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The upstream oil and gas sector is the second largest source of anthropogenic methane emissions within Canada. As new policies and regulations are being introduced to reduce methane emissions, Canada’s oil and gas producers and provincial regulators need to be able to quantify the cost-effectiveness of various strategies to reduce emissions and identify the best technologies to quantify methane emissions. Arolytics Incorporated, a Canadian SME, has developed a simulation-based software product called AROfemp, which they use to help their clients in the oil and gas sector design fugitive emissions management programs (FEMPs), in order to satisfy methane emissions regulations. However, in its current state, AROfemp does not address the quantification accuracy of these FEMPs and is lengthy to run. The first objective of this project is to review the internal design and stochastic simulation model of the software, and apply statistical and computing techniques to accelerate the code in order to support more complex evaluations. The second objective is to review the statistical methods used to estimate the parameters in the simulation model, propose related improvements (by e.g. proposing more realistic probability distributions), and ensure these outputs are all delivered with a statistically sound error estimate.
Audrey Béliveau;Christiane Lemieux
Arolytics Incorporated
Mathematics
Professional, scientific and technical services
University of Waterloo
Business Strategy Internship
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