Well Intervention Optimization

The Problem Today

Production engineers manually analyze decline curves, pressure data, and intervention history across hundreds of wells. The decision cycle stretches 2–6 weeks from decline identification to intervention approval. 30–40% of interventions underperform expectations due to poor timing or method selection. Data is scattered across production databases, SCADA sensors, well logs, intervention records, and economic models.

How It Works

Every well intervention solution is powered by a Decision Value Loop – a continuous cycle of five stages:

1. Sense: Continuous production data from 1,000+ wells – rates, pressures, temperatures, water cut, gas-oil ratio.
2. Analyze: Real-time decline curve analysis against reservoir models. Compare candidate interventions by cost, expected uplift, and risk.
3. Decide: Rank interventions by NPV. Recommend optimal timing and method for each well.
4. Act: Generate intervention work order with cost estimate and production forecast.
5. Learn: Track post-intervention production against forecast. Refine decline models and intervention effectiveness per formation type.

Why Not Off-the-Shelf AI?

Each reservoir is geologically unique. Intervention decisions require integrating subsurface understanding (geology, reservoir simulation) with surface operations (production data, equipment availability) and economics (oil prices, intervention costs). Generic AI cannot model the physics of reservoir behavior.

The metricsIQ Advantage

Our OSDU expertise directly applies – well intervention data integration is the canonical OSDU use case. Our team has built production optimization systems at enterprise scale. The combination of geospatial, time-series, and complex data integration is a core competency we bring from 47Lining heritage and Fortune 50 Oil & Gas engagements.