1. Introduction to Digital Oilfield
- Provides an introduction to:
- What is digitalization
- Purpose and motivation of digitalization
- Other digital industries
- Digital oilfield
- This course starts with an introduction to oil and gas field development phases and activities and the transition of this industry to digital solutions with the ever-increasing volume of gathered data and their processing requirements.
- Digitalization, driven from technology innovation, is also discussed in the context of overall step changes to an industry and how technology change also impacts people and process and can be impacted and impact regulations (governance).
- This introduction to digital oilfield also previews and explains the main concepts that are covered in the detailed courses including big data and data management, high-performance computing, artificial intelligence, instrumentation and controls, computer security and communications, and software design for digital oilfields.
- In setting the context and providing perspective on digitalization, detailed examples and case studies that will be used to illustrate and for hands-on examples are introduced covering the major offshore operations (drilling, reservoir surveillance/production optimization, marine operations, environmental monitoring, asset integrity/facility management, and safety & risk).
2. Data Management for Digital Oilfield
- Provides an overview of data gathering, management, and analysis techniques with a focus on digital oilfield.
- The main principles and applications of databases, data analytics, and data mining are covered.
- Digital oilfield makes use of sensors, data storage (big data), and ubiquitous communication. It requires acquiring, storing, and using real-time data to make operating and business decisions to:
- Increase production efficiency
- Reduce downtime
- Implement field-wide efficiencies
- Optimize reservoir recovery and management.
3. High Performance Computing for Digital Oilfield
- Provides an overview of the key principles of HPC and their application in digital oilfield.
- Topics include:
- Computer architecture
- Parallel processing principles: synchronization and communication
- Cloud computing
- Oilfield operations generate huge amounts of data and require real time processing of the data. HPC can significantly reduce the computational time required for data processing.
- Applications include:
- Seismic data processing and velocity modelling
- Reservoir simulations
- Multiphase flow modelling
- Remote monitoring
- Dynamic positioning
4. Artificial Intelligence for Digital Oilfield
- Explores some of the key AI concepts and their application in digital oilfield, including:
- Knowledge representation
- Neural networks
- Fuzzy logic
- AI is used to increase the accuracy of predictions to near-cognitive robotic comprehension in machine learning.
- AI provides collaborative workflows with intrinsic analysis capabilities to make better decisions and optimize processes.
5. Instrumentation and Controls for Digital Oilfield
- Focuses on the instrumentation and control systems used in oil and gas operations, both on the surface and downhole.
- Topics include:
- Measurement and sensors
- Fundamental mathematics required in the design of control systems
- Control system tuning
- Hands-on examples such as control valves on wells, pumps, pipes, chokes, and compressors are provided and their application to automated and remote operations is discussed.
6. Computer Security and Communications for Digital Oilfield
- Provides an overview of a wide range of topics related to different forms of wired and wireless communication systems.
- Includes discussions of network security and cyber risks and methods to mitigate these risks.
- Communication requirements for Digital Oilfield are increasing with the growth of connected sensors, instrumentation, and control systems for remote operations and monitoring.
7. Software Design for Digital Oilfield
- Introduces the key topics of software design including program structure, algorithms and data structures, program complexity, and program verification. Applications include modeling, simulation, prediction, and optimization of a technical or business process.