Subsurface Data Trust Visualization With Version Comparison
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Solution Overview
Problem
The energy industry faces challenges in managing and utilizing subsurface data due to data silos and the absence of standardized formats, leading to inefficient workflows and hindered decision-making capabilities.
Innovation Solution
A method and system for contextual visualization of trustworthiness in subsurface data, utilizing a version train view, stamp view, and comparison view to ensure transparent data management and integration, with user interfaces that allow domain experts to validate and certify data for specific uses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based open-source frameworks are used to standardize data management, then data standardization is improved, but data consolidation time increases to several days or weeks
Solution Approach 1:
The system performs preliminary actions by pre-processing and standardizing data as it is ingested into the data lake, rather than waiting until consolidation time. Data is tagged with metadata, quality scores, and trust indicators during initial ingestion, so that when consolidation is needed, the work is already partially done. This eliminates the need for time-consuming post-hoc standardization efforts.
Solution Approach 2:
The patent replaces manual mechanical data consolidation processes with automated computational systems. Machine learning algorithms automatically assess data quality, assign trust scores, and match data across sources without human intervention. This substitution of automated systems for manual processes dramatically reduces consolidation time from weeks to minutes or hours.
2Loss of information
If data is aggregated at a centralized location, then data availability is improved, but data search time increases significantly
Solution Approach 1:
The system segments the centralized data lake into organized collections grouped by domain, project, or data type. Each segment is indexed with detailed metadata tags. When a search is performed, the system queries only relevant segments rather than scanning the entire data lake, dramatically reducing search time while maintaining centralized data availability.
Solution Approach 2:
The patent introduces an intermediary indexing layer between the centralized data storage and user queries. Metadata indexes and data catalogs act as intermediaries that map user search requests to actual data locations. This intermediary layer enables rapid retrieval from the centralized repository without requiring users to search through all aggregated data manually.
3Quantity of substance
If conventional data management systems are used, then data storage is achieved, but data conversion costs and time increase
Solution Approach 1:
The system implements a universal data lake architecture that can store multiple data types and formats from various sources without requiring format-specific conversion processes. The standardized data model and schema registry enable heterogeneous data to be ingested and queried uniformly, eliminating the need for expensive and time-consuming format conversions while maintaining comprehensive data storage capacity.
4Ease of operation
If user interfaces do not indicate data suitability, then interface simplicity is maintained, but data trustworthiness assessment deteriorates
Solution Approach 1:
The patent employs color-coded visual indicators in the user interface to communicate data quality and trustworthiness. Data elements are displayed with color tags (e.g., green for high quality, yellow for moderate, red for low quality) based on automated quality assessments. This visual encoding allows users to quickly assess data reliability without complicating the interface, maintaining simplicity while providing rich trustworthiness information.
Solution Approach 2:
The system implements self-service data quality assessment where the interface automatically displays trust indicators, quality scores, and suitability flags for each data element without requiring users to manually verify or query data quality attributes. The system serves itself by automatically annotating data with quality metadata and presenting this information in the UI, enabling users to make informed decisions without adding interface complexity.
Data Source
AI summary
A method implements contextual visualization of trustworthiness for subsurface data. The method involves displaying a version train view for a hierarchical collection of a set of subsurface data. The version train view includes a version train element of a first version of the hierarchical collection. The version train element displays stamp data. The method further involves displaying a comparison view with a first version view of the first version and a second version view of a second version of the hierarchical collection. The method further involves comparing the first version to the second version to generate difference data. The method further involves updating the first version view and the second version view with the difference data to form an updated first version view and an updated second version view. The method further involves displaying the updated first version view and the updated second version view.