Research Paper

Core Innovation

This paper introduces a novel ML model that forecasts the Gas Lift Performance Curve using only historical production time series data, eliminating the need for downhole gauges or multi-rate well tests. Coupled with a Bayesian Optimization Framework, it efficiently determines optimal gas injection rates under facility constraints, enabling practical and scalable gas lift optimization in unconventional fields.

Why It Matters

Oil producers face challenges optimizing gas lift in unconventional wells due to lack of downhole data and costly testing. This workflow delivers measurable production uplift by leveraging existing surface data and advanced ML, reducing operational costs and enabling scalable optimization across large well portfolios. It transforms gas lift management into a data-driven, cost-effective process.

Potential Customers & Pain Points

• Oil and gas operators – Need to optimize gas lift without expensive downhole gauges
• Asset managers – Require scalable production uplift solutions
• Field engineers – Lack real-time data for gas injection optimization
• Energy service companies – Seek cost-effective well performance enhancement tools

Business Model

Subscription-based SaaS platform offering gas lift optimization analytics and recommendations, with tiered pricing based on number of wells managed and support levels.

Competitive Landscape

• Schlumberger
• Halliburton
• Baker Hughes
• Weatherford

Implementation Challenges

• Integration with existing field data infrastructure
• Operator trust in ML-driven recommendations without downhole validation
• Variability in unconventional reservoir characteristics affecting model generalization

Validation Strategy

• Expand pilot deployments across diverse unconventional fields
• Demonstrate consistent production uplift and cost savings
• Collect operator feedback to refine model accuracy and usability
• Partner with service companies for broader market adoption