Motivated by the needs for creating fast and accurate models of complex geological scenarios, accuracy and efficiency of three stencils for the isotropic eikonal equation on rectangular grids are evaluated using a Fast Marching implementation. The stencils are derived by direct modelling of the wave front, resulting in new and valuable insight in terms of improved upwind and causality conditions. After intro- ducing a method for generalising first order upwind stencils to higher order, a new second order Diagonal stencil is presented. Similarly to the Multistencils Fast Marching ap- proach, the Diagonal stencil makes use of nodes in the diagonal directions, whereas the traditional Godunov stencil uses solely edge-connected neighbours. The Diagonal stencil uses nodes close to each other, reaching upwind, to get a more accurate estimate of the angle of incidence of the arriving wave front. Although the stencils are evaluated in a Fast Marching setting, they can be adapted to other efficient eikonal solvers. All first and second order stencils are evaluated in a range of tests. The first test case models a folded structure from the Zagros fold belt in Iran. The other test cases are constructed to investigate specific properties of the examined stencils. The numerical investigation considers convergence rates and CPU times for non-constant and constant speed first-arrival computations. In conclusion, the Diagonal stencil is the most efficient and accurate of the three alternatives.

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.

Research background Software maintenance is both difficult and costly, and software organizations have little tangible evidence with regards to how they can improve their software maintenance practices and precisely what elements comprise the tradeoffs, the expected costs, and the benefits of alternative solutions. Scientific challenges The effort required to maintain software systems depends on many interacting factors, such as the software system's structural properties; the use of analysis, design principles and patterns; available documentation of requirements; design and code; tool support; and developer skills. Given the complexities of the problems at hand, empirical studies in realistic yet controlled settings are needed to better understand how such factors drive the costs of software maintenance and the quality of the software artefacts. In the Maintenance of Object-Oriented Software (MOOSE) project at Simula Research Laboratory, we have pursued large-scale, controlled experiments that combine a high degree of realism with the necessary degree of control, so as to obtain credible results that are applicable to the problems faced by the software industry. Obtained and expected results The studies reported herein contribute to a significantly improved understanding of software maintenance performance by pushing the boundaries of state-of-the-art empirical software engineering research. The results can help software maintenance organizations improve the efficiency of their maintenance processes and the quality of the products being maintained. Specifically, the results provide empirical evidence on tradeoffs and expected costs and benefits when applying design principles and patterns, simple task prioritization techniques, the Unified Modelling Language (UML), and pair programming in different maintenance contexts.

his talk describes a novel numerical modelling approach to simulate a sand-laden, highly turbulent fluid flow, also known as a turbidity current, with the Lattice Boltzmann method. Turbidity currents are sediment laden, highly turbulent submarine flows that are often triggered by catastrophic events like tsunamis or earthquakes. When a tur- bidity current comes to rest, the heaviest, coarsest grains settle first and the finest, lightest grains settle last. These deposits are called Turbidites. Turbidity currents constitute an important factor in the transport of clastic sediments into deep waters . Deposits from such flows are common in the deep water areas throughout the world and many of them constitute important petroleum reservoirs. The modelling and prediction of such deposits are therefore of prime interest both in the academic community and in the industry.Traditionally, turbidity currents have been modelled as a multiphase fluid flow in terms of continuum physics in a partial differential equation setting. Numerous models exist, and they are almost without exception based on traditional computational fluid dynamics (CFD). These are based on the assumption that the fluid and other quantities are in principle a continuum. Although improvement has been made, such models are associated with challenges. It is for instance difficult phenomenologically to describe and model the dynamics, deposition and erosion of sand particles in complex fluid flow from a traditional CFD approach.We will introduce a framework for modelling turbidity currents and turbidite development. The fluid phase is modeled by the Lattice Boltzmann equation, and the sand dynamics is described by the Basset-Boussinesq-Oseen (BBO) for spherical particles in a turbulent fluid. The two phases interact non-linearly through momentum exchange. We will describe basic features of the model and give an overview of the numerical implementation. Current status and outlook on the development process will be reported as well.

Simula Research Laboratory conducts research in the fields of scientific computing, software engineering, and networks and distributed systems. In addition, the organization has a strong focus on education and innovation. Recently, the educational efforts have been strengthened through the foundation of the Simula School of Research and Innovation, a graduate school that collaborates closely with the University of Oslo and is co-owned by industrial partners.

The scientific computing research at Simula spans from development of generic software tools, via numerical methods, to a variety of applications in science and technology. The field of scientific computing is characterized by being interdisciplinary, involving mathematics, computer science, and the relevant application field(s).

In this talk I will briefly present the Simula organization and the principles on which our research activities are based. Thereafter, I will give an overview of the ongoing projects in scientific computing. In particular, I will discuss our research on computational middleware, simulation of electromechanical behavior of the heart, use of inverse modeling for determination of myocardial infarction, biomedical flow simulations, and different applications in geoscience. These applications range from simulation of the deformation in sedimentary basins to 4D modeling of the lithosphere. The research in computational geosciences is financed by one of the leading technological innovators in the oil and gas business, StatoilHydro.

We discuss the Lattice Boltzmann method (LBM), which provides a new way of thinking on complex, multiphase fluid flow problems. We present the basics of the LBM as well as its origins in the Cellular Automata. The particle-based nature of the LBM makes it well suited to study fluid-boundary interfaces. We then commence to sketch advanced applications like particle suspension problems and we show how the LBM can be applied on turbidity currents, a challenging fluid flow problem. We show that the LBM has a good deal of versatility in modeling physical systems.