2019 AAPG Annual Convention and Exhibition:

Datapages, Inc.Print this page

Towards a Better Understanding of Architecture and Pore-Space Distribution in Clastic Sediments and Rocks: Studying Sedimentary Systems Using Simple Stratigraphic Forward Models

Abstract

As it becomes increasingly evident that major changes in the global energy system are unavoidable, the question arises what will be the role of sedimentary geologists who have contributed so much to the energy industry. There is widespread agreement that large-scale carbon capture and storage must be a significant component of the efforts to reduce CO2 emissions; and the same skills that are required to find and develop hydrocarbon reservoirs are also needed for identifying and taking advantage of geological formations for long-term subsurface storage of carbon. Other areas where our expertise in sedimentology and stratigraphy can gain increasing importance are: the response of coastal systems and rivers to sea-level rise; and learning more about the impacts of rapid climate change in the geological past. Addressing these challenging problems is aided by a slow but irreversible (r)evolution in the academic and applied geosciences: an increasing number of geoscientists are taking advantage of the boom in high-level computer programming and scripting applied to large datasets, often using open-source software. While a significant focus is placed on applying advanced machine learning techniques to difficult challenges, there are many questions that can be addressed using simple but transparent forward models and more conventional approaches to pattern recognition and data analysis. For example, important new insights into the geomorphology and stratigraphy of meandering fluvial and submarine systems can be gained through using a simple model of meandering and testing its validity with time-lapse satellite imagery and high-quality seismic reflection data. In another application, I illustrate how long-recognized stratal patterns in slope minibasins forming on mobile substrates can be quickly quantified and modeled, to improve our understanding of pore space and seal distribution in such settings.