@inproceedings {Simula.SC.532,
	title = {A Flexible Stochastic Approach to Constraining Uncertainty in Forward Stratigraphic Models},
	journal = {18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation},
	year = {2009},
	publisher = {Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation},
	type = {Conference},
	abstract = {Pinpointing the location of reservoir rocks and stratigraphic traps is crucial to the cost-effective extraction of oil. Forward stratigraphic models have been used to aid interpretations of these rocks and traps by determining their potential sand, shale and silt content. These stratigraphic models often rely on diffusion as a basis for simulating the processes of rock-erosion and the deposition of sediment, which forms the basis for rock formation. Diffusion-based modelling is consistent with observations in sedimentary environments over long time-scales. However, a persistent problem with these models is the determination of coefficients that specify the effectiveness of diffusion for each sediment type. These coefficients, the so-called transport coefficients, are usually determined by a manual inversion to fit the data. In many cases, varying the transport coefficients by orders of magnitude can still achieve consistency with observations of sediment thickness from seismic data. By mapping the uncertainty in these transport coefficients to probability distributions, a probabilistic method can be used to determine the uncertainty in the output of the model, the basin bathymetry and the location of rocks and their original sediment compositions. We provide an example from the Ebro Basin, located off the east coast of the Iberian Peninsula. The stochastic approach consists of generating a surface response of the stratigraphic forward model for each point in the domain. The building blocks, in our case, are Hermite polynomials, an orthogonal set. The surface response is a function of the uncertain parameters in the stratigraphic model, the most prominent of which are the diffusion coefficients. Assigning probability distributions to the uncertain parameters then determines the statistical properties of the surface response model. We show the time-dependent development of uncertainty in the Ebro Basin in the sand, silt and shale content of deposited sediment using this methodology.},
	isbn = {978-0-9758400-7-8},
	author = {Clark, Stuart and Are Magnus {Bruaset} and Tor O. {S{\o}mme} and Tore M. {L{\o}seth}},
	editor = {Anderssen, R.S. and Braddock, R.D. and Newham, L.T.H.}
}
@misc {Simula.SC.510,
	title = {Handling Uncertainty in Numerical Models of Sedimentary Deposition: a Stochastic Approach},
	howpublished = {Talk given at the 33rd International Geological Congress},
	year = {2008},
	month = {August},
	abstract = {Forward stratigraphic models simulates the process of sedimentary deposition over geological time periods and helps geoscientists and engineers to understand the location of reservoir rocks and stratigraphic traps. The most popular and commonly used models are diffusion-based. A major draw-back with such models is that diffusion coefficients, that largely control the outcome of the simulations, are highly uncertain and difficult to derive., This clearly reduces their usefulness at describing the evolution of a basin. Diffusion coefficients represent conglomerations of physical processes in sedimentation and basin morphology that depend, for instance, on slope. By considering the diffusion coefficients as randomly distributed fields, the uncertainty in the morphology of a basin can be constrained. We use a Polynomial Chaos Expansion (PCE) to approximate the model at each spatial and temporal point of interest. The Probabilistic Collocation Method (PCM) is used to calculate the coefficients of the PCE, based on a few, specifically chosen, calculations of the underlying model of sedimentation. In contrast to the Monte Carlo approach, which may require hundreds or thousands of runs of the simulator, the PCM requires only a dozen or so, depending on the number of inputs and the desired accuracy. Furthermore, the PCM is not tied to a particular implementation of a sedimentation model, as it treats the model as a blackbox, and can be placed upon any depositional simulator. This method allows for the quantification of the stochastic moments of the height and the fractional distribution of the materials. By understanding the spatial distribution of the uncertainty in basin evolution, these depositional models can begin to have real meaning in industrial applications. Our approach is not dependent on depositional modelling, but can be applied to any process-oriented geological simulation.},
	author = {Clark, Stuart and Are Magnus {Bruaset} and Tore M. {L{\o}seth}}
}
@misc {Simula.SC.176,
	title = {Particle-Based Methods in the Modelling of Turbidity Currents and Turbidites},
	howpublished = {Talk at the 33rd IGC Congress in Oslo},
	year = {2008},
	month = {August},
	author = {Al-Khayat, Omar and Tore M. {L{\o}seth} and Are Magnus {Bruaset} and Hans Petter {Langtangen}}
}
