Downstream Logistics Optimization
Downstream and midstream energy facilities include a complex network of personnel, equipment, processes, and infrastructure. Maintaining base operations involves safe, reliable, and efficient movement of hydrocarbons into and out of sites to support the manufacturing and delivery of products to market.
The management of logistics operations is largely handled with paper-based and manually-intensive workflows, exposing companies to increased financial and field risk. Hydrocarbon distribution networks are diverse, and companies must effectively monitor, analyze, and optimize continuous operations.
The advent of energy-focused cloud technology now gives the industry the capability to improve the visibility and management of operations – orchestrating critical processes, automating workflows, and allowing cross-functional personnel to work efficiently together. AWS’ Downstream Logistics Optimization is a cloud-native solution to improve the tracking, analysis, forecasting, and optimization of hydrocarbon logistics operations. By combining geospatial intelligence and machine-learning services, the solution improves how schedulers, operators, and field personnel manage logistics operations of feeds, intermediates, and products - to improve business costs, field efficiency, utilization, and lower risk.
Monitor for anomalies and notify relevant personnel.
ETA predictions based on real-time variables.
Enable operational changes for field efficiency and incremental value.
Integrate with business applications and Contact Center.
Lower Operating Cost
Incremental Margin Profit
Lower Field Risk
Customer Case Study
TC Energy planners used to spend days and weeks to manually analyze, review and validate information from disparate sources to optimize available pipeline capacity. The company wanted to improve safety and cost-efficiency of operations, create a seamless transfer of information, and provide operational recommendations to controllers for real-time optimization of pipeline performance.
Leveraged data from existing OT systems in an Operations Data Lake, and applied Machine Learning services like Amazon SageMaker to build a forecasting model for optimizations. The solution was also able to forecast scenarios based on market conditions and provide anomaly detection and alerting for gas controllers. The company also used an intelligent document processing workflow powered with Artificial Intelligence to ingest historical paper-based data to aide with operational planning and regulatory compliance.
- Optimization of pipeline capacity and asset utilization
- Anticipated fuel cost savings and operational efficiencies
- Processed 20M+ record images (ensure safety, maintenance, regulatory compliance)
We can now maximize capacity from our existing system to serve our customers’ needs immediately, instead of building new facilities.”
Director of Capacity Management, TC Energy
How to get started
Phase 1: Discovery
- IT Security Review
- Data Source Identifications
- Process Flow Discovery
- IT Security Approval
- Finalize Data Strategy
- Infrastructure Inputs into Planning
- Define Engagement Score
Phase 2: Align
- Connectivity Identifications
- Source Prioritization
- Build RACI
- Define Models, Anomalies, User Stories
- Draft Architecture
- Define RACI
- Define Analytics/ML strategy
Phase 3: Launch
- Build Architectures
- Build Dashboards
- Implement and Validate Analytics/ML
- Solution Training
- End-to-End Workflow Testing
- Implement Solutions
- Implemented Dashboards
- Deploy Use Cases