25% reduced downtime with KPI-driven performance monitoring of flares to identify anomalies and reduce pollution

Overview

The company faced frequent pipeline downtime, high-risk exposure, and inadequate asset visibility. Aligned Automation implemented IoT sensors and AI/ML models for predictive maintenance, enabling real-time monitoring, streamlined workflows, and early issue detection. The solution significantly reduced service interruptions, mitigated risks, and enhanced cost efficiency across extensive pipeline networks.

Real Entire Case Study

Challenges

Unpredictable downtime: frequent service interruptions due to pipeline corrosion and leakage.
Risk exposure: high risk of environmental and operational hazards.
Lack of visibility: inadequate real-time monitoring of asset health.

Value Delivered

Aligned Automation implemented a data-driven approach using IoT sensors and AI/ML models to predict pipeline corrosion, leakage, and overall asset health. This enabled:

Predictive maintenance: AI/ML models provided early warnings for pipeline issues.
Efficient workflow: closed-loop, streamlined processes for quick action.
Continuous monitoring: real-time visibility into asset health across thousands of miles of pipeline

Capabilities

Data & Analytics

AI/ML

Process Transformation

About Client

This multinational oil and gas company is among the world’s largest companies out of any industry. This $381 billion company has over 86,000 employees worldwide and a presence in more than 70 countries.

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