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Proceedings Papers

Deep Learning-Driven Acceleration of Stochastic Gradient Methods for Well Location Optimization Under Uncertainty

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Esmail Eltahan, Kamy Sepehrnoori, Faruk Omer Alpak
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220754-MS
https://doi.org/10.2118/220754-MS
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View Paper titled, Deep Learning-Driven Acceleration of Stochastic Gradient Methods for Well Location Optimization Under Uncertainty
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Proceedings Papers

Real Time Application of Deep Learning Based Flare Smoke Detection

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Kratika Gupta, Purba Chatterjee, Xianzhong John Chen, Jitendra Kadam
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220857-MS
https://doi.org/10.2118/220857-MS
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View Paper titled, Real Time Application of Deep Learning Based Flare Smoke Detection
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Proceedings Papers

Estimating Petrophysical Properties Directly from Seismic: A Deep Learning Application to Carbonate Field for CO 2 Storage Potential

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C. L. Lew, M. I. Ahmad Fuad, M. S. Jaya, A. Trianto, C. MacBeth
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220847-MS
https://doi.org/10.2118/220847-MS
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View Paper titled, Estimating Petrophysical Properties Directly from Seismic: A Deep Learning Application to Carbonate Field for CO 2 Storage Potential
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Proceedings Papers

The Role of Personalized Generative AI in Advancing Petroleum Engineering and Energy Industry: A Roadmap to Secure and Cost-Efficient Knowledge Integration: A Case Study

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Amr Gharieb, Mohamed Adel Gabry, Mohamed Y. Soliman
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220716-MS
https://doi.org/10.2118/220716-MS
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View Paper titled, The Role of Personalized Generative AI in Advancing Petroleum Engineering and Energy Industry: A Roadmap to Secure and Cost-Efficient Knowledge Integration: A Case Study
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Proceedings Papers

Damage Classification of Oil & Gas Equipment Components Using Siamese Networks

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Aaradhy Sirothia, Gaurav Nair, Rohan Pandya
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220921-MS
https://doi.org/10.2118/220921-MS
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View Paper titled, Damage Classification of Oil & Gas Equipment Components Using Siamese Networks
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Proceedings Papers

Optimizing Image Logging Acquisition with Deep Learning

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C. Guimaraes, R. Piazza
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220840-MS
https://doi.org/10.2118/220840-MS
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View Paper titled, Optimizing Image Logging Acquisition with Deep Learning
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Proceedings Papers

Advancing Proxy Modeling in Reservoir Simulation: A Multi-Step Embed to Control Approach

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Jungang Chen, Eduardo Gidlin, John Killough
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220917-MS
https://doi.org/10.2118/220917-MS
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View Paper titled, Advancing Proxy Modeling in Reservoir Simulation: A Multi-Step Embed to Control Approach
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Proceedings Papers

Enhanced 3D Pore Segmentation and Multi-Model Pore-Scale Simulation by Deep Learning

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Haotian Li, Billal Aslam, Bicheng Yan
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220838-MS
https://doi.org/10.2118/220838-MS
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View Paper titled, Enhanced 3D Pore Segmentation and Multi-Model Pore-Scale Simulation by Deep Learning
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Proceedings Papers

Graph-Level Feature Embedding with Spatial– Temporal GCN Method for Interconnected Well Production Forecasting

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Ziming Xu, Juliana Y. Leung
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220790-MS
https://doi.org/10.2118/220790-MS
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View Paper titled, Graph-Level Feature Embedding with Spatial– Temporal GCN Method for Interconnected Well Production Forecasting
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Proceedings Papers

The U-Net Enhanced Graph Neural Network for Multiphase Flow Prediction: An Implication to Geological Carbon Sequestration

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Zeeshan Tariq, Hussein Hoteit, Shuyu Sun, Moataz Abualsaud, Xupeng He, Muhammad AlMajid, Bicheng Yan
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220757-MS
https://doi.org/10.2118/220757-MS
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View Paper titled, The U-Net Enhanced Graph Neural Network for Multiphase Flow Prediction: An Implication to Geological Carbon Sequestration
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Proceedings Papers

Embed-to-Control-Based Deep-Learning Surrogate for Robust Nonlinearly Constrained Life-Cycle Production Optimization: A Realistic Deepwater Application

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Quang Minh Nguyen, Mustafa Onur, Faruk Omer Alpak
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220783-MS
https://doi.org/10.2118/220783-MS
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View Paper titled, Embed-to-Control-Based Deep-Learning Surrogate for Robust Nonlinearly Constrained Life-Cycle Production Optimization: A Realistic Deepwater Application
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Proceedings Papers

Advancements in Production Optimization through an Innovative Hybrid Data-Physics Architecture

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Raya Matoorian, Mohammadreza Malaieri, Roman Shor, Roberto Aguilera
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220777-MS
https://doi.org/10.2118/220777-MS
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View Paper titled, Advancements in Production Optimization through an Innovative Hybrid Data-Physics Architecture
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Proceedings Papers

Improving Reliability of Seismic Stratigraphy Prediction: Integration of Uncertainty Quantification in Attention Mechanism Neural Network

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C. T. Ang, A. H. Elsheikh
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220708-MS
https://doi.org/10.2118/220708-MS
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View Paper titled, Improving Reliability of Seismic Stratigraphy Prediction: Integration of Uncertainty Quantification in Attention Mechanism Neural Network
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Proceedings Papers

Efficacy Gain From a Deep Neural Network-Based History-Matching Workflow

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Bicheng Yan, Yanhui Zhang
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220876-MS
https://doi.org/10.2118/220876-MS
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View Paper titled, Efficacy Gain From a Deep Neural Network-Based History-Matching Workflow
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Proceedings Papers

Transfer Learning in Subsurface Flow Surrogate Model with Physics-Guided Neural Network

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H. B. Cheng, J. H. Qiao, Y. C. Wei, S. C. Li, P. Zeng, H. B. Yu
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220978-MS
https://doi.org/10.2118/220978-MS
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View Paper titled, Transfer Learning in Subsurface Flow Surrogate Model with Physics-Guided Neural Network
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Proceedings Papers

Refined Diffusion Posterior Sampling for Seismic Denoising and Interpolation

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Tung Nguyen, Tung Pham, Minh Nguyen, Tuan Nguyen
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-221000-MS
https://doi.org/10.2118/221000-MS
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View Paper titled, Refined Diffusion Posterior Sampling for Seismic Denoising and Interpolation
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Proceedings Papers

Inertial Effect of Drill Pipe Friction Trend and Early Warning of Stuck Pipe

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Muchen Liu, Xianzhi Song, Zhaopeng Zhu, Gensheng Li, Hao Xiao, Li Fu, Tao Pan, Xintong Li, Yanlong Yang
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220991-MS
https://doi.org/10.2118/220991-MS
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View Paper titled, Inertial Effect of Drill Pipe Friction Trend and Early Warning of Stuck Pipe
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Proceedings Papers

ConGANergy: A Framework for Engineering Data Augmentation with Application to Solid Particle Erosion

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Jun Zhang, Yijie Li, Weiping Pei, Siamack Shirazi
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-220954-MS
https://doi.org/10.2118/220954-MS
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View Paper titled, ConGANergy: A Framework for Engineering Data Augmentation with Application to Solid Particle Erosion
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Proceedings Papers

Fast Evaluation of Reservoir Connectivity via a New Deep Learning Approach: Attention-Based Graph Neural Network for Fusion Model

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Tariq Saihood, Ahmed Saihood, Mohamed Adel Al-Shaher, Christine Ehlig-Economides, Zeinab Zargar
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-221029-MS
https://doi.org/10.2118/221029-MS
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View Paper titled, Fast Evaluation of Reservoir Connectivity via a New Deep Learning Approach: Attention-Based Graph Neural Network for Fusion Model
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Proceedings Papers

Physics Informed Machine Learning for Reservoir Connectivity Identification and Production Forecastingfor CO2-EOR

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Masahiro Nagao, Akhil Datta-Gupta
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-221057-MS
https://doi.org/10.2118/221057-MS
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View Paper titled, Physics Informed Machine Learning for Reservoir Connectivity Identification and Production Forecastingfor CO2-EOR
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