Artificial-Lift Optimization Begins with Data — Not AI

Artificial-lift systems are essential to sustaining production across unconventional assets, yet many operators rely on static control strategies and disconnected data. Changing well conditions can lead to suboptimal drawdown, reduced production, equipment stress, and reactive maintenance.

This executive brief explores why artificial-lift optimization starts with a strong data foundation—not AI alone. By unifying data across instruments, lift controls, SCADA, historians, edge systems, and enterprise environments, operators can improve visibility and move toward predictive and autonomous performance management.

Learn how a data-driven approach to artificial-lift optimization can help operators:

• Unify and contextualize operational data across wells, pads, facilities, and enterprise systems
• Improve production and recovery through real-time lift optimization
• Reduce downtime with predictive maintenance and equipment insights
• Optimize energy consumption and extend equipment life
• Increase workforce productivity through automation and visibility
• Enable continuous adjustment as conditions change
• Build a scalable foundation for machine learning and autonomous operations
• Align production decisions with asset and financial objectives