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Cheniere Produces First LNG in CCL Stage 3

Cheniere Energy, Inc. has produced the first liquefied natural gas (LNG) at the first train of the company’s Corpus Christi Stage 3 Liquefaction Project (CCL Stage 3). Cheniere said in a news release that the commissioning process continues to progress and that it expects substantial completion of Train 1 to be achieved at the end […]

Cheniere Energy, Inc. has produced the first liquefied natural gas (LNG) at the first train of the company’s Corpus Christi Stage 3 Liquefaction Project (CCL Stage 3).

Cheniere said in a news release that the commissioning process continues to progress and that it expects substantial completion of Train 1 to be achieved at the end of the first quarter of 2025, over six months ahead of the guaranteed completion date.

Upon substantial completion, Bechtel Energy, Inc. will transfer the care, custody and control of the completed train to Cheniere, according to the release.

CCL Stage 3 consists of seven midscale trains, with an expected total production capacity of over 10 million metric tons per annum (mtpa) of LNG. Upon the substantial completion of all seven trains of CCL Stage 3, the expected total production capacity of the Corpus Christi liquefaction facility will be over 25 mtpa of LNG, Cheniere said.

Full notice to proceed on CCL Stage 3 was issued to Bechtel by Cheniere in June 2022.

In October 2024, Cheniere set a voluntary, measurement-informed Scope 1 annual methane intensity target for its liquefaction facilities.

The Scope 1 methane target leverages data from its multi-scale emissions measurement and mitigation programs. The methane target is consistent with the requirements to achieve Gold Standard under Cheniere’s membership in the United Nations Environment Programme’s (UNEP) Oil & Gas Methane Partnership (OGMP) 2.0, according to an earlier statement from the company.

Cheniere said it aims to consistently maintain a Scope 1 annual methane emissions intensity of 0.03 percent per metric ton of LNG produced across its two U.S. Gulf Coast liquefaction facilities by 2027.

The company’s methane target was guided by its Quantification, Monitoring, Reporting and Verification (QMRV) projects, which included data from approximately 50 aerial measurements of Cheniere’s operations at its liquefaction facilities performed over a 16-month period.

“Cheniere’s LNG plays a critical role in meeting the world’s growing need for secure and reliable energy, while supporting the transition to a lower-carbon future,” Cheniere President and CEO Jack Fusco said. “Our methane emissions intensity target reflects our commitment to leverage measurement-informed emissions data to improve the climate competitiveness of our LNG and ensure the long-term resilience of our business”.

In the three and nine months ended September 30, 2024, Cheniere generated revenues of approximately $3.8 billion and $11.3 billion, as well as net income of approximately $0.9 billion and $2.3 billion, according to its most recent earnings release.

Cheniere’s net income decreased approximately $808 million year-over-year in the third quarter. The decreases were primarily attributable to “unfavorable variances related to changes in fair value of our derivative instruments,” the company said. The decreases were partially offset by lower provisions for income tax, as well as lower net income attributable to non-controlling interests.

“Our team’s unwavering focus on safety, execution and capital discipline once again enabled key achievements throughout our business, highlighted by our 1,000th LNG cargo at Corpus Christi, continued progress on Stage 3, and further follow-through on our comprehensive capital allocation plan,” Fusco said. “Our outstanding results and improved outlook enable us to further raise and tighten our guidance ranges for 2024, while the progress achieved on Stage 3 provides increased visibility into our production forecast for 2025. As we complete another strong year at Cheniere, reinforcing our track record for operational excellence and safety, executing on our long-term capital allocation plan, and growing our leading infrastructure platform remain our foremost priorities as we aim to reliably meet the energy needs of our customers worldwide for decades to come”.

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2025 playbook for enterprise AI success, from agents to evals

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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