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Keywords: bi-lstm
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Proceedings Papers
Predicting Hydrocarbon Production Behavior in Heterogeneous Reservoir Utilizing Deep Learning Models
Fatick Nath, Sarker Asish, Happy R. Debi, Mohammed Omar S. Chowdhury, Zackary J. Zamora, Sergio Muñoz
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3863926-MS
... using DL models. Among three wells, LSTM and Bi-LSTM perform relatively closer in three counties with non-linear production history and reported as Frio with (R 2 ± 84%, Error ± 9), La Salle ( R 2 ± 97%, Error ± 4) and Zavala (R 2 ± 91%, Error ± 5). Among all tested models, Bi-LSTM showed superior...
Proceedings Papers
Fatick Nath, Karina Murillo, Sarker Monojit Asish, Deepak Ganta, Valeria Limon, Edgardo Aguirre, Gabriel Aguirre, Happy R. Debi, Jose L. Perez, Cesar Netro, Flavio Borjas
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3707202-MS
... learning and deep neural network to estimate and predict the geomechanical properties of the Permian Basin. The log-derived prediction algorithm includes (a) Single-Well prediction, 75% of log data of a single well is used as a specimen for training the Bi-LSTM, and the rest 25% of data of the same well...
Proceedings Papers
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5418-MS
... to explore the possibility of using this technology in the petroleum industry to automate production forecasting to save time and cost where traditional methods may fail. In this paper, a deep Bidirectional Long Short Term Memory (Bi-LSTM) neural network was used to increase the accuracy of future production...