GoldMineCo—Use of EIDI Data in Gold-Mining Operations

GoldMineCo is one of the largest gold-mining companies in the world. The company has mining operations in various continents. In 2018, it produced several million ounces of gold. As of December 31, 2018, the company had more than 50 million ounces of proven and probable gold reserves.

Faced with a downturn in gold prices since 2012 and rising exploration costs, the life cycle to bring new reserves online is extensive with a 10-year minimum to transition from discovery to production. GoldMineCo was under pressure to reduce operational costs and believed that better utilization and analysis of its operations data would identify root causes of production inefficiencies.

The company decided that cost control was paramount, so they set target goals for processing costs. In early 2013, GoldMineCo worked diligently to trim excess costs from its operations, prior to any returns with the PI System. In 2013, 2014, and 2015, they made strides in reducing their all-in-sustaining processing costs. Additional reductions needed to come from innovative use of digital technology.

In 2014, GoldMineCo leadership launched a corporate-wide initiative to standardize KPI reporting for its mines. GoldMineCo's digital transformation team recommended an enterprise-wide real-time data infrastructure approach, acting as the real-time data system of record for consolidated operations and production data. Even though business conditions were difficult and GoldMineCo was in cost-cutting mode, the company decided to standardize on OSIsoft's PI System software via an OSIsoft enterprise agreement (EA). In earlier cases where the PI System was used sporadically throughout the company, they found that individual users achieved cost reductions with the PI System, but these accomplishments were not well documented and not transferred to similar facilities. The company as a whole didn't benefit significantly because of lack of collaboration with corporate personnel. Upon EA execution, PI Systems were deployed across all GoldMineCo sites in a standardized manner, with the expectation that benefits of scale could be achieved with the right employee training and with greater collaboration.

GoldMineCo decided to focus their efforts on the following areas:

  • • Develop process plant modeling and use PI for root cause analysis for optimization.
  • • Create a dynamic mass-energy balance.
  • • Improve water management and reporting.
  • • Transition to a more effective condition-based maintenance (CBM) program.
  • • Reduce data gathering time for regulatory compliance reporting.
  • • Perform big data analytics using PI as the real-time operations data feed.

The company began an extensive user-training initiative and standardized things such as asset data definitions, online calculations, real-time notifications, and reporting. GoldMineCo also decided to build out its PI AF hierarchical data model for several of its sites, which provided a templatized way to define physical assets, real-time calculations, and real-time notification alerts in a reusable manner. This helped their personnel analyze and identify root causes of equipment failures, excess energy usage, and production inefficiencies. These efforts resulted in improved equipment availability and utilization, increased output rates, and reduced dilution, which greatly impacted profitability.

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