Abstract

Neural network predictive models are popular for production forecasting in unconventional reservoirs. They have the ability to learn complex input-output mapping between well properties and observed production responses from the large amount of data collected in the field. Additionally, the flow behavior in hydraulically fractured unconventional reservoirs is not well understood making such statistical models practical. Variants of neural networks have been proposed for production prediction in unconventional reservoirs, offering predictive capability of varying levels of granularity, accuracy and robustness against noisy and incomplete data. Neural network predictive models that incorporate physical understanding are especially useful for subsurface systems as they provide physically sound predictions.
In this work, we propose a new Dynamic Physics-Guided Deep Learning (DPGDL) model that …

Date

May 15, 2023

Authors

Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour

Conference

SPE Western Regional Meeting

Pages

D021S004R002

Publisher

SPE