LNG Market and Global Supply Chain
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This workshop examines the global LNG market and the complexities of its supply chain, from production and liquefaction to shipping, regasification, and distribution. It highlights market drivers, trade flows, and the evolving role of LNG in meeting global energy demand, while addressing the challenges of supply security and price volatility.
Workshop Objectives
• Understand the structure and dynamics of the global LNG market.
• Analyze major trade routes, supply hubs, and emerging demand centers.
• Review pricing mechanisms, contractual structures, and market trends.
• Explore logistics challenges in LNG shipping, storage, and delivery.
• Assess geopolitical, regulatory, and sustainability factors shaping the LNG supply chain.
About the Presenter
The workshop is led by an experienced energy professional in LNG markets, trading, and global supply chains. Combining technical knowledge with commercial insight, the presenter offers participants a well-rounded perspective on how LNG supply and demand interact across international markets.
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For PEA Members Only
Modern Reservoir Characterization : Integrating Artificial Intelligence (AI) with Traditional Methods
During this workshop, participants will develop a more comprehensive grasp of the geophysical theory of seismic inversion and explore advanced seismic reservoir characterization techniques. Discover the essential steps in the seismic reservoir characterization workflow using machine learning, covering data processing to inversion analysis. Acquire valuable knowledge applicable to your everyday projects.In classical reservoir characterization, it is commonly assumed that the reservoir exhibits both elastic and hydraulic isotropy. However, real reservoirs often exhibit anisotropic characteristics and spatial heterogeneity on multiple scales. Relying solely on core and log data may not accurately represent the larger reservoir volume. Therefore, the most effective way to accurately analyze the physical properties of most actual reservoirs, across their entire volume, involves utilizing advanced machine learning methods for acquired and interpreted seismic data.