scholarly journals Assessment of methane emissions from the U.S. oil and gas supply chain

Science ◽  
2018 ◽  
pp. eaar7204 ◽  
Author(s):  
Ramón A. Alvarez ◽  
Daniel Zavala-Araiza ◽  
David R. Lyon ◽  
David T. Allen ◽  
Zachary R. Barkley ◽  
...  
2021 ◽  
Author(s):  
Stefan Ladage ◽  
Martin Blumenberg ◽  
Dieter Franke ◽  
Andreas Bahr ◽  
Rüdiger Lutz ◽  
...  

Abstract Methane emissions along the natural gas supply chain are critical for the climate benefit achievable by fuel switching from coal to natural gas in the electric power sector. For Germany, one of the world’s largest primary energy consumers, we conducted fleet-conversion modelling taking domestic and export country specific emissions in the natural gas and coal chains into account. Methane leakage rates below 4.9 % (GWP20; immediate 4.1 %) in the natural gas supply chain lead to overall reduction of greenhouse gas emissions by fuel shifting from lignite and hard coal to natural gas. Reported supply chain methane emissions for Germany’s natural gas mix are with < < 1 % leakage rates well below this Germany-specific break-even leakage rate. Even a potential supply by U.S.-American liquefied natural gas to Germany would not exceed this critical rate. Supply chain emission scenarios demonstrate that a complete shift to natural gas would emit 30–55 % less greenhouse gases than from the coal mix. However, further abating supply chain methane emissions in the oil and gas sector should remain a prime effort, when considering natural gas as bridge fuel on the path to achieve the Paris climate goals.


2021 ◽  
Author(s):  
Md Abdur Rahman ◽  
Syed M. Belal

Abstract Keeping track of the oil and gas supply chain is challenging task as the route and transportation requires sophisticated security environment - both physical systems’ and IT systems’ security. Thanks to the recent advancement in IoT, specialized sensors can keep track of the required supply chain environment. With the help of blockchain, the supply chain data can be immutably saved for further sharing with stakeholders. Due to the introduction of AI as an embedded element within 6G networks, the end-to-end supply chain process can now be automated for safety, security, and efficiency purposes. By leveraging 6G, AI, blockchain, and IoT, the supply chain data during the transportation or at rest can be monitored for any changed environment during the movement of the ship through national or international routes. In this paper, we study the requirements of such intelligent and secure supply chain management system conducive to the oil and gas industry. We also show our proof-of-concept implementation and initial test results. Our obtained results show promising prospect of the current system to be deployed to safeguard the oil and gas supply chain.


2017 ◽  
Vol 52 ◽  
pp. 689-708 ◽  
Author(s):  
Ahmed M. Ghaithan ◽  
Ahmed Attia ◽  
Salih O. Duffuaa

2014 ◽  
Author(s):  
Jan Dell ◽  
Virginia Hart
Keyword(s):  

Author(s):  
Ahmed M. Ghaithan ◽  
Ahmed Attia ◽  
Salih O. Duffuaa

The oil and gas networks are overlapped because of the inclusion of associated gas in crude oil. This necessitates the integration and planning of oil and gas supply chain together. In recent years, hydrocarbon market has experienced high fluctuation in demands and prices which leads to considerable economic disruptions. Therefore, planning of oil and gas supply chain, considering market uncertainty is a significant area of research. In this regard, this study develops a multi-objective stochastic optimization model for tactical planning of downstream segment of oil and natural gas supply chain under uncertainty of price and demand of petroleum products. The proposed model was formulated based on a two-stage stochastic programming approach with a finite number of realizations. The proposed model helps to assess various trade-offs among the selected goals and guides decision maker(s) to effectively manage oil and natural gas supply chain. The applicability and the utility of the proposed model has been demonstrated using the case of Saudi Arabia oil and gas supply chain. The model is solved using the improved augmented ε-constraint algorithm. The impact of uncertainty of price and demand of petroleum products on the obtained results was investigated. The Value of Stochastic Solution (VSS) for total cost, total revenue, and service level reached a maximum of 12.6 %, 0.4 %, and 6.2% of wait-and see solutions, respectively. Therefore, the Value of the Stochastic Solution proved the importance of using stochastic programming approach over deterministic approach. In addition, the obtained results indicate that uncertainty in demand has higher impact on the oil and gas supply chain performance than the price.


2015 ◽  
Vol 15 (1) ◽  
pp. 83-92
Author(s):  
Diana Uspanova ◽  
Oluchi Uwannah ◽  
Liang-Chieh Cheng
Keyword(s):  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John C. Lin ◽  
Ryan Bares ◽  
Benjamin Fasoli ◽  
Maria Garcia ◽  
Erik Crosman ◽  
...  

AbstractMethane, a potent greenhouse gas, is the main component of natural gas. Previous research has identified considerable methane emissions associated with oil and gas production, but estimates of emission trends have been inconsistent, in part due to limited in-situ methane observations spanning multiple years in oil/gas production regions. Here we present a unique analysis of one of the longest-running datasets of in-situ methane observations from an oil/gas production region in Utah’s Uinta Basin. The observations indicate Uinta methane emissions approximately halved between 2015 and 2020, along with declining gas production. As a percentage of gas production, however, emissions remained steady over the same years, at ~ 6–8%, among the highest in the U.S. Addressing methane leaks and recovering more of the economically valuable natural gas is critical, as the U.S. seeks to address climate change through aggressive greenhouse emission reductions.


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