Machine Learning Applications in Subsurface Energy Resource Management (eBook, PDF)

State of the Art and Future Prognosis
Redaktion: Mishra, Srikanta

• Format: PDF

Produktbeschreibung

Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for machine learning applications in subsurface energy resource management (e.g., oil and gas, geologic carbon sequestration, geothermal energy).

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 378
• Erscheinungstermin: 27. Dezember 2022
• Englisch
• ISBN-13: 9781000823875
• Artikelnr.: 66653122

Autorenporträt

Dr. Srikanta Mishra is Senior Research Leader and Technical Director for Geo-energy Resource Modeling and Analytics at Battelle Memorial Institute, the world's largest independent contract R&D organization. He is nationally and internationally recognized for his expertise in developing and communicating physics-based and data-driven predictive models for subsurface resource management. Dr. Mishra currently serves as the Technical Lead of the SMART (Science Informed Machine Learning for Accelerating Real-time Decisions for Subsurface applications) initiative, organized by the US Department of Energy and involving multiple national laboratories and universities. He was a recipient of the Society of Petroleum Engineers (SPE) Distinguished Member Award in 2021, and also served as a Global Distinguished Lecturer on Big Data Analytics for SPE during 2018-19 and received the 2022 SPE Data Science and Engineering Analytics Award.

Inhaltsangabe

Section I: Introduction
1. Machine Learning Applications in Subsurface Energy Resource Management: State of the Art
2. Solving Problems with Data Science
Section II: Reservoir Characterization Applications
3. Machine Learning-Aided Characterization Using Geophysical Data Modalities
4. Machine Learning to Discover
Characterize
and Produce Geothermal Energy
Section III: Drilling Operations Applications
5. Real-Time Drilling and Completion Analytics: From Cloud Computing to Edge Computing and Their Machine Learning Applications
6. Using Machine Learning to Improve Drilling of Unconventional Resources
Section IV: Production Data Analysis Applications
7. Machine Learning Assisted Production Data Filtering and Decline Curve Analysis in Unconventional Plays
8. Hybrid Data-Driven and Physics-Informed Reservoir Modeling for Unconventional Reservoirs
9. Role of Analytics in Extracting Data-Driven Models from Reservoir Surveillance
10. Machine Learning Assisted Forecasting of Reservoir Performance
Section V: Reservoir Modeling Applications
11. An Efficient Deep Learning Based Workflow Incorporating a Reduced Physics Model for Drainage Volume Visualization in Unconventional Reservoirs
12. Reservoir Modeling Using Fast Predictive Machine Learning Algorithms for Geological Carbon Storage
13. Physics-Embedded Machine Learning for Modeling and Optimization of Mature Fields
14. Deep Neural Network Surrogate Flow Models for History Matching and Uncertainty Quantification
15. Generalizable Field Development Optimization Using Deep Reinforcement Learning with Field Examples
Section VI: Predictive Maintenance Applications
16. Case Studies Involving Machine Learning for Predictive Maintenance in Oil and Gas Production Operations
17. Machine Learning for Multiphase Flow Metering
Section VII: Summary and Future Outlook
18. Machine Learning Applications in Subsurface Energy Resource Management: Future Prognosis