Combining Baker Hughes energy technology expertise with C3 AI technology
AI-based applications on a hardened AI platform
BHC3™ applications leverage advanced machine learning and AI technology to uncover patterns from large data sets, enabling predictive action for oil and gas operations. BHC3 SaaS applications are cloud agnostic and address challenges across the upstream, midstream and downstream sectors.
A powerful alliance: BHC3 on Microsoft Azure
This strategic alliance brings together an ecosystem of technology specialists to help the energy industry more rapidly scale digital transformation programs. We have integrated and optimized BHC3 AI applications on Microsoft Azure, delivering our technology on Azure's established and secure cloud platform that meets the global compliance needs of highly regulated industries, including energy.
Digital transformation in oil and gas
AI offers significant potential for true business transformation. BHC3 AI technologies can impact all aspects of energy-related operational efficiencies, including improving reliability and reducing downtime, optimizing production, and increasing yield. AI also extends beyond industrial operations to help organizations reimagine and redefine their businesses, supporting critical business priorities such as the energy transition.
Enterprise AI for petrochemical reliability
Summary
A global top 10 petrochemical manufacturer with operations in more than 40 countries configured BHC3™ Reliability for a key chemical production plant to avoid unplanned downtime. Potential system shutdowns and production downtime impact production availability and performance as well as product quality. At one chemical production plant, these issues contributed to more than $100 million per year in maintenance costs and lost revenue. The company engaged BakerHughesC3.ai to deploy BHC3 Reliability for identification of equipment failure risks, as well as risks from process upsets across the plant’s 16 production systems. The company was able to identify anomalous operations and receive early warning of critical issues with advance notice ranging from 10 days to 6 months.
Learn more about our technologies
BHC3™ Reliability provides reliability engineers, maintenance managers, and executives with comprehensive monitoring across critical and non-critical assets and enables proactive, AI-based predictive maintenance. BHC3 Reliability identifies anomalous behavior across systems and assets, provides prioritized alerts, recommends prescriptive actions, and enables collaboration through an integrated workflow. With BHC3 Reliability, enterprises can maximize uptime, reduce maintenance costs, and improve operational efficiency.
BHC3 Sustainability helps sustainability, plant, and energy managers achieve targets for energy cost, GHG emissions, water consumption, and waste reduction. The application models energy efficiency and emissions at every level of industrial processes from the individual equipment up to the plant as well as SKU-level product carbon footprints. Advanced AI models identify opportunities for energy efficiency, prioritize emission reduction strategies, alert operators to efficiency anomalies, and verify progress against sustainability goals.
BHC3 Process Optimization applies advanced analytic techniques on top of operational data to improve production yield and process efficiency. BHC3 Process Optimization identifies process anomalies across systems, provides prioritized alerts and process KPIs, enables root cause analysis, and recommends optimization actions. Process and production engineers can use BHC3 Process Optimization to maximize production levels and optimize energy efficiency.
The BHC3 AI Suite uses a unique model-driven architecture to accelerate delivery and reduce the complexities of developing enterprise AI applications. The BHC3 AI model-driven architecture provides an “abstraction layer,” that allows developers to build enterprise AI applications by using conceptual models of all the elements an application requires, instead of writing lengthy code.
