  Deep neural networks can generate more accurate shale gas production
forecasts in counties with a limited number of sample wells by utilizing
transfer learning. This paper provides a way of transferring the knowledge
gained from other deep neural network models trained on adjacent counties into
the county of interest. The paper uses data from more than 6000 shale gas wells
across 17 counties from Texas Barnett and Pennsylvania Marcellus shale
formations to test the capabilities of transfer learning. The results reduce
the forecasting error between 11% and 47% compared to the widely used Arps
decline curve model.
