Book Description

Sedimentological models capture the processes and subsequent deposits that explain the distribution of facies within a depositional system. The first sedimentological models for deep-water depositional systems were portrayed as idealized shelf break to slope submarine basin sediment dispersal systems. These models were developed from ancient outcrop exposures (Mutti and Lucchi, 1972) and from the modern day seafloor (Normark, 1970, 1978). More recent model development has been based largely on observations from modern slope channels including the Amazon Channel (Pirmez and Imran; 2003), offshore West African (Abreu et al., 2003; Deptuck et al., 2003), and attempts at generalization from multiple studies (Mayall et al., 2006), as well as ancient outcrop studies (e.g., Brushy Canyon; Gardner et al., 2003). Concepts from these sedimentological models have been the principle foundation for development of quantitative geostatistical models. A geostatistical model adapts the conceptualization of facies distribution from the sedimentological model. This information is then coded into a three-dimensional, gridded computer model directly constrained to available data (i.e., wireline logs, core data, and seismic attributes). Geostatistical models developed for deep-water depositional systems have primarily focused on either sinuous channels confined by levees or erosional surfaces (e.g., Larue and Hovadik, 2006; Labourdette et al., 2007; Pyrcz et al., 2008; McHargue et al., 2010; Sylvester et al., 2010) or basin-floor or overbank lobes associated with loss of confinement from sinuous channels (Pyrcz et al., 2005; Wellner et al., 2006; Zhang et al., 2009). Although widely used, such geostatistical models have limited applicability in fitting all deep-water depositional systems, and cases exists that require modification of such models or creation of entirely new models. In this dissertation I show the importance of synthesizing sedimentological and geostatistical models based on observations from the data. The primary objectives of this dissertation are 1) to present methodologies to enable the creation of better sedimentological models from remote sensing data, and 2) to present a means to model depositional architectures for a system that cannot currently be captured with standard geostatistical modeling approaches. The main contributions are threefold. The first contribution, presented in Chapter 1, is a methodology designed to extract subseismic, lithologic information from inverted pre-stack seismic reflectivities. Also, in Chapter 1, the predictive power of this methodology is demonstrated on a dataset from the subsurface of the Molasse Basin in Upper Austria. Beyond this dissertation, Bernhardt et al. (in review) adopted the methodology to support the development of a more predictive sedimentological model for the same dataset. The second contribution, presented in Chapter 2, is a new approach for building predictive quantitative spatial models for a deep-water channel belt, in which sand deposition is controlled by mass-transport-deposit-topography. This methodology leverages sedimentological interpretations derived from subseismic, lithologic information as presented in Chapter 1 and the sedimentological work of Bernhardt et al. (in review). The final contribution of this dissertation is presented in two outcrop studies. Chapters 3 and 4 utilize extensive data collected from deep-water channel outcrops to build digital outcrop models. The model from Chapter 3 is used to demonstrate the predictive power of pre-stack seismic-reflectivity data in interpreting the large-scale architecture of a heterolithic deep-water channel system exposed in the sea cliffs along Blacks Beach near La Jolla, California. Finally, the outcrop modeling study presented in Chapter 4 presents a methodology to capture structural and stratigraphic uncertainty in outcrop observations in order to analyze the three-dimensional channel morphology of the Cerro Toro deep-water channel belt exposed in Sierra del Toro outcrops in the Magallanes Basin of Chile. These four chapters are described in more detail below.