Abstract

Developing a reliable deep learning model for new unconventional reservoirs, is often constrained by the limited number of wells available. Transfer learning is a useful approach to alleviate data needs in training a neural network. This involves storing knowledge gained while solving one problem (source data) and applying it to a different but related problem (target data). However, the transfer of incorrect knowledge can impede the performance of the model trained for the new field. Furthermore, the black-box nature of such networks makes it difficult to interpret the contribution of the multiple source models used for knowledge transfer. Hence, ranking their viability for transfer can help with avoiding negative transfer and improving the predictive performance in the target dataset. After identifying the appropriate network, we propose a neural network structure to combine multiple source models into one network to be …

Metadata

publication

Unconventional Resources Technology Conference, 20–22 June 2022, 2192-2211, 2022

year

2022

publication date

2022/10/10

authors

J Cornelio, S Mohd Razak, Y Cho, HH Liu, R Vaidya, B Jafarpour

link

https://library.seg.org/doi/abs/10.15530/urtec-2022-3723965

book

Unconventional Resources Technology Conference, 20–22 June 2022

pages

2192-2211

publisher

Unconventional Resources Technology Conference (URTeC)