poissons ratios.

Quantitative estimate of fracture density variations in the Nordegg with azimuthal AVO and curvature: A case study

We investigated the accuracy of surface seismic attributes in predicting fracture density variations within the Nordegg Formation in west central Alberta. We know from core, drill samples, well-log, and drilling data that the Nordegg zone is fractured to some degree. These fractures are of interest because the reservoir has very low permeability, and therefore natural fractures may materially affect well performance. 3D surface seismic techniques such as amplitude variation with azimuth or azimuthal AVO (AVAz), variation of velocity with azimuth (VVAz), curvature, and coherence techniques are all tools that have been used to predict fractures in a qualitative fashion. In this study, we wanted to understand how well these attributes predicted the reservoir quality in a quantitative fashion. Previous quantitative studies have used image log orientation data or estimated ultimate recoveries (EUR) in vertical wells as validation data. The conclusiveness of these studies has been subject to several problems: f...

Coherence and curvature attributes on preconditioned seismic data

Seismic data are usually contaminated with both random and coherent noise, even when the data have been properly migrated and are multiple-free. Seismic attributes are particularly effective at extracting subtle features from relatively noise-free data. Certain types of noise can be addressed by the interpreter through careful structure-oriented filtering or postmigration footprint suppression. However, if the data are contaminated by multiples or are poorly focused and imaged due to inaccurate velocities, the data need to go back to the processing team.

Detecting stratigraphic features via cross-plotting of seismic discontinuity attributes and their volume visualization

Over the last decade, our industry has witnessed steadily increasing computer power, core memory, peripheral storage, and advanced display technology, as well as development of commercial software backed by dedicated research efforts. These developments have lead to a moderate acceptance of volume interpretation of 3D seismic data by the geoscience community. 3D volume rendering is one form of visualization that involves opacity control to view the features of interest “inside” the 3D volume. A judicious choice of opacity applied to edge-sensitive attribute subvolumes, such as curvature or coherence, corendered with the seismic amplitude volume can both accelerate and lend confidence to the interpretation of complex structure and stratigraphy.