TY - GEN
T1 - Sonic Waves Travel-time Prediction
T2 - 2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022
AU - Wong, W. K.
AU - Nuwara, Yohanes
AU - Juwono, Filbert H.
AU - Motalebi, Foad
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Sonic wave travel-time prediction is an important task in oil and gas exploration as it provides important information on the content and lithography of the rocks. Travel-time data, however, are not always accessible due to practical considerations. Currently, machine learning methods have been used to infer these values. In this paper, we look at the application of machine learning in predicting sonic wave travel-time, specifically in terms of challenges, benchmarks, and datasets. In addition, we present some preliminary results of sonic wave travel-time prediction using existing machine learning regression methods, namely curve fitting artificial neural network and multiple linear regression. Finally, this paper is aimed to act as a 'bridge' between machine learning practitioners and domain-specific oil and gas engineers.
AB - Sonic wave travel-time prediction is an important task in oil and gas exploration as it provides important information on the content and lithography of the rocks. Travel-time data, however, are not always accessible due to practical considerations. Currently, machine learning methods have been used to infer these values. In this paper, we look at the application of machine learning in predicting sonic wave travel-time, specifically in terms of challenges, benchmarks, and datasets. In addition, we present some preliminary results of sonic wave travel-time prediction using existing machine learning regression methods, namely curve fitting artificial neural network and multiple linear regression. Finally, this paper is aimed to act as a 'bridge' between machine learning practitioners and domain-specific oil and gas engineers.
KW - ANN
KW - MLR
KW - Sonic wave
KW - lithography
UR - http://www.scopus.com/inward/record.url?scp=85147021908&partnerID=8YFLogxK
U2 - 10.1109/GECOST55694.2022.10010361
DO - 10.1109/GECOST55694.2022.10010361
M3 - Conference Proceeding
AN - SCOPUS:85147021908
T3 - 2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022
SP - 159
EP - 163
BT - 2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 October 2022 through 28 October 2022
ER -