Online Live Streaming Course

4 Sessions (approx 3 hrs/each) : November 25 to 28 , 2024

Duration : 12 hours

Course Description:

In the rapidly evolving landscape of oil and gas production, the ability to predict well performance accurately is not just an advantage—it’s a necessity. This 12-hour master-class offers a deep dive into the cutting-edge methods that are transforming the oil and gas industry.  This course is ideal for professionals who are looking to leverage data science to enhance performance prediction and optimization strategies. By the end of the course, participants will confidently apply data science techniques to optimize performance and make informed decisions in their respective roles within the industry.

Learning Outcomes:

• Understand various tools and techniques used in reservoir characterization.
• Identify key challenges in forecasting production from unconventional reservoirs.
• Explore AI applications and benefits in managing reservoir assets.
• Assess real-world applications of data-driven reservoir management.
• Understand the practical implications and results of ML-based optimization.
• Understand ethical issues and considerations in the application of data science.
• Gain insights into the future direction of predictive modeling.

Course Objectives:

1. To Equip Participants with Advanced Data Science Skills: Enhance the ability to apply data science and machine learning techniques to predict well performance.
2. To Foster a Deep Understanding of Complex Reservoirs including challenges and techniques for characterizing and managing complex reservoirs.
3. To Provide Hands-On Experience with Predictive Modeling: Offer practical experience in building and applying predictive models for reservoir performance.
4. To Explore the Ethical Aspects of Data Science including considerations in the application of data science in the oil and gas industry.
5. To Prepare Participants for Future Developments in reservoir characterization and performance prediction.

Who Should Attend? This course is tailored for mid to advanced-level professionals in the oil and gas sector, including reservoir engineers, data analysts, and production engineers.

Prerequisites: Participants are expected to have a mid-level to advanced career experience in the oil and gas industry and should have a foundational understanding of reservoir engineering, data analytics, machine learning and basic programming skills, particularly in Python.

Format: Online course with hands-on exercises to apply theoretical knowledge and interactive discussion of real-world case studies.

Course Outline:

Day 1: Setting the Scene: Advanced Interpretation Techniques for Complex Reservoirs – 3hrs

• Overview of tools and techniques for reservoir characterization and production forecasting. (30 min)
  • Challenges in forecasting production from unconventional reservoirs. (30 min)

     Coffee Break (15 min)

• Defining artificial intelligence, machine learning and deep learning (30 min)
  • Role of artificial intelligence in modern asset management.  (30 min)
  • Case Study 1: Benefits and challenges of data driven models  (30 min)

Day 2: Fundamental Machine Learning and Exploratory Data Analysis – 3hrs

• Fundamental machine learning algorithms and their applications in reservoir management (60 min)
  • ML in Python: Fundamental methods and libraries (30 min)

       Coffee Break (15 min)

• Practical Example: Data preparation and exploratory Data Analysis (EDA) (75 min)

Day 3: Predictive Modeling for Reservoir Performance  – 3hrs

•  Practical Example: Prediction of well performance using advanced data science methods. (75 min)

       Coffee Break (15 min)

• Measures of uncertainty and reliability in the model’s predictions: Confidence and predictive intervals. (45 min)
  • Model benchmarking and optimization metrics (45 min)

Day 4: Challenges, Ethical Considerations and Future Developments – 3hrs

• Case Study 2: Production optimization using ML (60min)

      Coffee Break (15 min)

• Ethical considerations in data science  (30 min)
  • Future developments in prediction of well performance (30 min)
  • Wrap up (30 min)

Target Audience (expanded version):

• Experienced Reservoir Engineers seeking to integrate data science into their reservoir management strategies.
• Data Analysts and Technologists in the Energy Sector aiming to apply data science for optimization of oil and gas production.
• Experienced Production Engineers seeking to enhance well performance and recovery rates through data-driven decisions.
• Technical Managers interested in data science for resource management.

Prerequisites (expanded version):

1. Foundational Knowledge in Reservoir Engineering including basic reservoir dynamics, fluid properties, and well operations.
2. Familiarity with data analysis concepts and methodologies.
3. Knowledge of statistical tools and techniques commonly used in data analysis.
4. Proficiency in Programming: Ability to write and understand code, preferably in Python, as it will be used for data analytics during the course.
5. A general understanding of machine learning concepts and their applications.
6. Mathematical Competency: Comfort with mathematical concepts such as algebra, calculus, and probability, which underpin data science techniques.