Description
The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy).
- Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance)
- Offers a variety of perspectives from authors representing operating companies, universities, and research organizations
- Provides an array of case studies illustrating the latest applications of several ML techniques
- Includes a literature review and future outlook for each application domain
This book is targeted at practicing petroleum engineers or geoscientists interested in developing a broad understanding of ML applications across several subsurface domains. It is also aimed as a supplementary reading for graduate-level courses and will also appeal to professionals and researchers working with hydrogeology and nuclear waste disposal.
Table of Contents
Section I: Introduction
1. Machine Learning Applications in Subsurface Energy Resource Management: State of the Art
Srikanta Mishra
2. Solving Problems with Data Science
Jared Schuetter
Section II: Reservoir Characterization Applications
3. Machine Learning-Aided Characterization Using Geophysical Data Modalities
Vikram Jayaram and Tao Zhao
4. Machine Learning to Discover, Characterize, and Produce Geothermal Energy
V. Vesselinov, M. Mudunuru, B. Ahmmed, S. Karra, and D. O’Malley
Section III: Drilling Operations Applications
5. Real-Time Drilling and Completion Analytics: From Cloud Computing to Edge Computing and Their Machine Learning Applications
Dingzhou Cao, Jingshuang Xue, and Yu Sun
6. Using Machine Learning to Improve Drilling of Unconventional Resources
Ruizhi Zhong
Section IV: Production Data Analysis Applications
7. Machine Learning Assisted Production Data Filtering and Decline Curve Analysis in Unconventional Plays
David Fulford
8. Hybrid Data-Driven and Physics-Informed Reservoir Modeling for Unconventional Reservoirs
Sathish Sankaran and Hardik Zalavadia
9. Role of Analytics in Extracting Data-Driven Models from Reservoir Surveillance
Raj Banerjee
10. Machine Learning Assisted Forecasting of Reservoir Performance
Emre Artun
Section V: Reservoir Modeling Applications
11. An Efficient Deep Learning Based Workflow Incorporating a Reduced Physics Model for Drainage Volume Visualization in Unconventional Reservoirs
Tsubasa Onishi, Hongquan Chen, Akhil Datta-Gupta, and Srikanta Mishra
12. Reservoir Modeling Using Fast Predictive Machine Learning Algorithms for Geological Carbon Storage
Seyyed Hosseini, Richard Larson, Parisa Shokouhi, Vikas Kumar, Sumedha Prathipati, Dan Kifer, Jonathan Garcez, Luis Ayala, Michael Riedl, Brandon Hill, Sanjay Tamrakar, Jared Schuetter, and Srikanta Mishra
13. Physics-Embedded Machine Learning for Modeling and Optimization of Mature Fields
Pallav Sarma
14. Deep Neural Network Surrogate Flow Models for History Matching and Uncertainty Quantification
Su Jiang and Louis Durlofsky
15. Generalizable Field Development Optimization Using Deep Reinforcement Learning with Field Examples
Jincong He, Yusuf Nasir, and Shusei Tanaka
Section VI: Predictive Maintenance Applications
16. Case Studies Involving Machine Learning for Predictive Maintenance in Oil and Gas Production Operations
Luigi Saputelli, Carlos Palacios, and Cesar Bravo
17. Machine Learning for Multiphase Flow Metering
Patrick Bangert
Section VII: Summary and Future Outlook
18. Machine Learning Applications in Subsurface Energy Resource Management: Future Prognosis
Srikanta Mishra
