LNG
Project financing raised by JP Morgan will enable YPF and its partners Eni and ADNOC’s XRG to launch Phase 2 of Argentina LNG, boosting production to 18 mtpa by 2030–2031.
The Gorgon Phase 3 project aims to counter declining reservoir pressure to sustain gas supplies to Western Australia’s domestic market and support LNG exports to Asia.
By resuming work on the Rovuma LNG project, ExxonMobil tees up a final investment decision expected in 2026 as East Africa’s LNG hub begins to take shape with three separate projects now in construction.
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Train 4 is expected to add 6 million tonnes per year of capacity to the South Texas liquefied natural gas project when it goes online in 2030. -
Louisiana-based project will use operator’s Optimized Cascade process to turn feed gas into LNG.
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The floating liquefied natural gas ship Hilli Episeyo, currently offshore Cameroon, is planned for upgrades before redeployment to Argentina.
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As a champion of floating natural gas liquefaction technology, Eni is positioning itself to play a major role in Africa with a fast-growing gas-liquefaction capacity targeted to reach 14 million tonnes per year by 2028.
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NewMed has targeted the fourth quarter of 2025 to declare a final investment decision (FID) on Phase 1B of the Chevron-operated Leviathan gas project expansion after striking Israel’s largest ever gas export deal.
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TotalEnergies, Global Infrastructure Partners, and NextDecade divvy up Train 4 stakes ahead of an expected FID in September.
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The Japanese company ratcheted up the competitive nature of contracting for a floating production, storage, and offloading vessel and an onshore liquefied natural gas plant for the Abadi project by announcing dual front-end engineering design (FEED) contracts.
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This paper addresses how close collaboration has enabled the development of a robust and cost-efficient solution for the Ormen Lange project by using carefully selected technology elements and an accelerated qualification process to mature them.
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This study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data.
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This study presents the development of a novel modeling tool designed to predict condensate emulsions, focusing on key factors causing emulsions such as pH, solid content, asphaltene concentration, droplet size, and organic acids.
