Parameter Estimation and Sensitivity Analysis in Clastic Sedimentation Modeling | SLB
Campos Basin, Brazil, South America, Offshore
A. Acevedo, A. Khramtsov, H.A. Madhoo, L. Noomee, and D. Tetzlaff, Schlumberger
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Parameter Estimation and Sensitivity Analysis in Clastic Sedimentation Modeling

This chapter appears in Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment, available for purchase from Springer. This book explores the current state of the art and informs readers about the latest geostatistical and space-based technologies for assessment and management in the contexts of natural resource exploration, environmental pollution, hazards and natural disaster research. The content covers 3D visualization, time-series analysis, environmental geochemistry, numerical solutions in hydrology and hydrogeology, geotechnical engineering, multivariate geostatistics, disaster management, fractal modeling, petroleum exploration, geoinformatics, sedimentary basin analysis, spatiotemporal modeling, digital rock geophysics, advanced mining assessment and glacial studies, and range from the laboratory to integrated field studies.

Mathematics plays a key part in the crust, mantle, oceans and atmosphere, creating climates that cause natural disasters, and influencing fundamental aspects of life-supporting systems and many other geological processes affecting Planet Earth. As such, it is essential to understand the synergy between the classical geosciences and mathematics, which can provide the methodological tools needed to tackle complex problems in modern geosciences.

The development of science and technology, transforming from a descriptive stage to a more quantitative stage, involves qualitative interpretations such as conceptual models that are complemented by quantification, e.g. numerical models, fast dynamic geologic models, deterministic and stochastic models. Due to the increasing complexity of the problems faced by today's geoscientists, joint efforts to establish new conceptual and numerical models and develop new paradigms are called for.


During numeric modeling of clastic sedimentation, the modeler is faced with assigning values to several parameters that are difficult to estimate. However, proper handling of uncertainties translates to valuable uncertainty information in the output. To illustrate this process, we used a model called GPM (Geologic Process Modeler) to reproduce a deepwater turbidite system from the Campos Basin, offshore Brazil, for which good-quality 3D seismic was available. In the first stage we used a paleo-basin floor surface reconstructed from seismic and placed sediment sources at locations indicated by the inferred paleogeography. In the second stage, we varied several input parameters within a range of reasonable values while comparing results to observed data. The results of all plausible models, considered jointly, provide a picture of the uncertainty of the occurrence of observed features and sediment properties.

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