Leaks along the length of oil and natural gas pipelines result in the emission of greenhouse gases (GHG) such as carbon dioxide, methane, and others. Continuous monitoring to detect the presence of these gases rather than waiting for periodic inspections delivers a significant and immediate impact in reducing the GHGs that are warming the Earth. In fact, continuous monitoring systems can reduce methane emissions by approximately 60-90% when combined with rapid repair protocols. Additionally, eliminating in-person inspections reduces vehicle fuel consumption and associated emissions by up to 40%. Predicting pipeline emissions with AI is even better.
IoT + AI Blueprint:
Assemble the collection of components displayed below to address the use case of reducing greenhouse gas emissions along oil and natural gas pipelines.
Sensor(s)
MG-811 Sensor: Detects CO2 Gas
MQ-4 Sensor: Detects Methane Gas
Panic Button: Person in the Area Detects Emission Leak
LoRa: Create Low Bandwidth Coverage Needed to Reach Internet Access
Cellular: If Coverage Available and Cost-Effective for High Bandwidth Scenarios
Satellite: When No Terrestrial Coverage Options are Available for Medium Bandwidth Scenarios
Digital Twin Modeling
Collection of Oil and Natural Gas Pipelines
Data Processing + Storage Location(s)
Edge: Along Oil and Natural Gas Pipelines
Cloud: Filtered Data Relayed from Edge to Monitor Collections of Pipelines Across a Broader Region
Analytics
Compare Sensor Data Values to Defined Setpoints, KPI Value Ranges and Thresholds
Filter out Duplicate Sensor Data Value Readings
Automation
Green KPI: No Action
Yellow KPI: Warn Notification for Awareness When Sensor Values are Trending Towards Dangerous Parts Per Million (PPM) Levels
Red KPI: Alert Notification for Oil, Gas, or Pipeline Technicians to take Action when Methane Levels Exceed 1,000 PPM or CO2 Levels Exceed 5,000 PPM.
People
Deploy and Maintain Solution
SMEs Define KPIs and Actions
Pipeline Technicians, Oil and Gas Field Technicians
Security
Uniquely Identify Each Device
TLS 1.3 Encryption for Data in Transit
Encrypt Data at Rest
Validate Device Messages to Ensure they use Expected Data Format
Rotate Security Tokens
Limit IP Address Ranges
AI Anomaly Detection
Zero Trust: Reauthenticate Device Messages Through Every Step of the System
Artificial Intelligence
Machine Learning Time Series Forecasting Model to Predict Future Occurrence of Emissions Leakage
IoT Platform
Device SDK Captures Sensor Data from Physical Twin and Securely Sends it as a JSON Payload to the IoT Platform Along with a Unique Identifier and Security Token
IoT Platform Captures, Authenticates, and Saves Incoming Device + Sensor Data to a Message Queue
Background Process Takes Queued Data and Hydrates the Digital Twin by Saving it to a Database Table that Mimics the Structure of the Physical Twin
AI Model is Trained and Retrained with Digital Twin Dataset of Current and Historical Data Where Properties of Twin are Mapped to ML Features
Hot Path Data is Sent to Streaming Analytics to Facilitate Real Time Alerting and Automation
Same Hot Path Data is Also Sent to AI Model for Inferencing to Predict Future Occurrences
Rob is a writer, teacher, speaker, world traveller and undersea explorer. He's also a thought leader in the areas of enterprise mobility and the Internet of Things.
