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Goal-Oriented Intelligence in Optimization of Distributed Parameter Systems

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Conference paper

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Models of complex systems can be differentiated by their ability to reproduce or generate system behavior, by their prediction power, by their robustness, or, conversely, by their sensitivity to inputs and parameters by their uncertainty if captured and by their intelligence. Even the term prediction is not unique. First, a first-principle physically based distributed parameter model could be an excellent predictor if a it captures the main system behavior, and b its parameters and inputs are known accurately otherwise, it would fail, possibly drastically. Second, predictive power depends on the data, on the goal, and on the time scale. For example, scheduling of pumping and injection in an oil field for maximum profit over the next 5 years or pumping from a contaminated aquifer in order to maintain certain low concentration at a compliance point for the next 20 years, vs. prediction of plume migration in groundwater towards a nearby river, over time in each case, the model has a slightly different expected function, as well as different intelligence type. The paper reviews the recent developments in subsurface fluid flow management such as optimization of oil production and groundwater remediation both sharing similar practices, though for different purposes as a continuous struggle to increase intelligence by a adapting new tools such as artificial intelligence and dynamic stochastic control b attempting to integrate these tools and c reducing uncertainty. Although the systems discussed seem specific to the mathematical geosciences specifically to oil reservoirs and contaminated aquifers, and although these systems are very different from man-made machines, similar rigid structure and reliance on differential-integral calculus, as well as the serial processing, knowledge evolution, and uncertainty propagation from one discipline to the next exist in most science and engineering fields, and so does the need for a paradigm shift.

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  • Operations Research

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