The Challenge
A subsurface team needs to quickly locate seismic datasets, technical reports, and contract information for a new evaluation area.
The organisation’s archive contains thousands of files across multiple storage environments with inconsistent metadata, varying naming conventions, and limited searchable context. Locating trusted information requires significant manual effort and specialist knowledge.
As a result, teams spend valuable time searching, validating, and organising data before interpretation work can begin.
Improving Archive Discovery Workflows
Exploration Archives AI helps teams improve archive discovery and metadata enrichment by introducing AI-assisted capabilities directly into existing workflows.
Rather than replacing operational processes, the platform automates repetitive, high-volume tasks while keeping experts in control of review and decision-making.
Using Exploration Archives AI, the team can:
- Ask questions in plain language to locate relevant content
- Automatically classify incoming files
- Scan SEG-Y datasets and extract metadata
- Extract key details from contract documents into searchable fields
These capabilities help teams reduce manual effort while improving visibility and consistency across archive environments.
Key Capabilities Used
AI Archive Assistant
Search archive content and metadata using natural language questions to quickly locate relevant information across large archive environments.
Automated File Classification
Automatically classify files to improve metadata consistency and reduce manual tagging effort at scale.
SEG-Y Metadata Scanning & Classification
Scan SEG-Y datasets, extract metadata, and classify seismic files to improve searchability and archive visibility.
Contract Metadata Extraction
Extract key contract information into structured, searchable fields to improve accessibility and reduce manual document review effort.
Result
By combining archive discovery, metadata enrichment, and AI-assisted classification, the organisation improves how teams interact with existing archive environments.
Key outcomes include:
- Faster access to trusted subsurface information
- Reduced manual effort in archive workflows
- Improved metadata consistency and searchability
- Better reuse of existing archive content
Most importantly, these improvements are achieved without replacing existing workflows or compromising governance and expert oversight.
Explore Exploration Archives AI
Learn how Exploration Archives AI can support archive discovery, metadata enrichment, and operational workflow efficiency across subsurface data environments.
