Given a seismic data set, there are numerous seismic attributes for geophysicists to analyze. Attributes that are based on the morphology and continuity (or discontinuity) of seismic reflections, are typically classified as Geometric Attributes, while those based on the character, phase, and amplitude of specific seismic events are classified as Waveform Attributes. Under proper conditions, and using an integrated approach, these attributes can be used for hydrocarbon detection and reservoir characterization.
Very often, we are faced with describing highly-faulted reservoirs, or delineating detailed stratigraphy, in areas with complex intersections or cross-cutting relationships. Perhaps the localization and characterization of natural fracturing is critical to a project for either well targeting or wellbore avoidance. Attributes such as coherence (incoherence), similarity and seismic curvature, can greatly aid in the identification of such features. Lago has performed studies in project areas where the fault displacements are subtle, and the dominant frequency of the seismic data is low. In the past, such areas presented a nearly insurmountable challenge in fault identification and resolution. Today, however, seismic curvature attributes are particularly adept at identifying and detecting faults and fault extents under these difficult conditions.
Waveform attributes have been used for decades to help geoscience teams identify such reservoir properties as lithology, fluid saturation, porosity and thickness. The attributes employed for this work are typically seismic amplitude, wavelet phase and/or frequency, and time thickness. Combinations of attributes can be used to help add statistical merit and visual reinforcement of these indicators. In most projects, seismic forward modeling is used to guide and assist in the interpretation of seismic attributes. Using 2D interpolation, models are constructed from petrophysical logs and depositional geometries to create a series of what-if scenarios for changes in rock and/or fluid properties. Using a wavelet extracted from actual seismic data, or a theoretical wavelet designed to represent the frequency content of actual seismic data, modeled reflections allow us to predict how reservoir changes are manifested on the actual seismic data. Waveform attributes are then extracted from the model, studied in detail, and then used as a template for reservoir characterization.