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Reducing Downtime & Cost with AI-Powered Predictive Maintenance

Impact Delivered

  • Higher failure prediction accuracy
  • Reduced unplanned downtime
  • Lower maintenance costs
  • Improved operational efficiency
  • The Customer

    The Customer

    A major Oil & Gas operator managing complex assets and large multi-source datasets across downhole and surface equipment—seeking to predict failures earlier, optimize production, and improve operational visibility and efficiency.

  • The Challenge

    The Challenge

    3D-Priniting-Challenges-Img.jpg

    Build accurate, explainable failure prediction and optimization across the lifecycle by:

    • Predicting downhole failures (rod parts, pumps, tubing, casing, formation issues).
    • Monitoring surface issues (pump jack parts, flow lines, valves, leaks, pressures).
    • Deriving ML patterns (e.g., warranty-to-failure) with weather/operations sub-factors.
    • Ingesting high-resolution visual and sensor data in near-real-time.
    • Archiving data for historical reporting and deeper analysis.
  • The Solution

    The Solution

    the-solution-img_nextiva.jpg

    An AI/ML-driven analytics platform tailored for Oil & Gas operations:

    • Downhole analytics: Predictive models for critical subsurface components and conditions.
    • Surface analytics: Monitoring flow lines, vents, leaks, pressures, and related equipment.
    • Derived analytics: ML-based pattern detection including warranty-to-failure analyses with context sub-factors.
    • Realtime monitoring: Dynamometer cards and fact analytics via browser and mobile.
    • Data acquisition: Drone-captured visual inspections; seamless well & sensor data ingestion.
    • Data archiving: Persistent storage for history, reporting, and model refinement.
  • Solution Components

    Solution Components

    Momentum-powered RESTful APIs; Neural-network models; Real-time monitoring (dynamometer cards & fact analytics); High-resolution visual inspections (drones); Secure data acquisition & archiving; Integrated data platform

  • The Results

    The Results

    The solution improved reliability and operational outcomes:

    • Earlier failure detection: Actionable lead time to intervene.
    • Optimized production: Insights to tune operations and throughput.
    • Cost savings: Fewer breakdowns and targeted maintenance.
    • Scalable architecture: Secure APIs and integrated data flows supported expansion.

Impact Delivered

  • Higher failure prediction accuracy
  • Reduced unplanned downtime
  • Lower maintenance costs
  • Improved operational efficiency

The Customer

A major Oil & Gas operator managing complex assets and large multi-source datasets across downhole and surface equipment—seeking to predict failures earlier, optimize production, and improve operational visibility and efficiency.

The Challenge

3D-Priniting-Challenges-Img.jpg

Build accurate, explainable failure prediction and optimization across the lifecycle by:

  • Predicting downhole failures (rod parts, pumps, tubing, casing, formation issues).
  • Monitoring surface issues (pump jack parts, flow lines, valves, leaks, pressures).
  • Deriving ML patterns (e.g., warranty-to-failure) with weather/operations sub-factors.
  • Ingesting high-resolution visual and sensor data in near-real-time.
  • Archiving data for historical reporting and deeper analysis.

The Solution

the-solution-img_nextiva.jpg

An AI/ML-driven analytics platform tailored for Oil & Gas operations:

  • Downhole analytics: Predictive models for critical subsurface components and conditions.
  • Surface analytics: Monitoring flow lines, vents, leaks, pressures, and related equipment.
  • Derived analytics: ML-based pattern detection including warranty-to-failure analyses with context sub-factors.
  • Realtime monitoring: Dynamometer cards and fact analytics via browser and mobile.
  • Data acquisition: Drone-captured visual inspections; seamless well & sensor data ingestion.
  • Data archiving: Persistent storage for history, reporting, and model refinement.

Solution Components

Momentum-powered RESTful APIs; Neural-network models; Real-time monitoring (dynamometer cards & fact analytics); High-resolution visual inspections (drones); Secure data acquisition & archiving; Integrated data platform

The Results

The solution improved reliability and operational outcomes:

  • Earlier failure detection: Actionable lead time to intervene.
  • Optimized production: Insights to tune operations and throughput.
  • Cost savings: Fewer breakdowns and targeted maintenance.
  • Scalable architecture: Secure APIs and integrated data flows supported expansion.
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