Abstract

Transfer learning is a machine learning concept whereby the knowledge gained (e.g., a model developed) in one task can be transferred (applied) to solve a different but related task. In the context of unconventional reservoirs, the concept can be used to transfer a machine learning model that is learned from data in one field (or shale play) to another, thereby significantly reducing the data needs and efforts to build a new model from scratch. In this work, we study the feasibility of developing deep learning models that can capture and transfer common features in a rich dataset pertaining to a mature unconventional play to enable production prediction in a new unconventional play with limited available data. The focus in this work is on method development using simulated data that correspond to the Bakken and Eagle Ford Shale Plays as two different unconventional plays in the US. We use formation and …

Metadata

publication

SPE Middle East Oil and Gas Show and Conference, D031S031R007, 2021

year

2021

publication date

2021/12/15

authors

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

link

https://onepetro.org/SPEMEOS/proceedings-abstract/21MEOS/3-21MEOS/D031S031R007/474435

conference

SPE Middle East Oil and Gas Show and Conference

pages

D031S031R007

publisher

SPE