Abstract

Constructing reliable data-driven models to predict well production performance (eg, estimated ultimate recovery, cumulative production, production curves, etc.) for unconventional reservoirs requires large amounts of data. However, when considering unconventional reservoirs in their early stages of development, where data and the wells drilled are limited, one may benefit from leveraging available data and/or pretrained models from other more developed fields. Transfer learning, the process of storing knowledge gained while solving one problem (source data) and applying it to solve a different but related problem (target data), provides a workflow for alleviating data needs in training a data-driven model in fields with limited data. However, a pitfall in the application of transfer learning is the possibility of negative transfer, that is, transferring incorrect or irrelevant knowledge to the target data. In particular, the …

Metadata

publication

SPE Journal 28 (05), 2385-2414, 2023

year

2023

publication date

2023/10/11

authors

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

link

https://onepetro.org/SJ/article/28/05/2385/519406

journal

SPE Journal

volume

28

issue

05

pages

2385-2414

publisher

OnePetro