Physical Modelling for Systems and Control: Lecture Notes Course sc4032, 2009-2010

In these notes the formulation of models is aimed at obtaining a description of the dynamic behaviour of processes under transient conditions. This implies that we will formulate the equations of motion of the process variables that describe the evolution of the process as a function of time. Our models will formulate the process dynamics in a form as required for the understanding of process operations such as startup and shutdown, or for studying the transitions from one operating condition to another one as, e.g., required by grade changes in a production plant or by changes in the composition of the feedstock. Process dynamic models also are of great importance for providing control engineers with qualitative and quantitative descriptions of the transient behaviour of processes that are to be used in model based control system design.Delft Center for Systems and Control (DCSC)Mechanical, Maritime and Materials Engineerin

Selected topics in identification, modelling and control: Progress report on research activities in the Mechanical Engineering Systems and Control Group. Vol. 7

Mechanical, Maritime and Materials Engineerin

Selected topics in identification, modelling and control: Progress report on research activities in the Mechanical Engineering Systems and Control Group. Vol. 8

Mechanical, Maritime and Materials Engineerin

Selected topics in identification, modelling and control: Progress report on research activities in the mechanical engineering systems and control group

Mechanical, Maritime and Materials Engineerin

Selected topics in identification, modelling and control: Progress report on research activities in the Mechanical Engineering Systems and Control Group.. Vol. 9

Mechanical, Maritime and Materials Engineerin

Controllability and observability in two-phase porous media flow

Reservoir simulation models are frequently used to make decisions on well locations, recovery optimization strategies etc. The success of these applications is, among other aspects, determined by the controllability and observability properties of the reservoir model. In this paper it is shown how the controllability and observability of two-phase flow reservoir models can be analyzed and quantified with aid of generalized empirical Gramians. The empirical controllability Gramian can be interpreted as a spatial covariance of the states (pressures or saturations) in the reservoir resulting from input perturbations in the wells. The empirical observability Gramian can be interpreted as a spatial covariance of the measured bottom hole pressures or well bore flow rates resulting from state perturbations. Based on examples in the form of simple homogeneous and heterogeneous reservoir models we conclude that the position of the wells and of the front between reservoir fluids, and to a lesser extent the position and shape of permeability heterogeneities that impact the front, are the most important factors that determine the local controllability and observability properties of the reservoir.Geoscience and EngineeringCivil Engineering and Geoscience

Model-Based Control and Optimization of Large Scale Physical Systems - Challenges in Reservoir Engineering

Due to urgent needs to increase efficiency in oil recovery from subsurface reservoirs new technology is developed that allows more detailed sensing and actuation of multiphase flow properties in oil reservoirs. One of the examples is the controlled injection of water through injection wells with the purpose to displace the oil in an appropriate direction. This technology enables the application of model-based optimization and control techniques to optimize production over the entire production period of a reservoir, which can be around 25 years. Large scale reservoir flow models are used for optimizing production settings, but suffer from high levels of uncertainty and limited validation options. One of the challenges is the development of reduced complexity models that deliver accurate long-term predictions, and at the same time are not more complex than can be warranted by the amount of data that is available. In this paper an overview will be given of the problems and opportunities for model-based control and optimization in this field aiming at the development of a closed-loop reservoir management system.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin

Controllability, observability and identifiability in single-phase porous media flow

Over the past few years, more and more systems and control concepts have been applied in reservoir engineering, such as optimal control, Kalman filtering, and model reduction. The success of these applications is determined by the controllability, observability, and identifiability properties of the reservoir at hand. The first contribution of this paper is to analyze and interpret the controllability and observability of single-phase flow reservoir models and to investigate how these are affected by well locations, heterogeneity, and fluid properties. The second contribution of this paper is to show how to compute an upper bound on the number of identifiable parameters when history matching production data and to present a new method to regularize the history matching problem using a reservoir’s controllability and observability properties.Petroleum EngineeringCivil Engineering and Geoscience