IBM > Case Studies > Santos: Saving millions with a predictive asset monitoring and alert system

Santos: Saving millions with a predictive asset monitoring and alert system

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Customer Company Size
Large Corporate
Region
  • Asia
  • Pacific
Country
  • Australia
Product
  • IBM SPSS Collaboration and Deployment Services
  • IBM SPSS Lab Services
  • IBM SPSS Modeler Desktop
  • IBM SPSS Modeler Server
  • IBM SPSS Statistics Desktop
Tech Stack
  • Predictive Modeling
  • Internet of Things (IoT)
  • SCADA
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Cost Savings
  • Productivity Improvements
  • Employee Satisfaction
Technology Category
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Oil & Gas
Applicable Functions
  • Maintenance
  • Logistics & Transportation
Use Cases
  • Predictive Maintenance
  • Remote Asset Management
Services
  • Data Science Services
About The Customer
Santos Ltd. is one of the leading oil and gas producers in the Asia-Pacific region, serving the energy needs of homes, businesses and major industries across Australia and Asia. The company operates one of the largest exploration and production businesses in Australia. Its extensive operations rely on a huge and highly complex network of assets, including thousands of kilometers of pipeline, wells, pumps, compressors and other equipment. Santos reports annual revenues of AUD 4 billion (USD 2.9 billion). The company's network extends over a vast geographic area, much of it in remote and even hostile environments such as the Australian Outback.
The Challenge
Santos Ltd., one of the leading oil and gas producers in the Asia-Pacific region, operates one of the largest exploration and production businesses in Australia. Its extensive operations rely on a huge and highly complex network of assets, including thousands of kilometers of pipeline, wells, pumps, compressors and other equipment. Keeping this specialist infrastructure in working order is a key priority for the company, as any downtime can interrupt production and limit profitability. The company had been leveraging Internet of Things (IoT) technologies such as SCADA to collect information from thousands of sensors across its asset network for many years. The challenge was to harvest and sift through this data, recognize the patterns that indicate a high likelihood of asset failure, identify the most urgent issues, and get the right information to its engineers with enough lead time for them to take effective action.
The Solution
To create an effective alert system for equipment failure, Santos looked to predictive modeling. Embarking on a pilot project with help from IBM SPSS Lab Services, the company pulled structured and unstructured data from multiple sources including: the equipment trends database, which tracks SCADA data such as the speed at which a compressor is operating; the operator shift log, a system in which field operatives record their activity; the computerized maintenance management system, which maintains a record of assets and their maintenance history; and the asset loss and availability system, an accounting solution used to trace the sources of production losses. Using predictive models, the company can generate early warnings of any faults, and has gained insight into new ways to optimize power supply for greater efficiency.
Operational Impact
  • Optimized maintenance scheduling and use of materials, reducing costs.
  • Improved safety by cutting the time engineers spend in remote locations and traveling.
  • Enabled early alerts when corrosion inhibitor tanks are about to run dry, and when pumps are operating abnormally.
  • Helped field teams prioritize and optimize their schedules and inhibitor stocks.
Quantitative Benefit
  • Potential annual savings of AUD 10 million by increasing production uptime.
  • Switched to batteries costing AUD 2,000, which should last up to four years, from batteries that cost AUD 5,000 each and lasted about 18 months to two years.
  • Potential annual savings of AUD 1.1 million by injecting inhibitor periodically rather than keeping the pumps running continuously.

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