This diploma thesis deals with inverse pendulum control. There are described types of inverse pendulums, used power electronics, sensors and their connection to the MF624 control card and to external hardware. Further is described the identification of parameters, including the assembly of a custom algorithm for identification of viscous friction in the pendulum rotary coupling. For identification of moments of inertia are used 3D CAD models, where the thesis describes also use of these models for VRML visualization. The thesis also describes how to build a dynamic model by using Matlab-Simulink and the Simscape-Multibody toolbox. This model is further used in design of the controller using LQR and its simulation testing. The controller is complemented by swing-up algorithm, security elements and MF624 driver interface is designed. The control is tested on a real inverse pendulum assembly and implemented in external hardware.

In terms of a considerable increase the quality of characterization of tight-gas reservoirs, the aim of the present thesis was (i) an accurate representation of specific conditions in a reservoir simulation model, induced after the hydraulic fracturing or as a result of the underbalanced drilling procedure and (ii) performing the history match on a basis of real field data to calibrate the generated model by identifying the main model parameters and to investigate the different physical mechanisms, e.g. multiphase flow phenomena, affecting the well production performance. Due to the complexity of hydrocarbon reservoirs and the simplified nature of the numerical model, the study of the inverse problems in the stochastic framework provides capabilities using diagnostic statistics to quantify a quality of calibration and reliability of parameter estimates. As shown in the present thesis the statistical criteria for model selection may help the modelers to determine an appropriate level of parameterization and one would like to have as good an approximation of structure of the system as the information permits.

“History matching” (model calibration, parameter identification) is an established method for determination of representative reservoir properties such as permeability, porosity, relative permeability and fault transmissibility from a measured production history; however the uniqueness of selected model is always a challenge in a successful history matching. Up to now, the uniqueness of history matching results in practice can be assessed only after individual and technical experience and/or by repeating history matching with different reservoir models (different sets of parameters as the starting guess). The present study has been used the stochastical theory of Kullback & Leibler (K-L) and its further development by Akaike (AIC) for the first time to solve the uniqueness problem in reservoir engineering. In addition - based on the AIC principle and the principle of parsimony - a penalty term for OF has been empirically formulated regarding geoscientific and technical considerations. Finally a new formulation (Penalized Objective Function, POF) has been developed for model selection in reservoir history matching and has been tested successfully in a North German gas field. / „History Matching“ (Modell-Kalibrierung, Parameter Identifikation) ist eine bewährte Methode zur Bestimmung repräsentativer Reservoireigenschaften, wie Permeabilität, Porosität, relative Permeabilitätsfunktionen und Störungs-Transmissibilitäten aus einer gemessenen Produktionsgeschichte (history). Bis heute kann die Eindeutigkeit der identifizierten Parameter in der Praxis nicht konstruktiv nachgewiesen werden. Die Resultate eines History-Match können nur nach individueller Erfahrung und/oder durch vielmalige History-Match-Versuche mit verschiedenen Reservoirmodellen (verschiedenen Parametersätzen als Startposition) auf ihre Eindeutigkeit bewertet werden. Die vorliegende Studie hat die im Reservoir Engineering erstmals eingesetzte stochastische Theorie von Kullback & Leibler (K-L) und ihre Weiterentwicklung nach Akaike (AIC) als Basis für die Bewertung des Eindeutigkeitsproblems genutzt. Schließlich wurde das AIC-Prinzip als empirischer Strafterm aus geowissenschaftlichen und technischen Überlegungen formuliert. Der neu formulierte Strafterm (Penalized Objective Function, POF) wurde für das History Matching eines norddeutschen Erdgasfeldes erfolgreich getestet.

In terms of a considerable increase the quality of characterization of tight-gas reservoirs, the aim of the present thesis was (i) an accurate representation of specific conditions in a reservoir simulation model, induced after the hydraulic fracturing or as a result of the underbalanced drilling procedure and (ii) performing the history match on a basis of real field data to calibrate the generated model by identifying the main model parameters and to investigate the different physical mechanisms, e.g. multiphase flow phenomena, affecting the well production performance. Due to the complexity of hydrocarbon reservoirs and the simplified nature of the numerical model, the study of the inverse problems in the stochastic framework provides capabilities using diagnostic statistics to quantify a quality of calibration and reliability of parameter estimates. As shown in the present thesis the statistical criteria for model selection may help the modelers to determine an appropriate level of parameterization and one would like to have as good an approximation of structure of the system as the information permits.

“History matching” (model calibration, parameter identification) is an established method for determination of representative reservoir properties such as permeability, porosity, relative permeability and fault transmissibility from a measured production history; however the uniqueness of selected model is always a challenge in a successful history matching. Up to now, the uniqueness of history matching results in practice can be assessed only after individual and technical experience and/or by repeating history matching with different reservoir models (different sets of parameters as the starting guess). The present study has been used the stochastical theory of Kullback & Leibler (K-L) and its further development by Akaike (AIC) for the first time to solve the uniqueness problem in reservoir engineering. In addition - based on the AIC principle and the principle of parsimony - a penalty term for OF has been empirically formulated regarding geoscientific and technical considerations. Finally a new formulation (Penalized Objective Function, POF) has been developed for model selection in reservoir history matching and has been tested successfully in a North German gas field. / „History Matching“ (Modell-Kalibrierung, Parameter Identifikation) ist eine bewährte Methode zur Bestimmung repräsentativer Reservoireigenschaften, wie Permeabilität, Porosität, relative Permeabilitätsfunktionen und Störungs-Transmissibilitäten aus einer gemessenen Produktionsgeschichte (history). Bis heute kann die Eindeutigkeit der identifizierten Parameter in der Praxis nicht konstruktiv nachgewiesen werden. Die Resultate eines History-Match können nur nach individueller Erfahrung und/oder durch vielmalige History-Match-Versuche mit verschiedenen Reservoirmodellen (verschiedenen Parametersätzen als Startposition) auf ihre Eindeutigkeit bewertet werden. Die vorliegende Studie hat die im Reservoir Engineering erstmals eingesetzte stochastische Theorie von Kullback & Leibler (K-L) und ihre Weiterentwicklung nach Akaike (AIC) als Basis für die Bewertung des Eindeutigkeitsproblems genutzt. Schließlich wurde das AIC-Prinzip als empirischer Strafterm aus geowissenschaftlichen und technischen Überlegungen formuliert. Der neu formulierte Strafterm (Penalized Objective Function, POF) wurde für das History Matching eines norddeutschen Erdgasfeldes erfolgreich getestet.