Rose, ChristopherPasquier, PhilippeNguyen, AlainBeaudry, GabrielleIGSHPA Research Track (2022)2022-12-042022-12-042022https://hdl.handle.net/20.500.14446/336843Monitoring of well's specific capacity is commonly used to plan maintenance of injection wells in open-loop GSHP and standing column well systems. However, this method does not consider the effect of temperature on hydraulic conductivity. A first step towards an alternative approach that does include the effect of temperature is proposed in this work. We present a long short-term memory network capable of predicting the water level in the injection well of an operating GSHP system. The methodology consists of building a training set using a numerical model. A total of 500 simulations were conducted to evaluate hydraulic head signals under various inlet temperatures and flow rates along with hydraulic and thermal parameters drawn from a uniform distribution. Predictive performance of the artificial neural network is tested on an operational data set. The resulting RMSE between the forecasted and operational data set is 14.8 cm.application/pdfIn the Oklahoma State University Library's institutional repository this paper is made available through the open access principles and the terms of agreement/consent between the author(s) and the publisher. The permission policy on the use, reproduction or distribution of the article falls under fair use for educational, scholarship, and research purposes. Contact Digital Resources and Discovery Services at lib-dls@okstate.edu or 405-744-9161 for further information.10.22488/okstate.22.000024Conference proceedings