Back office ops · Production

How Infosys built a generative AI solution to process oil and gas drilling data with Amazon Bedrock

The problem

Oil and gas operations generate vast amounts of complex multimodal technical documents — well completion reports, drilling logs, and lithology diagrams — that conventional non-AI processing methods fail to handle due to specialized terminology, interconnected data relationships, and mixed text and image formats, resulting in inefficient data extraction and time-consuming manual processing.

First attempt

Three iterative RAG approaches were tried before the final design: the initial image-analysis approach worked for text but failed on image-related queries; ColBERT multi-vector embeddings proved difficult to store and manage; and fixed-size chunking improved keyword retrieval but produced fragmented long-form answers by splitting related information across chunks.

Workflow diagram · grounded in source
1
Technical document ingestion
trigger
“well completion reports, drilling logs, and intricate lithology diagrams, contain crucial information that drives operational decisions and strategic planning”
2
Multimodal image analysis
ai_action
“Over a thousand such technical images (including lithology diagrams, well completion charts, and drilling visualizations) were preprocessed using Amazon Nova Pro, a multimodal language model. An iterative prompting strategy was employed …”
3
Hierarchical chunking and embedding
ai_action
“The parent-child chunking hierarchy was retained with parent chunks of 1,200 tokens and child chunks of 512 tokens. We continued using Cohere English embeddings”
4
OpenSearch vector storage
integration
“The processed content (chunked text and complete image descriptions) was ingested into an OpenSearch Serverless vector database”
5
Hybrid search retrieval
ai_action
“This approach combined the strengths of semantic search and traditional keyword-based search”
6
BGE result reranking
ai_action
“implemented a BGE reranker to refine search results”
7
Domain-specific response generation
output
“Amazon Nova model for domain-specific response generation”
Reported outcome

The final hybrid RAG solution achieved 92% retrieval accuracy against a human expert baseline, under 2-second average query response time, a 4.7/5 user satisfaction rating from field engineers and geologists, a 40–50% decrease in manual document processing costs, and field engineers spending 60% less time searching for technical information.

Reported metrics
Average query response timeLess than 2 seconds
Retrieval accuracy92%
User satisfaction rating4.7/5
Manual document processing cost reduction40–50%
Show all 6 reported metrics
average query response timeLess than 2 seconds
retrieval accuracy92%
user satisfaction rating4.7/5
manual document processing cost reduction40–50%
engineer time searching for technical information60%
decision-making timeSignificant reduction in decision-making time
Reported stack
Amazon BedrockAmazon Bedrock Nova ProAmazon Bedrock Knowledge BasesAmazon OpenSearch ServerlessAmazon Titan Text EmbeddingsCohere Embed English modelBGE RerankerAmazon Q DeveloperPyMuPDFOpenCVInfosys TopazColBERT
Source
https://aws.amazon.com/blogs/machine-learning/how-infosys-built-a-generative-ai-solution-to-process-oil-and-gas-drilling-data-with-amazon-bedrock?tag=soumet-20
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The final hybrid RAG solution achieved 92% retrieval accuracy against a human expert baseline, under 2-second average query response time, a 4.7/5 user satisfaction rating from field engineers and geologists, a 40–50%…

What tools did this team use?

Amazon Bedrock, Amazon Bedrock Nova Pro, Amazon Bedrock Knowledge Bases, Amazon OpenSearch Serverless, Amazon Titan Text Embeddings, Cohere Embed English model, BGE Reranker, Amazon Q Developer, PyMuPDF, OpenCV.

What results were reported?

Average query response time: Less than 2 seconds; Retrieval accuracy: 92%; User satisfaction rating: 4.7/5; Manual document processing cost reduction: 40–50% (source-reported, not independently verified).

What failed first in this deployment?

Three iterative RAG approaches were tried before the final design: the initial image-analysis approach worked for text but failed on image-related queries; ColBERT multi-vector embeddings proved difficult to store and…

How is this back office ops AI workflow structured?

Technical document ingestion → Multimodal image analysis → Hierarchical chunking and embedding → OpenSearch vector storage → Hybrid search retrieval → BGE result reranking → Domain-specific response generation.

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