Contents
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
2.1. Measurement, uncertainty, and record keeping
2.2. Correlation and timescales
2.6. Representation and significance
2.8. Residuals and statistical distributions
2.10. Principal component analysis
3.1. Basic ideas of machine learning
3.2. Bias-variance complexity trade-off
3.4. Training and assessing a model
3.7. Optimization using a model
Chapter 4: Introduction to Machine Learning in the Oil and Gas Industry
4.4. Modeling physical relationships
4.5. Optimization and advanced process control
Chapter 5: Data Management from the DCS to the Historian
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
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?
Chapter 7: Project Management for a Machine Learning Project
7.1. Classical project management in oil & gas-a (short) primer
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
8.1. Defining artificial intelligence
8.2. AI impacts on oil and gas
8.6. Overcoming barriers to scaling up
8.7. Confronting front line change
Chapter 9: Global Practice of AI and Big Data in Oil and Gas Industry
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
10.1. Introduction to soft sensing
10.3. Combined heat and power (CHP)
10.4. Soft sensing and machine learning
10.5. Setting up a soft sensor
Chapter 11: Detecting Electric Submersible Pump Failures
11.3. Principal Component Analysis
11.5. Case study: diagnosis of the ESP broken shaft
Chapter 12: Predictive and Diagnostic Maintenance for Rod Pumps
12.3. Project method to validate our model