# Towards Better Shale Gas Production Forecasting Using Transfer Learning

arXiv: https://arxiv.org/abs/2106.11051 · HTML: https://mlsanity.com/paper/2106.11051/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)

## Built on by (most cited first, 3 of 3)

- [Self-supervised similarity models based on well-logging data](https://mlsanity.com/paper/2209.12444/) (2022)
- [Parallel Automatic History Matching Algorithm Using Reinforcement Learning](https://mlsanity.com/paper/2211.07434/) (2022)
- [Uncertainty-Driven Modeling of Microporosity and Permeability in Clastic Reservoirs Using Random Forest](https://mlsanity.com/paper/2503.16957/) (2025)

## Abstract (arXiv)

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.