Datapages, Inc.Print this page

Waite, Lowell E.1, Eugene C. Rankey2 
(1) Pioneer Natural Resources USA, Inc, Irving, TX 
(2) RSMAS/University of Miami, Miami, FL

ABSTRACT: Source-to-Sink: A Systems Approach to Sedimentation and the Stratigraphic Record

Depositional systems are influenced by a variety of climatic, tectonic, oceanographic, biologic, and sedimentologic processes ranging in spatial and temporal scale. Interactions among processes and depositional products lead to complex patterns of sediment composition and distribution of intrinsically limited predictability. In this talk, we present a conceptual overview with specific examples of one approach to the application of source-to-sink (S2S) concept, focusing on an integrated, non-reductionist, process-oriented, systemic methodology that enhances understanding and predictability within sedimentary systems and the resultant stratigraphic record. 
Building upon a number of studies within such diverse fields as mathematics, physics, geography, biology, sociology, and economics, the approach explicitly acknowledges that: 1) sedimentary systems and the stratigraphic record are influenced by a spectrum of processes operating across a broad range of time and space scales (from plate tectonics to grain transport); 2) in many cases, feedbacks and linkages exist among various subsystems (e.g., ‘reefs beget reefs’); and 3) as a result, many depositional systems have properties that are either not transportable among scales, or that span a broad range of scales of consideration. An approach that explores questions of scale and scaling, of system non-linearity, and of interdependent linkages is complimentary to approaches employed in classic reductionism, in which systems are broken down into their components and analyzed in isolation. Many insights gained from a systemic method are directly applicable to hydrocarbon exploration and production (e.g., scaling relations for geologic models); moreover, industry datasets represent an underused resource for the generating and testing of model predictions.


AAPG Search and Discovery Article #90026©2004 AAPG Annual Meeting, Dallas, Texas, April 18-21, 2004.