Big Data for Reservoir Surveillance
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This workshop examines how big data technologies are reshaping reservoir surveillance, enabling faster insights and more accurate decision-making. Participants will learn how to leverage data integration, analytics, and visualization tools to monitor reservoir performance and optimize field development strategies.
Workshop Objectives
• Understand the role of big data in modern reservoir surveillance.
• Learn methods for integrating and analyzing large, diverse datasets.
• Explore applications of predictive analytics and real-time monitoring.
• Apply data-driven insights to enhance production and reservoir management.
About the Presenter
The workshop will be delivered by an experienced professional in reservoir engineering, data analytics, and digital oilfield solutions. Combining technical knowledge with practical field experience, the presenter shares actionable strategies to help participants unlock value from data in reservoir surveillance.
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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.