Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

30508 Completed Projects

2882
AB
5105
BC
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projects by Category

Enhanced Techniques for History Matching and Forecasting of Petroleum Reservoir Data – Year Two

History matching refers to calibrating numerical or analytical models by the observed data. However, this task can be very challenging in presence of complex geology and/or many unknown data .
The purpose of this project is to introduce and apply the new techniques for efficient creation of predictive history-matched models for reservoir characterization of conventional and unconventional reservoirs, which can be used for probabilistic forecast and uncertainty quantification. It is expected to implement as set of code and introduce new workflows that can enhance the history matching task in various problems. This include the use and applications of the state-of-the-arts methods that can represent the geology and can efficiently and accurately calibrate the dynamic models by minimizing the computational cost.
This postdoctoral program provides a unique opportunity to further my studies in history matching and uncertainty quantification to a new level within Rock Flow Dynamics (RFD). This project helps me utilize interactions with industry and receive industrial feedback on the practicality of my algorithms.

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Faculty Supervisor:

Mario Costa Sousa

Student:

Partner:

Rock Flow Dynamics Inc;University of Calgary

Discipline:

Earth science

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Elevate

Enhanced Techniques for History Matching and Forecasting of Petroleum Reservoir Data

History matching refers to calibrating numerical or analytical models by the observed data. However, this task can be very challenging in presence of complex geology and/or many unknown data .
The purpose of this project is to introduce and apply the new techniques for efficient creation of predictive history-matched models for reservoir characterization of conventional and unconventional reservoirs, which can be used for probabilistic forecast and uncertainty quantification. It is expected to implement as set of code and introduce new workflows that can enhance the history matching task in various problems. This include the use and applications of the state-of-the-arts methods that can represent the geology and can efficiently and accurately calibrate the dynamic models by minimizing the computational cost.
This postdoctoral program provides a unique opportunity to further my studies in history matching and uncertainty quantification to a new level within Rock Flow Dynamics (RFD). This project helps me utilize interactions with industry and receive industrial feedback on the practicality of my algorithms.

View Full Project Description
Faculty Supervisor:

Mario Costa Sousa

Student:

Partner:

Rock Flow Dynamics Inc;University of Calgary

Discipline:

Earth science

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Elevate

Exploring optimal trading rules in a high-frequency portfolio

Given a set of financial instruments with inherent characteristics at different time intervals, we are interested in finding an optimal trading rule in a high-frequency trading context. A trading rule is defined as a combination of indicators as well as an entry threshold (and potentially other trading parameters). The objective function we are trying to maximize is the profits of the strategy based on the trading rule. One impact of the non-linearity of such problems is that the gradient of the objective function is hard to estimate using a black-box approach. High frequency tick data are used so a high volume of samples is available and this fact compounds the problem. In this project, we set to explore different methods to perform an efficient random search such as the use of low discrepancy sequences, simulated annealing and evolutionary procedures. TO BE CONT’D

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Faculty Supervisor:

Manuel Morales

Student:

Partner:

Squarepoint Technologies

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

Université de Montréal

Program:

Accelerate

Automated transaction classification using machine learning algorithm

The procurement process of an organization is key to understand company costs. Organizations gather large amounts of data coming from different sources (e.g. income statement, balance sheet, general ledger lines). This information is heterogeneous in nature as it is a mix of unstructured and structured data. Moreover, it needs to be cleaned and consolidated in a taxonomy to enable category management. The objective is to group like-to-like items and/or services into categories from Supply Market Analysis point of view and consider category management for the holistic spend. Supervised and unsupervised machine learning algorithm seemed to be natural choices for this kind of problem because of the nature of the available data. PwC has already a first iteration of a classification product, dubbed SAM (Spend Analysis Machine) and it is based on supervised learning for text classification on general ledger accounts and supplier characteristics. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Maciej Augustyniak;Manuel Morales;Manuel Morales;Maciej Augustyniak

Student:

Partner:

PwC Management Services LP

Discipline:

Mathematics

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

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