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This study uses life cycle analysis (LCA) to evaluate the greenhouse gas (GHG) performance of carbon dioxide (CO2) enhanced oil recovery (EOR) systems. A detailed gate-to-gate LCA model of EOR was developed and incorporated into a cradle-to-grave boundary with a functional unit of 1 MJ of combusted gasoline. The cradle-to-grave model includes two sources of CO2: natural domes and anthropogenic (fossil power equipped with carbon capture). A critical parameter is the crude recovery ratio, which describes how much crude is recovered for a fixed amount of purchased CO2. When CO2 is sourced from a natural dome, increasing the crude recovery ratio decreases emissions, the opposite is true for anthropogenic CO2. When the CO2 is sourced from a power plant, the electricity coproduct is assumed to displace existing power. With anthropogenic CO2, increasing the crude recovery ratio reduces the amount of CO2 required, thereby reducing the amount of power displaced and the corresponding credit. Only the anthropogenic EOR cases result in emissions lower than conventionally produced crude. This is not specific to EOR, rather the fact that carbon-intensive electricity is being displaced with captured electricity, and the fuel produced from that system receives a credit for this displacement.

Life Cycle Analysis of Natural Gas Extraction and Power Generation 3 Upstream Data Upstream data include the supply shares of natural gas and coal, as well as the energy requirements and material flows for the key activities for extraction, processing, and transport. These data are used to model the RMA and RMT stages in NETL's natural gas and coal models. 3.1 Natural Gas The primary unit processes of this model are based on data compiled by NETL. Secondary unit processes, such as production of construction materials besides steel, are based on third party data. Appendix A includes details on how these data are assembled in a model and references the detailed documentation in NETL's unit process library. Where data for the inventory are available, high and low values are collected, along with an expected value. When results are presented, three cases are shown: an expected case, a high case, and a low case. The high and low results (error bars on the results) are a deterministic representation of the variability on the data and not indicative of an underlying distribution or likelihood. 3.1.1 Sources of Natural Gas This inventory and analysis includes results for natural gas domestically extracted from seven sources in the lower 48 states:

Global trade in liquefied natural gas (LNG) is growing significantly, as is interest in the life-cycle greenhouse gas (GHG) emissions associated with LNG. Most assessments of lifecycle GHG emissions from LNG have employed national or regional average emission estimates; however, there is significant variability in emissions across different suppliers and across the natural gas supply chain. This work describes a framework for compiling supplier-specific GHG emission data for LNG, from the producing well to regasification at the destination port. A case study is presented for Cheniere Energy's Sabine Pass Liquefaction (SPL) LNG supply chain from production in the United States and delivered to China. GHG emission intensities are estimated to be 30−43% lower than other analyses employing national or regional average emission profiles. The segments driving these differences are gas production and gathering, transmission, and ocean transport. Extending the boundaries of this analysis to the power plant illustrates the effect of fuel switching from coal to natural gas; the effect of fuel switching in China is a 47−57% reduction in GHG emission intensity, cradle through power generation. This work highlights the important role customized life-cycle assessments can play to improve GHG emission estimates and differentiate supply chains to inform business and policy decisions related to the transition to a low carbon future.