Abstract
Abstract This paper presents a method for incorporating existing pipelines into the design of optimal CO2 transportation networks for carbon sequestration projects. The selection of the optimal pipeline transportation network is a crucial aspect of large-scale carbon sequestration projects as it greatly affects the project's economics. The method proposed in this paper aims to address the limitation of existing open-source tools such as SimCCS2.0which are unable to accommodate existing pipelines in techno-economic optimization. With the recent amendment to the 45Q laws, which now offers 70% more tax credits for carbon sequestration than in 2018, energy companies are exploring the possibility of repurposing gas and liquid transportation lines for CO2 transportation to abandoned oil and gas wells for carbon sequestration. This has further reinforced the need for a method that accounts for existing pipelines in sequestration economics. The proposed method achieves this by representing the pipeline paths on the construction cost graph as zero-cost paths. Additionally, pipeline tie-in locations are fixed by creating exclusion zones that limit inflow edges around the pipeline path. The solution is then obtained by solving for candidate transportation network routes using graph shortest path algorithms. This reformulation of the CO2 source-sink connection flow problem with limiting constraints on existing pipeline flow direction and capacity makes it possible to assess cost reduction associated with different CO2 sources tie-in locations along existing transport pipelines. The solution was developed using the Python programming language, and small-scale demo test cases have been used to illustrate its effectiveness in four tie-in cases with single pipeline and multiple pipelines that cut across several CO2 source and sink locations. The method has also been applied to the evaluation of a proposed CarbonSAFEII project and the results used to assess optimal pipeline tie-in points for expanded sequestration capacity. The developed python package is publicly available on GitHub for researchers and economic analysts to use for evaluating large-scale carbon capture, utilization, and storage (CCUS) projects, with the aim of encouraging further development and collaboration.
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