Foresight Pilot

Overview

FORESIGHT is an AI-powered ESP (Electric Submersible Pump) failure prediction and optimization solution that proactively reduces downtime and enhances equipment reliability. By combining real-time sensor data with machine learning and physics-based simulation, FORESIGHT predicts failures up to 90 days in advance, classifies fault types, and provides actionable alerts for better maintenance planning.

This one-week professional services pilot is designed to help you assess the feasibility of deploying FORESIGHT on Microsoft Azure, validate predictive workflows, and accelerate your Azure-driven asset reliability program.

Key Benefits

• Predictive ESP Failure Detection: Identify electrical, mechanical, and completion-related failures before they occur.
• Reduced Downtime: Receive early warnings and proactively plan interventions.
• Optimized ESP Performance: Extend run life and improve asset reliability through AI-driven analytics.
• Azure-Ready Architecture: Built on secure, scalable Azure services with real-time data streaming, model deployment, and visualization.

Pilot Deliverables

1. Azure Data Integration Setup
   • Connect PI Historian and field sensor feeds to FORESIGHT hosted on Azure.
2. Hybrid Model Deployment
   • Run real-time AI and physics-based models via Azure Web Apps or Kubernetes.
3. Monitoring Dashboards
   • Visualize Failure Probability Indicators and alerts with Power BI and Azure Monitor.
4. Scalability Recommendations
   • Guidance on expanding to full production using Azure Synapse, Azure ML, and other native tools.

Why Choose This Pilot?

• Proven Impact: Deployed across 83+ wells with high model accuracy and operator trust.
• Accelerated Insights: Rapidly demonstrate predictive capabilities and field value.
• Expert Guidance: Delivered by AIQ’s AI and subsurface specialists for upstream operations.

Next Steps

1. Purchase & Kickoff: Start the pilot and configure Azure environment.
2. Model Deployment: AIQ team tailors and deploys FORESIGHT models to your asset data.
3. Performance Review: Conclude with a report on predictive accuracy and roadmap for scale-up.