Artificial-Lift Optimization Begins with Data — Not AI
Artificial-lift systems are essential to sustaining production across unconventional assets, but many operators still manage lift performance using static control strategies and disconnected data. As well conditions change, fragmented information can lead to suboptimal drawdown, reduced production, increased equipment stress, and reactive maintenance.
This executive brief explores why artificial-lift optimization begins with a strong data foundation—not AI alone. By unifying and contextualizing data across field instruments, lift control systems, SCADA, historians, edge systems, and enterprise environments, operators can create system-wide visibility and move from reactive operations toward predictive and increasingly autonomous performance management.
The brief outlines a practical path from data unification to advanced analytics, machine learning, coordinated optimization, and closed-loop control. With an integrated edge-to-enterprise architecture, organizations can turn real-time operational data into actionable decisions while preserving existing infrastructure and scaling digital capabilities over time.
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 performance and recovery through real-time lift optimization
- Reduce unplanned downtime with predictive maintenance and early equipment-degradation insights
- Optimize energy consumption and extend equipment life
- Increase workforce productivity through automation and real-time operational visibility
- Connect analytics to control strategies for continuous adjustment based on changing conditions
- Build a scalable foundation for machine learning and more autonomous operations
- Align production decisions more closely with asset-level and financial objectives
Download the Executive Brief
Learn how upstream operators can move beyond fragmented data and reactive artificial-lift management to create a connected, data-driven foundation for higher production, lower operating costs, and scalable AI-enabled optimization.
