Publications
Transfer learning with recurrent neural networks for long-term production forecasting in unconventional reservoirs
Abstract
Robust production forecasting allows for optimal resource recovery through efficient field management strategies. In hydraulically fractured unconventional reservoirs, the physics of fluid flow and transport processes is not well understood and the presence of and transitions between multiple flow regimes further complicate forecasting. An important goal for field operators is to obtain a fast and reliable forecast with minimal historical production data. The abundance of wells drilled in fractured tight formations and continuous data acquisition effort motivate the use of data-driven forecast methods. However, traditional data-driven forecast methods require sufficient training data from an extended period of production for any target well, which may have limited practical use when the effective production life of wells is relatively short.
- Date
- August 11, 2022
- Authors
- Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
- Journal
- Spe Journal
- Volume
- 27
- Issue
- 04
- Pages
- 2425-2442
- Publisher
- OnePetro