Vinsamlegast notið þetta auðkenni þegar þið vitnið til verksins eða tengið í það: http://hdl.handle.net/1946/28752
Lumped parameter models have been shown to be a useful tool for geothermal reservoir analysis and production planning. Tank models are a common form of lumped parameter models, incorporating tanks of given capacitance partially filled with fluid. Between the tanks are connections with given conductance, that allow fluid to flow between connected tanks with different fluid levels. In this thesis, we analyze how tank models of varying complexity compare in terms of accuracy and utility. An algorithm called Complexity Reduction Algorithm (CRA) is developed that automatically finds those models that are likely to be the best by choosing a certain path through the model space. Since in general, it is reasonable to expect that a complex model is able to give an accurate fitting result and the optimum model indicated by CRA only has a medium complexity, a switch-back method is developed to decrease the training error of
the complex models.
In addition, in some cases, there is a large number ofproduction wells that are producing hot water,
which will lead to a situation where very many parameters needed to be estimated, since the number of parameters grows quadratically in terms of the number of tanks. The K-means clustering algorithm is shown to be suitable for finding an initial production tank configuration
under such situations.
Real data from the Laugarnes geothermal field and Reykir geothermal area in Iceland is shown in the thesis. The results show that the newly developed algorithm provides insights into model selection for lumped parameter models. The accuracy of both history-matched and predicted drawdown for lumped parameter models of varying complexity and the results by using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as an indicator for model selection have been shown.
Athugsemd: The full text of the thesis. The title is Complexity Analysis of Lumped Parameter models:Development of Complexity Reduction Algorithm