Data and Decision Analysis in Petroleum Industry

What are the Goals?

We aim to enhance participants’ understanding of decision making by focusing on data, methods, and behavioral aspects. We explore the role of intuitive versus analytical reasoning in decision-making. We will also discuss how sound approaches to decision-making can influence sustained development and innovation in the petroleum business.

At the end of this training course, you will:

• Understand the importance of good decision making in petroleum business
• Understand the relationship between data, uncertainty, and the concept of risk
• Gain skills in effective formulation of analytics
• Understand the human biases and their effect on decisions
• Learn about industry best practice and their real-world implementations

Who is this Training Course for?

This training course is useful for analysts, engineers, and managers involved in the data gathering, processing, interpretations, and supporting decisions. In short, all of us.

This training course is suitable to a wide range of professionals but will greatly benefit: 

• Technical staff interested in data and decision analysis
• Executives interested in gaining in-depth knowledge of analysis and how they support decisions
• Practitioners that work with data
• Researchers and practitioners who aim to broaden their knowledge of analytics and decision making

Daily Agenda

Introduction to Good Decision Making 

• Decision analysis
• Good decisions vs. good outcomes
• Information for effective decision making
• Introduction to data, statistics, and modern analytic methods
• Uncertainties: using probability to encode uncertainty

Effective Tools and Methods  

• Analytic tool: decision trees
• Analytic tool: statistical inference
• Analytic tool: regression analysis
• Using spreadsheets to solve real world oil and gas problems
• Practical aspects of data and decision analysis

Advance Topics 

• An introduction to optimization
• Linear Programming: Case Study, what is the best generator choice?
• Integer programming: Knapsack problem
• Bayesian analytics
• Value of Information: Case study, what is the value of seismic information?