Transforming Seismic Data Analysis with Generative AI: A Partnership Between Halliburton and AWS
Streamlining Complex Workflow Creation through Natural Language Interaction
Enhancing Accessibility and Efficiency in Energy Exploration
Leveraging Amazon Bedrock for Seamless Conversational Interfaces
Unveiling the AI-Powered Assistant: A Proof of Concept
Revolutionizing Workflow Generation: Technical Insights and Results
Conclusion: A New Era for Seismic Data Processing
Transforming Seismic Data Analysis with Generative AI
Seismic data analysis plays a critical role in energy exploration, yet traditionally posed challenges when configuring intricate processing workflows. Halliburton’s Seismic Engine, a robust cloud-native application for this purpose, previously required users to manually set up nearly 100 specialized tools—an arduous, error-prone task that often demanded deep expertise and limited accessibility.
To enhance the workflow experience, Halliburton partnered with the AWS Generative AI Innovation Center to develop an AI-powered assistant for the Seismic Engine. This innovative solution leverages Amazon’s suite of tools—Amazon Bedrock, Bedrock Knowledge Bases, Amazon Nova, and Amazon DynamoDB—to simplify the complex process of workflow creation, enabling geoscientists and data scientists to interact using natural language.
Streamlining Workflow with Conversational AI
Imagine a world where the arduous task of configuring seismic workflows is transformed into simple conversations. Our initiative focuses on two primary objectives: converting natural language queries into executable seismic workflows and establishing an intelligent Q&A system for the Seismic Engine documentation.
We built a proof-of-concept using Amazon Bedrock that allows geoscientists to chat with complex seismic tools naturally. This integration dramatically reduces the time required for building workflows, enabling users to focus on interpretation and analysis rather than configuration.
According to Phillip Norlund, Manager of Subsurface Technologies at Halliburton Landmark, this generative AI-powered tool has significantly improved efficiency and accessibility for users of various expertise levels.
Technical Overview
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Architecture: The backbone of our system is a FastAPI application hosted on AWS App Runner. User queries are processed through a streaming interface, where an intent router powered by Amazon Nova Lite analyzes requests to identify whether they pertain to workflow generation or technical information.
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Natural Language Processing: Upon receiving a user query, the intent router classifies it into three categories: “Workflow_Generation,” “QnA,” and “General_Question.” This classification ensures that the system provides targeted responses, enhancing user experience.
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Question Answering: For Q&A, the solution utilizes Amazon Bedrock Knowledge Bases, allowing us to efficiently manage and retrieve relevant documentation. This alleviates operational overhead and enables real-time responses to user queries.
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Workflow Generation: When generating workflows, the system employs a large language model (LLM) that translates natural language into executable YAML workflows, choosing from a pool of 82 available tools. The model considers parameters such as input data, processing requirements, and desired outputs, crafting tailored workflows that meet user needs.
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Multi-turn Conversations: Amazon DynamoDB supports context retention, permitting multi-turn interactions where users can refine or alter their workflows through further inputs.
Evaluation Outcomes
Despite the complexity of the workflows, our solution has demonstrated remarkable success:
- Efficiency: The generative AI assistant achieved workflow generation success rates of 84-97%, a significant improvement over traditional methods.
- Time Reduction: Where building workflows used to take minutes, our solution can generate them in mere seconds—achieving over a 95% reduction in creation time.
| Model | Complexity | Success Rate | Mean Generation Time (s) |
|---|---|---|---|
| Claude Haiku 3.5 | Simple | 84% | 8.3 |
| Medium | 90% | 12.4 | |
| Claude Sonnet 3.5 V2 | Simple | 86% | 11.2 |
| Medium | 97% | 15.8 |
Through empirical evaluation, we found that users of varying experience levels—whether new or seasoned—could enhance productivity and accuracy using our system.
Conclusion
Our partnership with AWS showcases how generative AI can revolutionize complex workflows, making advanced geophysical tools much more accessible. By enabling natural language interactions and intelligent Q&A systems, we have fundamentally improved productivity and efficiency in subsurface workflows.
As we continue to refine our system, we encourage other industries facing similar technical challenges to explore the potential of generative AI solutions. The groundwork laid in seismic data analysis is merely the beginning; the possibilities are boundless.
About the Authors
This project was a collaborative effort involving experts like Yuan Tian, Di Wu, Gan Luan, and others from the AWS Generative AI Innovation Center, each contributing unique expertise to create impactful solutions for real-world applications. Their collective efforts highlight the importance of interdisciplinary collaboration in reaching innovative milestones.
With this blog post, the intricate technicalities of the project and its significance in the broader context of seismic data analysis are articulated clearly, making it suitable for a wide-ranging audience, from tech enthusiasts to industry professionals.