Machine Learning and Data Science in the Oil and Gas Industry
Best Practices, Tools, and Case Studies
- 1st Edition - March 4, 2021
- Editor: Patrick Bangert
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 0 7 1 4 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 0 9 1 4 - 1
Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will l… Read more
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Request a sales quoteMachine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.
- Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful
- Gain practical understanding of machine learning used in oil and gas operations through contributed case studies
- Learn change management skills that will help gain confidence in pursuing the technology
- Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)
Oil and gas industry expert and practitioner working either in exploration, drilling, completions, engineering, production, maintenance or management
- Cover
- Dedication
- Title page
- Contents
- Copyright
- Contributors
- Foreword
- Chapter 1: Introduction
- Abstract
- 1.1. Who this book is for
- 1.2. Preview of the content
- 1.3. Oil and gas industry overview
- 1.4. Brief history of oil exploration
- 1.5 Oil and gas as limited resources
- 1.6. Challenges of oil and gas
- Chapter 2: Data Science, Statistics, and Time-Series
- Abstract
- 2.1. Measurement, uncertainty, and record keeping
- 2.2. Correlation and timescales
- 2.3. The idea of a model
- 2.4. First principles models
- 2.5. The straight line
- 2.6. Representation and significance
- 2.7. Outlier detection
- 2.8. Residuals and statistical distributions
- 2.9. Feature engineering
- 2.10. Principal component analysis
- 2.11. Practical advice
- Chapter 3: Machine Learning
- Abstract
- 3.1. Basic ideas of machine learning
- 3.2. Bias-variance complexity trade-off
- 3.3. Model types
- 3.4. Training and assessing a model
- 3.5. How good is my model?
- 3.6. Role of domain knowledge
- 3.7. Optimization using a model
- 3.8. Practical advice
- Chapter 4: Introduction to Machine Learning in the Oil and Gas Industry
- Abstract
- 4.1. Forecasting
- 4.2. Predictive maintenance
- 4.3. Production
- 4.4. Modeling physical relationships
- 4.5. Optimization and advanced process control
- 4.6. Other applications
- Chapter 5: Data Management from the DCS to the Historian
- Abstract
- 5.1. Introduction
- 5.2. Sensor data
- 5.3. Time series data
- 5.4. How sensor data is transmitted by field networks
- 5.5. How control systems manage data
- 5.6. Historians and information servers as a data source
- 5.7. Data visualization of time series data—HMI (human machine interface)
- 5.8. Data management for equipment and facilities
- 5.9. Simulators, process modeling, and operating training systems
- 5.10. How to get data out of the field/plant and to your analytics platform
- 5.11. Conclusion: do you know if your data is correct?
- Chapter 6: Getting the Most Across the Value Chain
- Abstract
- 6.1. Thinking outside the box
- 6.2. Costing a project
- 6.3. Valuing a project
- 6.4. The business case
- 6.5. Growing markets, optimizing networks
- 6.6. Integrated strategy and alignment
- 6.7. Case studies: capturing market opportunities
- 6.8. Digital platform: partner, acquire, or build?
- 6.9. What success looks like
- Chapter 7: Project Management for a Machine Learning Project
- Abstract
- 7.1. Classical project management in oil & gas-a (short) primer
- 7.2. Agile-the mindset
- 7.3. Scrum-the framework
- 7.4. Project execution-from pilot to product
- 7.5. Management of change and culture
- 7.6. Scaling-from pilot to product
- Chapter 8: The Business of AI Adoption
- Abstract
- 8.1. Defining artificial intelligence
- 8.2. AI impacts on oil and gas
- 8.3. The adoption challenge
- 8.4. The problem of trustf
- 8.5. Digital leaders lead
- 8.6. Overcoming barriers to scaling up
- 8.7. Confronting front line change
- 8.8. Doing digital change
- Chapter 9: Global Practice of AI and Big Data in Oil and Gas Industry
- Abstract
- 9.1. Introduction
- 9.2. Integrate digital rock physics with AI to optimize oil recovery
- 9.3. The molecular level advance planning system for refining
- 9.4. The application of big data in the oil refining process
- 9.5. Equipment management based on AI
- Chapter 10: Soft Sensors for NOx Emissions
- Abstract
- 10.1. Introduction to soft sensing
- 10.2. NOx and SOx emissions
- 10.3. Combined heat and power (CHP)
- 10.4. Soft sensing and machine learning
- 10.5. Setting up a soft sensor
- 10.6. Assessing the model
- 10.7. Conclusion
- Chapter 11: Detecting Electric Submersible Pump Failures
- Abstract
- 11.1. Introduction
- 11.2. ESP data analytics
- 11.3. Principal Component Analysis
- 11.4. PCA diagnostic model
- 11.5. Case study: diagnosis of the ESP broken shaft
- 11.6. Conclusions
- Chapter 12: Predictive and Diagnostic Maintenance for Rod Pumps
- Abstract
- 12.1. Introduction
- 12.2. Feature engineering
- 12.3. Project method to validate our model
- 12.4. Results
- Chapter 13: Forecasting Slugging in Gas Lift Wells
- Abstract
- 13.1. Introduction
- 13.2. Methodology
- 13.3. Focus projects
- 13.4. Data structure
- 13.5. Outlook
- 13.6. Conclusion
- Index
- No. of pages: 306
- Language: English
- Edition: 1
- Published: March 4, 2021
- Imprint: Gulf Professional Publishing
- Paperback ISBN: 9780128207147
- eBook ISBN: 9780128209141
PB
Patrick Bangert
Dr. Patrick Bangert is the Vice President of Artificial Intelligence at Samsung SDS where he leads both the AI software development and AI consulting groups that each provide various offerings to the industry. He is the founder and Board Chair of Algorithmica Technologies, providing real-time process modeling, optimization, and predictive maintenance solutions to the process industry with a focus on chemistry and power generation. His doctorate from UCL specialized in applied mathematics, and his academic positions at NASA’s Jet Propulsion Laboratory and Los Alamos National Laboratory made use of optimization and machine learning for magnetohydrodynamics and particle accelerator experiments. He has published extensively across optimization and machine learning and their relevant applications in the real world.
Affiliations and expertise
Vice President of Artificial Intelligence at Samsung SDSA, San Jose, CA, United States, and Founder and Board Chair of Algorithmica Technologies GmbH, Bad Nauheim, GermanyRead Machine Learning and Data Science in the Oil and Gas Industry on ScienceDirect