The Electro-Magnetic (EM) method is a newly developed technology aiming at heavy oil and in-situ bitumen production in an environmentally friendly way with smallest possible surface footprint. This paper outlines the process of the development of the new EM technology for heavy oil and in-situ bitumen production. It documents the step-by-step approach following the roadmap from the white-paper technology analysis to the commissioning and performing of field tests. A reservoir simulation example is provided for in-situ production with EM-heating. The current status of the technology is R&D; pilot operation is expected from 2012/13 on.

This paper deals with the application of neural networks and evolutionary techniques to the area of process identification and control. A distillation process is simulated with the dynamic flow-sheet simulator DIVA which employs a first-principle-based model. Several neural paradigms were implemented to adaptively model the concentration dynamics. A combined PI-Neural Net controller for concentration control is presented. Using genetic algorithms it was possible to optimize the network structure and reduce the size of the training set. Finally, some parallelization methods for neural and evolutionary algorithms, implemented on Connection Machine architectures, are briefly explained.

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Fehlertoleranzen der einzelnen MeBwerte nicht moglich, deutet dies auf z. B. schadhafte MeBeinrichtungen hin. Als Arbeitsmittcl zum Bilanzabgleich

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