Multivariate regression model from water level and production rate time series 
for the geothermal reservoir Waiwera (New Zealand)

Kühn, M.; Schöne, Tim

doi:10.1016/j.egypro.2017.08.196

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Multivariate regression model from water level and production rate time series for the geothermal reservoir Waiwera (New Zealand)

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Kühn, M.
3.4 Fluid Systems Modelling, 3.0 Geochemistry, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/schoene

Schöne, Tim
3.4 Fluid Systems Modelling, 3.0 Geochemistry, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Kühn, M., Schöne, T. (2017): Multivariate regression model from water level and production rate time series for the geothermal reservoir Waiwera (New Zealand). - Energy Procedia, 125, 571-579.
https://doi.org/10.1016/j.egypro.2017.08.196

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_2638917

Abstract

Water management tools are necessary to guarantee the preservation of natural resources while ensuring optimum utilization. Linear regression models are a simple and quick solution for creating prognostic capabilities. Multivariate models show higher precision than univariate models. In the case of Waiwera, implementation of individual production rates is more accurate than applying just the total production rate. A maximum of approximately 1,075 m3/day can be pumped to ensure a water level of at least 0.5 m a.s.l. in the monitoring well. The model should be renewed annually to implement new data and current water level trends to keep the quality.