Case Study – NNS Exploration Applying AI/Machine Learning
NNS Exploration Applying AI/Machine Learning
The project demonstrated the application of advanced data analytics and ML to existing well logs, showcasing the automated identification of 'missed pay'.
By applying Artificial Intelligence to legacy data, integrated into a trusted platform. The system then automates a petrophysics workflow analysis using unsupervised machine learning (ML) models on sample data to extract correlate-able features.
Nine NNS operators provided data to prepare and compile machine readable datasets for the Northern North Sea area, containing nearly 5,000 wells, and using machine learning analytics to identify missed 'pay zones', potentially containing additional hydrocarbon reserves.
The enhanced machine-readable dataset allowed analytics algorithms to be developed, helping identify remaining exploration potential in the Northern North Sea area.
- AI Driven Data Digitalisation & Image based reserves analytics
- Entry TRL 6,
- Target TRL 8
- 2 Conditioned datasets….
- 3 Ranked lists of missed pay….
- Industry wide and cross border collaboration, data contribution and support of 9 operators, Oil & Gas Authority and Norwegian Petroleum Directorate.
Data preparation is fundamental, and often more cumbersome than anticipated. Stakeholder Management along with clarity on consortium objectives and final delivery mechanisms is key.
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