EGU2020-1536, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-1536
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Nonlinear autoregressive neural networks to predict fracturing fluid flow into shallow groundwater

Reza Taherdangkoo1, Alexandru Tatomir1,2, Mohammad Taherdangkoo3, and Martin Sauter1
Reza Taherdangkoo et al.
  • 1Universität Göttingen, Geowissenschaftliches Zentrum , Abt. Angewandte Geologie, Göttingen, Germany (reza.taherdangkoo@geo.uni-goettingen.de)
  • 2Department of Earth Sciences, Uppsala University, Villavägen 16, S-75236 Uppsala, Sweden
  • 3Department of Artificial Intelligence, Tehran Business School, Tehran, Iran

Hydraulic fracturing fluid migration from the deep subsurface along abandoned wells may pose contamination threats to shallow groundwater systems. This study investigates the application of a nonlinear autoregressive (NAR) neural network to predict leakage rates of fracturing fluid to a shallow aquifer in the presence of an abandoned well. The NAR network was trained using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms. The dataset employed in this study includes fracturing fluid leakage rates to the aquifer overlying the Posidonia shale formation in the North German Basin (Taherdangkoo et al. 2019). We evaluated the performance of developed models based on the mean squared errors (MSE) and coefficient of determination (R2). The results indicate the robustness and compatibility of NAR-LM and NAR-BR models in predicting fracturing fluid leakage to the aquifer. This study shows that NAR neural networks are useful and hold a considerable potential for assessing the potential groundwater impacts of unconventional gas development.

References

Taherdangkoo, R., Tatomir, A., Anighoro, T., & Sauter, M. (2019). Modeling fate and transport of hydraulic fracturing fluid in the presence of abandoned wells. Journal of Contaminant Hydrology, 221, 58–68. https://doi.org/10.1016/j.jconhyd.2018.12.003

How to cite: Taherdangkoo, R., Tatomir, A., Taherdangkoo, M., and Sauter, M.: Nonlinear autoregressive neural networks to predict fracturing fluid flow into shallow groundwater, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1536, https://doi.org/10.5194/egusphere-egu2020-1536, 2019