Towards Better Shale Gas Production Forecasting Using Transfer Learning

TL;DR

This paper demonstrates that transfer learning with deep neural networks significantly improves shale gas production forecasting accuracy in data-scarce regions, outperforming traditional decline curve models.

Contribution

It introduces a transfer learning approach for shale gas forecasting, leveraging models trained on neighboring counties to enhance predictions in target areas.

Findings

Forecasting error reduced by up to 47%

Transfer learning outperforms traditional models

Effective across multiple shale formations

Abstract

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.