Catálogo de publicaciones - libros

Flow in petroleum reservoirs occurs on a wide variety of physical scales. This poses a continuing challenge to modelling and simulation of reservoirs since fine-scale effects often have a profound impact on flow patterns on larger scales. Resolving all pertinent scales and their interaction is therefore imperative to give reliable qualitative and quantitative simulation results. To overcome the problem of multiple scales it is customary to use some kind of upscaling or homogenisation procedure, in which the reservoir properties are represented by some kind of averaged properties and the flow is solved on a coarse grid. Unfortunately, most upscaling techniques give reliable results only for a limited range of flow scenarios. Increased demands for reservoir simulation studies have therefore led researchers to develop more rigorous multiscale methods that incorporate subscale effects more directly. In the first part of the paper, we give an overview of some of the many scales that are considered important for flow simulations. Next, we present and discuss several upscaling approaches that have played a role in the history of reservoir simulation. In the final part, we present some more recent approaches for modelling scales in the flow simulations based upon the multiscale paradigm. We conclude with a discussion of benefits and disadvantages of using multiscale methods, rather than using traditional upscaling techniques, in reservoir simulation.

In the present chapter a short review is given of the mathematical formulation relevant for geophysical flow modelling, and in addition computational examples are shown for some specific flow cases. These examples are described in some detail in order to illustrate useful methods to handle such problems in practice. The emphasis is on more local geophysical flows, including stratified flow over variable topography.

Solving the Vehicle Routing Problem (VRP) is a key to efficiency in transportation and supply chain management. The VRP is an NP-hard problem that comes in many guises. The VRP literature contains thousands of papers, and VRP research is regarded as one of the great successes of OR. Vehicle routing decision support tools provide substantial savings in society every day, and an industry of routing tool vendors has emerged. Exact methods of today cannot consistently solve VRP instances with more than 50–100 customers in reasonable time, which is generally a small number in real-life applications. For industrial problem sizes, and if one aims at solving a variety of VRP variants, approximation methods is the only viable approach. There is still a need for VRP research, particularly for large-scale instances and complex, rich VRP variants. In this chapter, we give a brief general introduction to the VRP. We then describe how industrial requirements motivate extensions to the basic, rather idealized VRP models that have received most attention in the research community, and how such extensions can be made. At SINTEF Applied Mathematics, industrial variants of the VRP have been studied since 1995. Our efforts have led to the development of a generic VRP solver that has been commercialized through a spin-off company. We give a description of the underlying, rich VRP model and the selected uniform algorithmic approach, which is based on metaheuristics. Finally, results from computational experiments are presented. In conclusion, we point to important issues in further VRP research.

The Long-Term Forest Treatment Scheduling Problem (LTFTSP) is the task of allocating treatments in a forest such that both sustainability and economic outcome is maximized. Solving such problems is demanded in more and more countries and the task is increasingly more complex because one must adhere to local legislation, environmental issues, and public interests. To be able to handle most aspects of the LTFTSP with adjacency constraints (which is the problem we solve), a rich, spatial model which is parameterized, is required. We present a model defined on discrete land units and time points, where the treatments to perform are parameterized. Many of the most commonly used criteria in the form of constraints and objective components in long-term forestry scheduling are included. Such criteria may be defined for the complete forest region in question, or for specific sub-regions. The complexity of the model requires a robust solution method. We have selected a heuristic approach based on Tabu Search. An initial solution is constructed by composition of economically optimal schedules for each land unit. This solution is made feasible by a greedy heuristic. The initial solution is iteratively improved by Tabu Search. Two different types of move are used in the Tabu Search procedure: Shifting a treatment to another time point, and exchanging one treatment program for another treatment program. The solution method is implemented in the software tool Ecoplan. Empirical results have been produced for a 1,541 stand case from Norway. The results show that when more than one objective is included in the objective function, the quality of the solution with respect to the individual objectives may be considerably reduced. Some of the quality loss, especially with regards to the “old forest” objective component may be explained by the initial state of the forest.

This paper discusses segmentation and reconstruction problems using a integer linear programming approach. These problems have important applications in remote sensing, medical image analysis and industrial inspection. We focus on methods that produce optimal or near-optimal solutions for the corresponding optimization problems. We show that for the two problems one may use similar ideas in both modeling and solution methods. These methods are based on Lagrangian decomposition and dynamic programming for certain subproblems (associated with lines in the image). Some computational experiences are also reported.

During the last half decade, the metal industry has been in a harsh situation seeing their profit margins squeezed to an extreme. Many companies have been forced to close down furnaces and plants. To help a major metal producing company manage this process, we developed a strategic mixed integer programming model. The main decisions addressed by the model involve the future plant structure and production capacities, the production portfolio at each plant and the by-product production. Here, we present the underlying MIP-model and give computational results. In addition, we show how the valuable by-product production can have impact on the optimal supply chain design.

In this paper we give an introduction to modelling the natural gas value chain including production, transportation, processing, contracts, and markets. The presentation gives insight in the complexity of planning in the natural gas supply chain and how optimization can help decision makers in a natural gas company coordinate the different activities. We present an integrated view from the perspective of an upstream company. The paper starts with decribing how to model natural gas transportation and storage, and at the end we present a stochastic portfolio optimization model for the natural gas value chain in a liberalized market.