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Energy verified Verified Outcome TRL 9

AI for Predictive Maintenance in Energy

domain Client: A global energy supermajor handshake Provider: Microsoft Azure / C3.ai schedule Deploy: Q4 2020 (Scale)
89 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: High

Executive Summary

ANALYST: COI RESEARCH

To improve safety and reduce downtime on offshore rigs, the company deployed AI models to monitor over 10,000 pieces of equipment (valves, compressors). The 'Open AI Energy Initiative' platform ingests sensor data to predict failures before they happen, allowing for preemptive maintenance.

rate_review Analyst Verdict

"A leading implementation of 'Industrial AI' at scale. Moving from schedule-based maintenance (fix it every month) to condition-based maintenance (fix it when it acts weird) saves millions in unnecessary labor and unplanned outages."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

Offshore equipment failure is incredibly costly ($millions/day) and dangerous. With thousands of sensors generating noise, human operators could not detect subtle patterns indicating imminent failure of critical valves or compressors.

psychology The Solution

The entity standardized its data lake on Azure Databricks. They deployed C3 AI Suite on Azure to run predictive models. These models analyze vibration, temperature, and pressure trends to flag anomalies. Technicians receive alerts via mobile devices.

settings_suggest Technical & Deployment Specs

Integrations
OSIsoft PI, SAP PM
Deployment Model
Public Cloud
Data Classification
Industrial IoT
Estimated TCO / ROI
High
POC Summary (2018-01-01 to 2019-01-01)

"Initial tests on control valves."

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
False Positives Medium Human-in-the-loop verification of AI alerts.
Data Silos High Adoption of OSDU (Open Subsurface Data Universe) standards.

trending_up Impact Trajectory

Audited value realization curve

Monitoring of >10,000 industrial assets Verified Outcome
Primary KPIReduction in unplanned downtime
Audit CycleStandardization of subsurface data (OSDU)

policy Compliance & Gov

  • Standards: HSE (Health Safety Environment)
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Industrial IoT

folder_shared Verified Assets

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Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for A global energy supermajor without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-837
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-837

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