Energy Digitization

ICPE 602
Credits: 1.5 (1.5 Lecture Hours)

Application of geostatistical techniques to build reservoir models through integration of geological core/well log, seismic and production data to generate a consistent reservoir description; background and insights to geostatistical modeling techniques and situation where the application of geostatistics could add value.

Prerequisite: Graduate classification.

ICPE 615
Credits: 1.5 (1.5 Lecture Hours)

Fundamentals of electricity grid development; monitoring, control and protection; renewable generation; microgrids and grid integration; electricity markets; long term planning and associated risk, and grid robustness.

Prerequisite: Graduate classification.

ICPE 638
Special Topics in Energy
Credits: 1.5 (1.5 Lecture Hours)

Discussion of basic concepts and methods used in data science with an emphasis on applications in energy; topics include concepts of probability theory, probability distributions, statistical data modeling and inference, linear regression and predictive models, dimension reduction, introduction to machine learning and statistical modeling of dependent data.

Prerequisite: Graduate classification.

ICPE 689
Credits: 1.5 (1.5 Lecture Hours)

This is an introductory course discussing machine learning methods commonly seen in data science with an emphasis on applications in energy. Topics to be discussed include supervised and unsupervised learning, clustering, classification, predictive models, performance evaluation, neural networks, and reinforcement learning.

Prerequisite: Graduate classification.

ICPE 689
Credits: 1.5 (1.5 Lecture Hours)

Introduction to the “digital oil field” (DOF) and the incorporation of new practices to leverage new technologies in drilling, production, and reservoir management processes. Discusses state-of-the-art digital technologies applied in the context of reservoir exploration and production, instrumentation, workflows for automation in drilling, production, and reservoir management. Overview of artificial intelligence for the smart integration of production systems.

Prerequisite: Graduate classification.

ICPE 689
Credits: 1.5 (1.5 Lecture Hours)

Motivated by the expectations for the impacts that data science will have on science and engineering, the goal of this class is to enhance the fundamental understanding and applications of data science throughout the process systems engineering field, particularly with a focus on energy applications.

Prerequisite: Graduate classification.

ICPE 637
Credits: 1.5 (1.5 Lecture Hours)

Basic concepts and methods of data science with an emphasis on energy-related applications; discussion of probability theory, data-based statistical modeling and inference, linear and non-linear regression and predictive models, dimensionality reduction, introduction to machine learning and statistical modeling of dependent data. 

Prerequisite: Graduate classification.

ICPE 639
Credits: 1.5 (1.5 Lecture Hours)

Discussion of machine learning methods commonly seen in data science with an emphasis to applications in energy; topics include supervised and unsupervised learning, clustering, classification, predictive models, performance evaluation, neural networks and reinforcement learning. 

Prerequisite: Graduate classification.