Background & Objective

Key Pain Points:

• Subsurface data are mostly stored in personal devices and manage personally by geoscientist/engineer
• Manual data management causing multiple version of subsurface data
• Aspire to have single storage for all subsurface data and integrated with reservoir and drilling database.

Solution Design:

• Develop feed zone identification workflow leveraging machine learning models to process subsurface data and producing more accurate and efficient analysis

Potential Benefit

• Reduce data duplication by providing single source of truth of all geoscience data
• Provide reliable data and enable faster and more confidence decisions
• Improve productivity by providing efficient workflow in visualizing subsurface data

Project Managers

• Business : Inusa Pamusyawara Fauzi, Sardiyanto, Antonius Rishang Untoro
• Digital : Bayu Alfajri