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Posted: March 28th, 2023
TOPIC: Carbon, Capture ,and Storage (CCS)
TOPIC: Carbon, Capture ,and Storage (CCS)
SUBJECT: Case Study
DESCRIPTION:
Throughout this course, you will develop an understanding of the deterministic and probabilistic methods used in uncertainty management that will enable you to review a simplified “straw man” case study of a hypothetical project. The purpose of the case study is to: • Discuss a hypothetical carbon capture and storage (CCS) project, called Curiosity, and investigate its uncertainty exposure. • Demonstrate application of standard deterministic methodology to identify, assess, and address the project uncertainties. • Demonstrate integrated probabilistic cost and schedule risk analysis methodology to define project reserves and sensitivities. • Develop and analyze what-if scenarios used for decision making. • Come up with general recommendations and decisions that are common for a capital project at the end of selection. Willing to pay more if someone knows how to create two PETRA figures in Excel.
I can definitely help you with the case study related to Carbon Capture and Storage (CCS) project, but unfortunately, I cannot create PETRA figures in Excel.
Let’s discuss the case study first. The Curiosity CCS project is a hypothetical project that aims to capture carbon dioxide emissions from an industrial facility and transport it to a storage site for long-term storage. The project involves several uncertainties, including technology uncertainties, market uncertainties, and regulatory uncertainties.
To manage these uncertainties, we can apply deterministic and probabilistic methods. Deterministic methods involve identifying and assessing risks based on historical data and expert opinions. This can be done by creating a risk register and using risk matrices to assess the likelihood and impact of each risk. Once the risks are identified, we can develop risk response plans to mitigate or avoid them.
On the other hand, probabilistic methods involve quantifying the uncertainties using statistical techniques and simulations. This can be done by conducting a Monte Carlo analysis, which involves running multiple simulations to determine the likelihood of different outcomes. This analysis can help us determine project reserves and sensitivities and identify what-if scenarios for decision making.
At the end of the selection, we can come up with general recommendations and decisions based on the project’s risks and uncertainties. This can include determining the feasibility of the project, evaluating alternative technologies, and assessing the financial viability of the project.
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