Advanced Well, Reservoir and Facilities Management (WRFM)
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A focused, practice-led program on integrating subsurface, wells, and facilities to lift production, improve recovery, and protect asset value. Participants use real field cases to diagnose constraints, rank opportunities, and implement closed-loop optimization aligned with company standards.
Workshop Objectives
• Build an integrated WRFM workflow linking reservoir, wellbore, and surface network models
• Diagnose production losses using nodal analysis, loss accounting, and structured root-cause methods
• Prioritize surveillance, workovers, artificial lift, and debottlenecking by risk, value, and execution readiness
• Optimize networks and facilities for pressure, capacity, flow assurance, and energy performance with KPIs for sustainment
About the Presenter
Delivered by a senior oil and gas practitioner with cross-disciplinary experience in reservoir engineering, production technology, and facilities optimization. Instruction emphasizes proven WRFM practices, decision-quality analysis, and practical tools that enable teams to progress from diagnosis to sustained performance improvement.
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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.