EXEN - CE - Faculty of Engineering

This module offers an introduction to a variety of unsupervised and supervised methods of data processing. Learn different architecture configurations for predictive modeling, kernel methods, neural networks, and techniques for evaluation of model performance. You'll bring real-world problems from your own workplace, and use machine learning to solve them. With access to the state-of-the-art resources in the Faculty of Engineering, and leading researchers in the area, your learning will be hands-on and practical with application to industry. Prerequisite: Restricted to students admitted into the Certificate for Artificial Intelligence

Dive into Deep Learning methodology and begin to build neural networks. The module will cover subjects such as convolutional neural networks and their applications to images; recurrent network models for processing natural language and speech. It will also introduce networks representing probability distributions, in particular Bayesian and Markov networks, and their applications. Co-requisite: EXEN 2451

An introduction to principles of reinforcement learning that include algorithms supporting action decision processes that optimize long-term performance. Topics include: dynamic programming, Q-learning, Monte Carlo reinforcement learning, and efficient algorithms for single- and multi-agent planning. Co-requisite: EXEN 2452

The theory of optimization of various drilling operational parameters for minimum cost drilling operation (more specifically use of physics based data driven models, mechanical specific energy concept, technical limit of drilling rate concepts will be discussed). This course will also address the design concepts of drilling hydraulics and drillstring mechanics, as well as, the design concepts of drilling directional, long horizontal, and extended reach wells. We will consider modern drilling technologies such as underbalanced drilling and managed pressure drilling.

The purpose of the course is to introduce the fundamentals of the nodal analysis approach and its applications in modeling and optimization of the oil/gas production process. The course content includes how to obtain the inflow performance relationship, how to model the single-phase and multiphase flow in wells, how to model the single-phase and multiphase flow through restrictions, how to identify the weak components in the production system, how to improve/optimize the production system based on the nodal analysis results, and how to design artificial lift methods.

This is a design course covering new developments in the area of well engineering. The course is designed for participants to develop an understanding of the basic principles of oil and gas well completion and stimulation engineering design, specifically: elements of a well completion design, well planning, casing design, cementing design, tubing design, perforating, sand control, and hydraulic fracturing.

Learn the practical aspects of reservoir engineering. The course covers reservoir engineering principles, different methods to assess the field performances and methods to develop different types of fields.

You will learn the fundamental properties of reservoir rocks and reservoir fluids from both experimental and theoretical perspectives. The experimental techniques used to measure these properties will be explained in detail. Relevant theories/models used to describe/correlate these properties will be covered. The course will also touch on the challenges and opportunities associated with the characterization of rock and fluid properties in tight/shale reservoirs.

Develop your understanding of basic principles of open-hole well logging and formation evaluation tools. You will learn conventional, reconnaissance, and graphical, open hole log interpretation techniques as well as techniques of evaluating shaly formations and gas bearing formations, and techniques of evaluating unconventional shale oil/shale gas reservoirs.

This course is designed to provide an extensive coverage of enhanced oil recovery methods. After covering the theory of displacement processes in porous media (waterflooding in specific), class exercises will be given. We will discuss improved waterflooding using chemicals. Field examples of different types of applications, as well as a pilot design will be provided. The course also covers new EOR technologies including the use of nano-materials.

This course will cover, extensively, the elements of thermal recovery techniques for heavy oil recovery. We will review the basics and laboratory scale understanding of steam and air injection techniques. You will learn how to select the right techniques for different reservoir types and geological environments using field case examples. Performance prediction techniques will be discussed as well.

This course will introduce the fundamental mechanisms of, and recent developments in the various techniques used for stimulating and recovering unconventional oil and gas resources. We will consider unconventional reservoirs such as tight gas reservoirs, tight oil reservoirs, shale gas reservoirs, and shale oil reservoirs. The following recovery techniques will be covered: primary recovery techniques, multistage hydraulic fracturing stimulations, gas injection methods, and chemical injection methods. There will be a special focus on CO2 injection methods, which can be used to enhance hydrocarbon recovery as well as permanently sequester CO2 in the depleted reservoirs. Examples will be provided to demonstrate how to apply these recovery techniques on a field scale