The reliability of reservoir models in predicting future performance is crucial for investment decisions in field and storage development projects. Flow complexities in porous media, including data availability and resolution, necessitate continuous integration between dynamic data analysis, modelling, and surveillance.

This session will explore the mutual influence of modelling and surveillance in creating predictive subsurface models. Discover innovative advancements in data analysis and dynamic modelling to identify critical surveillance activities and develop strategies for addressing uncertainties. Additionally, learn how surveillance data enhances the reliability of dynamic models through seamless integration workflows.

Session Managers: Chander Sekhar Singh, PETRONAS; Jazael Ballina, Baker Hughes

Discussion Leaders:

• Leveraging Data Analytics, Map-Based Study and MBAL for Dynamic Model History Matching in a Highly Compartmentalised System with Cross-Fault Communication by Gunajit Das, Dialog Energy Sdn. Bhd.
• Dynamic Model Improvement Through Well and Skin Modification to Reduce Production and Allocation Uncertainties In Viscous Multi-stack Reservoirs by Umar Islam M Dimyati, PETRONAS Carigali Sdn. Bhd.
• Achieving Excellence in Mature Gas Field Development: Insights from Arun Field's Development Scenario Selection by Setyo Nurretno Tri Buwono, Pema Global Energi
• Dynamic Modeling under Uncertainty Coupled with Surveillance Technology Lead to Informed Decision for Mature Field Redevelopment by Muhammad Hafiz Khairul Azmi, JX Nippon Oil & Gas Exploration (Malaysia) Limited