This course is designed for engineers and managers responsible for planning as well as optimizing existing operations. Specifically, those involved with drilling, reservoir, completions, and production in operating as well as service companies will find the course beneficial. Engineers working in newly founded data science teams in oil and gas companies will especially find inspiration from different case studies. Data science engineers will also find the distinction between models and a framework of integration with existing workflows greatly beneficial.
This Learning Level is set at: Intermediate to Advanced
This course provides attendees with a comprehensive methodology for well performance analysis with specific focus on unconventional oil and gas. The approach combines the use of several powerful techniques and will illustrate the practical aspects of production data analysis.
Data driven modeling is becoming a key differentiation to unlock higher recoveries from existing fields as well as identify new opportunities. The availability of data and democratization of these advanced algorithms is changing the landscape of subsurface workflows – helping create as well as improve existing ones. We are in an exciting phase in the industry where access as well as ease of using these advanced tools is transforming decision making in organizations.
In this course, we will start by introducing advanced analytical tools and techniques - machine learning and data mining algorithms used to identify of trends and patterns in any given dataset and predict future trends. We will showcase how each of these tools and techniques have been successfully applied to subsurface data - formation evaluation data, well testing data, reservoir data as well as data from surface facilities. We shall also present case studies of how integration of this seemingly disparate data can be done through new workflows that help identify opportunities to increase recovery. Finally, we will draw important distinctions between the more traditionally used forward models (physics-based approach such as reservoir simulation) and these statistics-based models. Using a case study that demonstrates integration of these two approaches, we shall conclude by a drawing out a framework for integration of these tools in your existing workflows.
In summary, this course looks at successful application of machine learning and data analytics in E&P industry in the last several years. We will start with fundamentals of data mining algorithms, machine learning algorithms (neural networks, decision tree analysis) and present their successful implementation on subsurface data. The course is devoted to field application of these tools and techniques with focus on production optimization and optimization of water/gas injection operations.
We have been collecting large amounts of subsurface data in the E&P industry. The easy access to advanced analytical tools and techniques at great computational speeds has democratized data-driven modeling. The use of these tools and techniques presents a great competitive advantage as we seek to increase recovery and be more efficient as an industry. Take this course to understand how to apply these tools and techniques to subsurface data and equip yourself with skills that is transforming the E&P business in the coming years.