Distributed Optimization and Control of Offshore Oil Production: The Intelligent Platform

Distributed Optimization and Control of Offshore Oil Production: The Intelligent Platform

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Abstract

We describe a novel approach to distributed optimization and control of offshore oil production systems. The model incorporates a complex pipeline network. Oil and gas production systems are represented as a network of connected hierarchical structures of sub sea wells, manifolds and clusters. We consider multiphase flow of water, gas, and oil in the pipelines, and account for discrete switching and typical inflow characteristics of the sub sea wells. Network methods based on variational calculus provide a modeling framework for decentralized optimization and control. Conservation laws and the second law of thermodynamics combined with the passivity theory of nonlinear control lead to conditions for stability and optimality. We describe interconnections in networks through matrix representations that capture a network's topology. Control strategies are derived from the model, and stability and convergence to the optimal solution follows from the passivity conditions. The proposed distributed controller network can be seen as a special case of a Multi Agent System (MAS).
Content

We describe a novel approach to distributed optimization and control of offshore oil production systems. The model incorporates a complex pipeline network. Oil and gas production systems are represented as a network of connected hierarchical structures of sub sea wells, manifolds and clusters. We consider multiphase flow of water, gas, and oil in the pipelines, and account for discrete switching and typical inflow characteristics of the sub sea wells. Network methods based on variational calculus provide a modeling framework for decentralized optimization and control. Conservation laws and the second law of thermodynamics combined with the passivity theory of nonlinear control lead to conditions for stability and optimality. We describe interconnections in networks through matrix representations that capture a network's topology. Control strategies are derived from the model, and stability and convergence to the optimal solution follows from the passivity conditions. The proposed distributed controller network can be seen as a special case of a Multi Agent System (MAS).

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