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

Company Size

1,000+

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
Employee Satisfaction
Productivity Improvements

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Oil & Gas

Applicable Functions

Logistics & Transportation
Maintenance

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.