Capgemini and AWS Enhance Oilfield Production Efficiency with AI-Powered Digital Twin
Oil and gas operations face increasing pressure to improve efficiency, make smarter decisions, and foster collaboration across teams. As the energy sector shifts focus on sustainability, maximizing output from existing assets becomes essential. Capgemini, leveraging AWS technology, introduces the Production Optimization Oilfield Digital Twin—a solution that transforms how oilfields operate both onshore and offshore.
The Challenge
Oilfields produce vast amounts of data from SCADA systems, historians, legacy platforms, and vendor-specific tools. Much of this data remains siloed, limiting its usefulness and slowing decision-making. Two key issues illustrate this problem:
- Onshore wells: Around 80% rely on electric submersible pumps (ESPs) as their main artificial lift system. These ESPs are maintained by multiple service providers using varied tools and workflows, which creates data silos and reactive maintenance. Without integrated, real-time data analysis, non-productive time (NPT) rises and ESP failures cause production delays.
- Offshore operations: Most offshore oilfields are mature brownfields managed with fluid injection methods like CO₂, N₂, and chemical injections. These reservoir management techniques depend on fragmented tools and manual calculations, making it difficult to dynamically optimize production strategies and often leading to inefficiencies.
How the Digital Twin Solution Helps
Capgemini’s Production Optimization Digital Twin uses AI and combines IT and OT data to provide a unified, contextualized view of operations. This platform delivers AI-driven insights and automates workflows, supporting both onshore and offshore assets.
Key Features
- Easy integration and data ingestion: Collects real-time sensor data and system information, ensuring accurate and current operational views.
- Unified data fabric: Brings together data from OSDU, SCADA, PI historians, legacy systems, and vendor platforms into one accessible interface.
- AI-powered analytics: Applies machine learning and domain physics to predict equipment failures, suggest corrective actions, and optimize production settings.
- Automated engineering workflows: Digitizes processes like nodal analysis, decline curve analysis, and equipment scheduling to minimize manual tasks and speed up decisions.
- Cloud-native scalability: Built on AWS, it offers high performance, scalability, and seamless integration with existing infrastructure.
Value Delivered to Operations
Deploying this digital twin solution brings measurable benefits by breaking down data silos and streamlining workflows:
- Faster well analysis: Engineering teams cut analysis time for underperforming or candidate wells from 7 hours to just 1.5 hours.
- Increased production: Onshore sites can boost hydrocarbon output by 1-1.5%, while offshore operations may see gains of 1-2%.
- Improved efficiency: Enhances well lift performance and supports better equipment health and longevity.
Capgemini and AWS Partnership
With 15 years of collaboration, Capgemini and AWS combine deep industry expertise and cloud technology to help clients modernize operations. Capgemini’s extensive experience in system integration complements AWS’s scalable infrastructure, enabling a smooth transition to cloud-powered solutions. Backed by 11,000 AWS-certified consultants and multiple AWS competencies, this partnership supports clients in deploying AI-powered digital twins efficiently and effectively.
Conclusion
The Production Optimization Digital Twin from Capgemini, powered by AWS, offers a practical way to enhance oilfield operations. By integrating data and applying AI-driven insights, it helps operations teams reduce downtime, optimize production, and extend equipment life. As oil and gas fields mature, digital twins will play a critical role in operational excellence and sustainable resource management.
To learn more about implementing AI solutions in operations, explore specialized AI courses at Complete AI Training.
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