Abstract

Forecasting the future performance of unconventional wells is essential for reserves estimation, production optimization and to support decision-making. In this paper, we present a physics-guided deep learning predictive model that forecasts production profiles given an input of completion, formation, and fluid properties. The predictive model accounts for prediction errors originating from potentially missing physical phenomena, simulation inputs, and incomplete reservoir description by correcting the production profiles obtained from a physical simulator. Using the multi-task learning concept, the predictive model creates a shared representation that can also be used to forecast production behavior, estimate reserves, and as a diagnostic tool to understand well performance under varying well properties. Compared to purely data-driven predictive models, it also allows for improved prediction for when well properties …

Date

2021

Authors

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

Book

Unconventional Resources Technology Conference, 26–28 July 2021

Pages

1843-1850

Publisher

Unconventional Resources Technology Conference (URTeC)