Emission management
Industry experts advocate for collaboration to develop and test technology that can reduce emissions without breaking the bank.
Viking Energy, the world’s first LNG-powered supply vessel, is set to become the world’s first ammonia-powered supply vessel.
Proving that green hydrogen can efficiently power steel pipe manufacturing could advance the EU’s plan to decarbonize its heavy industry by importing the sustainable fuel from North Africa after 2030.
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The Earth has huge capacity to store carbon dioxide emitted from energy production. This article discusses the technology of carbon capture, utilization, and storage (CCUS) and its challenges. -
The settlement to reduce emissions in North Dakota includes the largest ever Clean Air Act stationary source penalty and is expected to result in the reduction of more than 2.3 million tons of pollution.
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SponsoredTAQA bears the burden of reducing carbon emissions from the fossil fuel energy cycle and pays its undivided attention to decarbonizing its operations. In this article, it sheds light on its initiatives and approach to decarbonizing the oil and gas exploration and production industry.
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Government grants and tax incentives will drive carbon capture, storage, and/or utilization projects in the next decade as the industry seeks profitable business model.
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This paper proposes two new methods of accelerating the solidification (or mineralization) of CO2 in subsurface conditions, thus accelerating the cycle of the CO2 storage process.
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This paper examines the shortfalls of current CO2 capture and storage measures and their future within the context of increasing global consumption of fossil fuels.
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Applications are open for the funding, which comes from the Inflation Reduction Act, for projects that help monitor, measure, quantify, and reduce methane emissions.
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This paper presents efforts to reduce greenhouse-gas emissions and increase energy efficiency through the use of a real-time monitoring tool on exploration and production operated assets.
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This article explores the implementation of artificial intelligence vision for leak monitoring automation in the oil and gas industry and its role in improving safety standards, operational efficiency, and environmental performance.
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This paper investigates the use of machine learning to rapidly predict the solutions of a high-fidelity, complex physics model using a simpler physics model.
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