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Proceedings Papers

Deep Learning Models for Methane Emissions Identification and Quantification

Ismot Jahan, Mohamed Mehana, Bulbul Ahmmed, Javier E. Santos, Daniel O'Malley, Hari Viswanathan
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3866049-MS
https://doi.org/10.15530/urtec-2023-3866049
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View Paper titled, Deep Learning Models for Methane Emissions Identification and Quantification
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Proceedings Papers

Machine Learning Facilitates Prediction of Geomechanical Properties Directly from SEM Images in Unconventional Plays

Heehwan Yang, Deepak Devegowda, Mark Curtis, Chandra Rai
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3866084-MS
https://doi.org/10.15530/urtec-2023-3866084
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View Paper titled, Machine Learning Facilitates Prediction of Geomechanical Properties Directly from SEM Images in Unconventional Plays
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Proceedings Papers

Rock Thin-Section Analysis and Mineral Detection Utilizing Deep Learning Approach

Fatick Nath, Sarker Asish, Shaon Sutradhar, Zhiyang Li, Nazmul Shahadat, Happy R. Debi, S. M. Shamsul Hoque
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3865660-MS
https://doi.org/10.15530/urtec-2023-3865660
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View Paper titled, Rock Thin-Section Analysis and Mineral Detection Utilizing Deep Learning Approach
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Proceedings Papers

Auto-Identification and Real-Time Warning Method of Multiple Type Events During Multistage Horizontal Well Fracturing

Mingze Zhao, Yue Li, Yuyang Liu, Bin Yuan, Siwei Meng, Wei Zhang, He Liu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3863378-MS
https://doi.org/10.15530/urtec-2023-3863378
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View Paper titled, Auto-Identification and Real-Time Warning Method of Multiple Type Events During Multistage Horizontal Well Fracturing
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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
https://doi.org/10.15530/urtec-2023-3863926
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View Paper titled, Predicting Hydrocarbon Production Behavior in Heterogeneous Reservoir Utilizing Deep Learning Models
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Proceedings Papers

Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study from an Unconventional Shale Reservoir

Ted Holden, Ruth Kurian, Mohammed Ibrahim, Daniel Hampson, Jonathan Downton
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862247-MS
https://doi.org/10.15530/urtec-2023-3862247
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View Paper titled, Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study from an Unconventional Shale Reservoir
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Proceedings Papers

Modeling of Fiber-Optic Strain Response When Pumping Stops to Verify Potential Continuation of Fracture Extension

Kildare George Ramos Gurjao, Eduardo Gildin, Richard Gibson, Mark Everett
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3870194-MS
https://doi.org/10.15530/urtec-2023-3870194
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View Paper titled, Modeling of Fiber-Optic Strain Response When Pumping Stops to Verify Potential Continuation of Fracture Extension
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Proceedings Papers

Optimization Method for Fracture-Network Design Under Transient and Pseudosteady Condition Using UFD Technique and Deep Learning Approach

Junlei Wang, Yunsheng Wei, Yuewei Pan, Wei Yu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3853784-MS
https://doi.org/10.15530/urtec-2023-3853784
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View Paper titled, Optimization Method for Fracture-Network Design Under Transient and Pseudosteady Condition Using UFD Technique and Deep Learning Approach
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Proceedings Papers

Application of Deep Learning Neural Networks to Petrophysical Property Prediction: Case Study from the Powder River Basin

Cameron Snow
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3702980-MS
https://doi.org/10.15530/urtec-2022-3702980
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View Paper titled, Application of Deep Learning Neural Networks to Petrophysical Property Prediction: Case Study from the Powder River Basin
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Proceedings Papers

Prediction and Analysis of Geomechanical Properties using Deep Learning: A Permian Basin Case Study

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
https://doi.org/10.15530/urtec-2022-3707202
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View Paper titled, Prediction and Analysis of Geomechanical Properties using Deep Learning: A Permian Basin Case Study
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Proceedings Papers

Embedding Physical Flow Functions into Deep Learning Predictive Models for Improved Production Forecasting

Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3702606-MS
https://doi.org/10.15530/urtec-2022-3702606
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View Paper titled, Embedding Physical Flow Functions into Deep Learning Predictive Models for Improved Production Forecasting
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Proceedings Papers

Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs

Hamid Rahmanifard, Ian Gates, Abdolmohsen Shabib Asl
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3703284-MS
https://doi.org/10.15530/urtec-2022-3703284
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View Paper titled, Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs
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Proceedings Papers

Sequence-to-Sequence (Seq2Seq) Long Short-Term Memory (LSTM) for Oil Production Forecast of Shale Reservoirs

Cristhian Aranguren, Alfonso Fragoso, Roberto Aguilera
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722179-MS
https://doi.org/10.15530/urtec-2022-3722179
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View Paper titled, Sequence-to-Sequence (Seq2Seq) Long Short-Term Memory (LSTM) for Oil Production Forecast of Shale Reservoirs
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Proceedings Papers

Viable Solutions to Overcome Weaknesses of Deep Learning Applications in Production Forecasting: A Comprehensive Review

Y. Kocoglu, S. Gorell
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3721904-MS
https://doi.org/10.15530/urtec-2022-3721904
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View Paper titled, Viable Solutions to Overcome Weaknesses of Deep Learning Applications in Production Forecasting: A Comprehensive Review
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Proceedings Papers

Multi-Level of Fracture Network Imaging: A HFTS Use Case and Knowledge Transferring

Guoxiang Liu, Abhash Kumar, Song Zhao, Chung Yan Shih, Veronika Vasylkivska, Paul Holcomb, Richard Hammack, Jeffery Ilconich, Grant Bromhal
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723466-MS
https://doi.org/10.15530/urtec-2022-3723466
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View Paper titled, Multi-Level of Fracture Network Imaging: A HFTS Use Case and Knowledge Transferring
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Proceedings Papers

Forecasting Well-to-Well Interference in the Permian Basin by Applying Neural Networks on Pre-Run Simulations

Yogashri Pradhan, W. John Lee, Duc Lam, Kyle Hanna, Thuy Nguyen, Andrew (Quang) Tran
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723394-MS
https://doi.org/10.15530/urtec-2022-3723394
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View Paper titled, Forecasting Well-to-Well Interference in the Permian Basin by Applying Neural Networks on Pre-Run Simulations
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Proceedings Papers

Machine Learning Applications for Reservoir Pressure Trend Prediction in Potash Area of Delaware Basin

Olabode Thomas Ajibola, James Sheng, Phillip McElroy, Ebru Unal
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722944-MS
https://doi.org/10.15530/urtec-2022-3722944
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View Paper titled, Machine Learning Applications for Reservoir Pressure Trend Prediction in Potash Area of Delaware Basin
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Proceedings Papers

Earthquake Detection and Focal Mechanism Calculation Using Artificial Intelligence

Shane Quimby, Yanwei Zhao, Jie Zhang
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723688-MS
https://doi.org/10.15530/urtec-2022-3723688
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View Paper titled, Earthquake Detection and Focal Mechanism Calculation Using Artificial Intelligence
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Proceedings Papers

A Deep Learning Framework for Multi-Horizon Probabilistic Production Forecasting in Unconventional Reservoirs

Rohan Thavarajah, Hamed Darabi, Nick Ruta, Amir Salehi, Amir Kianinejad
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723682-MS
https://doi.org/10.15530/urtec-2022-3723682
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View Paper titled, A Deep Learning Framework for Multi-Horizon Probabilistic Production Forecasting in Unconventional Reservoirs
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Proceedings Papers

Improved UCR Development Decision through Probabilistic Modeling with Convolutional Neural Network

Han Young Park, Yunhui Tan, Baosheng Liang, Yuguang Chen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723793-MS
https://doi.org/10.15530/urtec-2022-3723793
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View Paper titled, Improved UCR Development Decision through Probabilistic Modeling with Convolutional Neural Network
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