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In February 2018, the United States enacted significant financial incentives for carbon capture, utilization, and storage (CCUS) that will make capture from the lowest-capture-cost sources economically viable. The largest existing low-capture-cost opportunity is from ethanol fermentation at biorefineries in the Midwest. An impediment to deployment of carbon capture at ethanol biorefineries is that most are not close to enhanced oil recovery (EOR) fields or other suitable geological formations in which the carbon dioxide could be stored. Therefore, we analyze the viability of a pipeline network to transport carbon dioxide from Midwest ethanol biorefineries to the Permian Basin in Texas, which has the greatest current carbon dioxide demand for EOR and large potential for expansion. We estimate capture and transport costs and perform economic analysis for networks under three pipeline financing scenarios representing different combinations of commercial and government finance. Without government finance, we find that a network earning commercial rates of return would not be viable. With 50% government financing for pipelines, 19 million tons of carbon dioxide per year could be captured and transported profitably. Thirty million tons per year could be captured with full government pipeline financing, which would double global anthropogenic carbon capture and increase the United States' carbon dioxide EOR industry by 50%. Such a development would face challenges, including coordination between governments and industries, pressing timelines, and policy uncertainties, but is not unprecedented. This represents an opportunity to considerably increase CCUS in the near-term and develop long-term transport infrastructure facilitating future growth.

In response to anthropogenic CO2 emissions, geological storage has emerged as a practical and scalable bridge technology while renewables and other environmentally friendly energy production methods mature. While an attractive solution, geological storage of CO2 has inherent risk. Two primary concerns are recognized: (1) leakage of CO2 through caprock imperfections, and (2) brine displacement resulting in contamination of drinking water sources. Three mechanisms for both CO2 and brine leakage have been identified: diffuse leakage through the caprock, leakage through faults and fractures in the caprock, and finally, leakage through man-made pathways such as abandoned wells from oil and gas exploration. While the first two leakage mechanisms are important, we emphasize the risks associated with the presence of abandoned wells. This is due to the large number and density of wells from a history of oil and gas exploration around the world, and the high degree of uncertainty surrounding the properties of these abandoned wells. With current proposed legislation in both the United States and Europe, a need is emerging for practical assessment of leakage risk. In order to accurately predict leakage of brine and CO2 from the injection layer, the geological information for the injection site and the location and makeup of the man-made leakage pathways previously alluded to must be taken into account. Unfortunately, both the geology and abandoned well metadata are typically high in uncertainty, which must be accounted for. With such a high number of random variables, the current state of the art is running many realizations of a system, using a Monte Carlo approach. This requires that the underlying solution algorithms be accurate, and efficient. In the past, many researchers in both academia and industry have turned to robust numerical analysis packages used in the oil industry. However, due to the large range of scales important to this problem (domains of tens of kilometers on a side affected by leakage pathways with diameters of tens of centimeters) such modeling techniques become computationally expensive for all but the most basic analysis. A computational model developed at Princeton University, and currently being commercialized by Geological Storage Consultants, LLC has been shown to be efficient with sufficient accuracy to allow for comprehensive risk assessment of CO2 injection projects. The model allows for mixing solution methods- using computationally expensive algorithms for formations of greater importance (e.g.- the injection formation) and more efficient, simplified algorithms in other areas of the domain. This ability to arbitrarily mix solution methods offers significant flexibility in the design and execution of models. This paper addresses the framework and algorithms used, and illustrates the importance of efficiency and parallelism using the case study of an injection site in Alberta, Canada. We show how the framework can be used for project planning, for risk mitigation (insurance), and for regulatory groups. Finally, the importance of flexible analysis tools that allow for efficient and effective management of computational resources is discussed. ; publishedVersion