Abstract

The physics of fluid flow and transport processes in hydraulically fractured unconventional reservoirs are not well understood. As a result, the predicted production behavior using conventional simulation often does not agree with the observed field performance data. The discrepancy is caused by potential errors in the simulation model and the physical processes that take place in complex fractured rocks subjected to hydraulic fracturing. Additionally, other field data such as well logs and drilling parameters containing important information about reservoir condition and reservoir characteristics are not conveniently integrated into existing simulation models. In this paper, we discuss the development of a deep learning model to learn the errors in simulation-based performance prediction in unconventional reservoirs. Once trained, the model is expected to forecast the performance response of a well by …

Metadata

publication

SPE Middle East Oil and Gas Show and Conference, D031S031R003, 2021

year

2021

publication date

2021/12/15

authors

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

link

https://onepetro.org/SPEMEOS/proceedings-abstract/21MEOS/3-21MEOS/D031S031R003/474664

conference

SPE Middle East Oil and Gas Show and Conference

pages

D031S031R003

publisher

SPE