Seismic information evaluation is a vital part of power exploration, however configuring advanced processing workflows has historically been a time-consuming and error-prone problem. Halliburton’s Seismic Engine, a cloud-native software for seismic information processing, is a robust instrument that beforehand required handbook configuration of roughly 100 specialised instruments to create workflows. This course of was not solely time-consuming but additionally required deep experience, probably limiting the accessibility and effectivity of the software program.
To deal with this problem, Halliburton partnered with the AWS Generative AI Innovation Middle to develop an AI-powered assistant for Seismic Engine. The answer makes use of Amazon Bedrock, Amazon Bedrock Data Bases, Amazon Nova, and Amazon DynamoDB to remodel advanced workflow creation into conversations. Geoscientists and information scientists can configure processing instruments via pure language interplay as a substitute of handbook configuration.
On this publish, we’ll discover how we constructed a proof-of-concept that converts pure language queries into executable seismic workflows whereas offering a question-answering functionality for Seismic Engine instruments and documentation. We’ll cowl the technical particulars of the answer, share analysis outcomes displaying workflow acceleration of as much as 95%, and focus on key learnings that may assist different organizations improve their advanced technical workflows with generative AI.
Our collaboration with AWS has been instrumental in accelerating subsurface interpretation workflows. By integrating Amazon Bedrock companies with Halliburton Landmark’s DS365 Seismic Engine, we have been capable of scale back historically time‑consuming workflow‑constructing duties by an order of magnitude. This generative AI–powered workflow assistant not solely improves effectivity and accuracy but additionally makes our superior geophysical instruments extra accessible to a broader vary of customers. The scalable cloud‑native structure on AWS has enabled us to ship a seamless, conversational expertise that essentially improves productiveness throughout subsurface workflows.
— Phillip Norlund, Supervisor of Subsurface Applied sciences, Halliburton Landmark
— Slim Bouchrara, Senior Product Proprietor of Subsurface R&D, Halliburton Landmark
Answer overview
Our undertaking aimed to deal with two key targets: remodeling pure language queries into executable seismic workflows, and offering an clever query and reply (Q&A) system for Seismic Engine documentation. To attain this, we developed an answer utilizing Amazon Bedrock that allows geoscientists to work together with advanced seismic instruments via pure dialog.The spine of our system is a FastAPI software deployed on AWS App Runner, which handles consumer queries via a streaming interface. When a consumer submits a question, an intent router powered by Amazon Nova Lite analyzes the request to find out whether or not it’s searching for workflow technology or technical info. For Q&A requests, the system makes use of Amazon Bedrock Data Bases with Amazon OpenSearch Serverless to supply related solutions from listed documentation. For workflow requests, a technology agent utilizing Anthropic’s Claude on Amazon Bedrock creates YAML workflows by choosing from 82 out there Seismic Engine instruments.
To take care of context and allow multi-turn conversations, we built-in Amazon DynamoDB for chat historical past and interplay logging. The system helps streaming responses for each Q&A and workflow technology, offering speedy suggestions to customers because the system processes their requests. This structure permits advanced technical workflows to be created and modified via pure dialog, whereas sustaining the exact management required for seismic information processing. The next diagram illustrates the answer structure.
Question routing and intent classification
After the consumer’s question is supplied to the system, the Intent Router classifies the intent label of the given question by calling Amazon Nova Lite by way of the Amazon Bedrock API. The massive language mannequin (LLM) is given a immediate to provide one in all three intent labels: “Workflow_Generation”, “QnA”, and “General_Question”.The “Workflow_Generation” label is used to route queries associated to workflow technology, together with studying/loading datasets, information processing operations, and numerous requests involving manipulating particular datasets. The “QnA” intent label is used for questions associated to particular instruments, requests for pattern workflows, or questions on Seismic Engine documentation. The “General_Question” label is reserved for queries unrelated to Seismic Engine operations or workflows.In our implementation, Amazon Nova Lite carried out the routing process effectively, providing an excellent steadiness between accuracy and latency.
Query answering implementation
The Q&A element handles Seismic Engine-related queries through the use of Amazon Bedrock Data Bases, a completely managed service for end-to-end Retrieval Augmented Era (RAG) workflow. We selected Bedrock Data Bases as a result of it alleviates the operational overhead of managing vector databases, chunking methods, and embedding pipelines. As a completely managed service, it handles infrastructure scaling, safety, and upkeep mechanically, in order that our workforce might concentrate on answer improvement somewhat than RAG infrastructure operations. The service gives native assist for a number of chunking methods together with hierarchical chunking, which maintains parent-child relationships to steadiness granular retrieval with broader doc context.The information sources embody instrument documentation markdown recordsdata and Seismic Engine manuals saved in S3. We saved instrument documentation recordsdata unchunked since they’re comparatively quick, preserving full context for particular person instruments. For longer paperwork like Seismic Engine manuals, we used hierarchical chunking with default settings. We use Amazon Titan Textual content Embeddings V2 for embedding technology and OpenSearch Serverless because the vector database. The system additionally shops metadata comparable to file names, URLs, and doc sorts for every listed merchandise for downstream use.For each retrieval and response technology, we use Amazon Bedrock Data Bases’ retrieve_and_generate API with Claude 3.5 Haiku because the mannequin. The system helps multi-turn conversations by sustaining session context, and responses are formatted with inline citations for enhanced traceability.
Notice: This answer was developed and evaluated utilizing Claude 3.5 Sonnet V2 and Claude 3.5 Haiku. Since then, these fashions have been succeeded by Claude Sonnet 4.5 and most lately Claude Sonnet 4.6, in addition to Claude Haiku 4.5, all out there via Amazon Bedrock. The answer structure helps mannequin upgrades with out code modifications, in an effort to use the newest mannequin capabilities.
This strategy allows our system to supply context-aware, related solutions to consumer queries about Seismic Engine instruments and workflows.
Workflow technology
For queries labeled as “Workflow_Generation”, our answer makes use of LLM brokers to transform pure language into executable YAML workflows. The agent is certain with 82 instruments out there on Seismic Engine. Based mostly on the consumer’s question and gear specs that outline inputs, parameters, and outputs, the agent selects acceptable instruments, determines their appropriate execution order, and generates a YAML workflow that addresses the consumer’s necessities. The next determine illustrates the workflow technology course of.
We used each Claude 3.5 Sonnet V2 and Claude 3.5 Haiku in our implementation, orchestrated via the LangChain framework for agent administration and gear binding. The fashions are supplied with detailed instrument descriptions and specs, in order that they’ll perceive every instrument’s capabilities and necessities. When producing workflows, the system considers not solely the specific necessities within the consumer’s question but additionally consists of obligatory default parameters when particular values aren’t supplied.The workflow technology course of helps multi-turn conversations, in order that customers can modify beforehand generated workflows via pure language requests. Through the use of dialog historical past saved in Amazon DynamoDB, the LLM can both generate new workflows or modify current ones in accordance with the consumer’s present question.
Analysis
To judge our answer’s effectiveness, we created a complete take a look at dataset of query-workflow pairs, consisting of each low and medium complexity workflows. These have been derived from actual historic workflows and validated by subject material specialists to confirm they precisely characterize typical consumer requests.
Workflow technology outcomes
| Mannequin | Complexity | Success Price | Imply Era Time (s) | Median Era Time (s) |
| Claude Haiku 3.5 | easy | 84% | 8.3 | 5.9 |
| medium | 90% | 12.4 | 9.1 | |
| Claude Sonnet 3.5 V2 | easy | 86% | 11.2 | 11.5 |
| medium | 97% | 15.8 | 16.6 |
Each fashions demonstrated robust efficiency, with Claude Sonnet 3.5 V2 displaying superior success charges, notably for medium complexity workflows. The system delivers responses via streaming, offering customers with speedy suggestions because the workflow is generated, with full workflows delivered inside 5.9-16.6 seconds. Claude Haiku 3.5 gives quicker technology instances, offering a trade-off possibility between velocity and accuracy.
Comparability to baseline efficiency
| Person Kind | % Success | % Failure | Time to Construct Easy Move (min) | Time to Construct Complicated Move (min) |
| New Person | 70% | 20% | 4 | 20 |
| Skilled Person | 85% | 10% | 2 | 5 |
| Our Answer | 84-97% | 3-16% | 0.13-0.26 | 0.21-0.28 |
Our generative AI answer demonstrates the next enhancements:
- Success charges of 84-97% surpass each new and skilled customers.
- Workflow creation time is diminished from minutes to seconds, representing over a 95% time discount.
These outcomes exhibit that customers throughout expertise ranges can improve productiveness by over 95%, whereas sustaining or exceeding the accuracy of handbook workflow creation.
Conclusion
On this publish, we confirmed how we used Amazon Bedrock to remodel advanced technical processes into pure conversations. By implementing an AI-powered assistant with built-in Q&A capabilities, we achieved workflow technology success charges of 84-97% whereas decreasing creation time by over 95% in comparison with handbook processes. The system’s means to deal with each low and medium complexity workflows, mixed with its contextual understanding of Seismic Engine instruments, demonstrates how generative AI can enhance industrial software program usability with out compromising accuracy.
This strategy additionally generalizes effectively to different domains with advanced, multi-step agentic workflows requiring specialised instrument information and configuration. As subsequent steps, take into account exploring multi-agent architectures utilizing frameworks like Strands Brokers SDK with Amazon Bedrock AgentCore for improved accuracy via specialised sub-agents.
In regards to the authors
