1st Edition

Machine Learning Applications in Subsurface Energy Resource Management State of the Art and Future Prognosis

Edited By Srikanta Mishra Copyright 2023
    378 Pages 169 B/W Illustrations
    by CRC Press

    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.

    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


    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.