Agentic Workflows: Powering Intelligent Energy Solutions

Understanding AI agents

AI agents are autonomous software entities designed to perform specific tasks or solve particular problems. In the our AI platform, these agents work collaboratively to tackle complex energy industry challenges:

1. Specialized expertise: Each agent focuses on a specific domain or task
2. Adaptive Learning: Agents improve their performance over time through experience
3. Collaborative problem solving: Multiple agents can work together on complex tasks
4. Real-time decision making: Agents can respond quickly to changing conditions

Agentic workflows in action

Our platform leverages agentic workflows to streamline complex processes in the energy sector:

1. Task decomposition: The AI Orchestration Hub breaks down complex queries into subtasks
2. Agent assignment: Specialized agents are assigned to each subtask based on their expertise
3. Parallel processing: agents work simultaneously on different aspects of the problem
4. Information sharing: share results and insights through a common knowledge base
5. Result synthesis: Orchestration Hub combines agent outputs into a coherent solution

Case study: Field development planning

Let's explore how agentic workflows revolutionize field development planning (FDP):

FDP workflow example

1. Data collection agent: Gathers relevant geological and production data
2. Simulation parameter agent: Automates the generation of simulation parameters
3. Reservoir analysis agent: Conducts in-depth analysis of reservoir characteristics
4. Compliance agent: Ensures adherence to industry regulations and best practices
5. Optimization agent: Determines optimal well placement and production strategies
6. Scenario evaluation agent: Rapidly assesses multiple development scenarios

This agentic approach enables engineers to quickly generate and assess various field development options, leading to more informed decision making and improved resource allocation. Our advanced AI agents are designed to work in harmony with the our established simulation software like Intersect™ high-resolution reservoir simulator.

The power of collaboration

Agentic workflows in the our AI platform offer several key advantages:

1. Enhanced problem-solving: Combine diverse AI capabilities to tackle complex challenges
2. Scalability: Easily add or modify agents to address new requirements
3. Continuous improvement: Agents learn and adapt, improving overall system performance
4. Flexibility: Quickly reconfigure workflows to address changing industry needs
5. Domain expertise integration: Leverage industry-specific knowledge in AI processes

Adapting to AI advancements

Our generative AI (GenAI) platform is designed to evolve with the rapidly changing AI landscape:

1. Flexible model integration: Easily incorporate new AI models as they become available
2. Continuous learning: Agents adapt to new data and methodologies
3. Scalable architecture: Expand capabilities without overhauling existing systems
4. Performance benchmarking: Regularly assess and optimize agent performance

The future of energy AI

As the energy landscape evolves, the SLB agentic AI workflows are poised to drive innovation across the sector. From optimizing renewable energy systems to improving subsurface imaging for exploration, our AI agents are ready to tackle the industry's most pressing challenges.By choosing our GenAI platform, energy companies gain access to cutting-edge technology backed by decades of domain expertise. We offer not just a tool, but a partnership to navigate the complex landscape of AI-driven innovation in energy.