Ontological Frameworks for Petroleum Data Aggregation
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Solution Overview
Problem
Large-scale petroleum engineering systems face challenges in efficiently managing and accessing disparate data sources, leading to cumbersome data retrieval and suboptimal decision-making due to the complexity and heterogeneity of data sets across various databases.
Innovation Solution
The implementation of ontological frameworks and representation learning techniques using deep models like convolutional neural networks (CNNs) for aggregating data from disparate sources, enabling unified semantic querying and improved recommendation systems in petroleum engineering domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional recommendation algorithms (such as rule-based reasoning and collaborative filtering) are used for petroleum exploration data, then implementation is straightforward, but the techniques prove suboptimal when deployed to extremely massive and complex PE data sets
Solution Approach 1:
The patent introduces an ontological framework as an intermediary layer between disparate petroleum engineering data sources and analysis systems. This framework provides standardized semantic mappings and relationships, enabling traditional algorithms to effectively process massive complex datasets without requiring complete system redesign. The ontology acts as a mediator that translates heterogeneous data into a unified semantic structure.
Solution Approach 2:
The patent transforms the approach by changing from traditional algorithmic parameters to ontological semantic parameters. Instead of relying on statistical patterns from collaborative filtering, the system uses ontological relationships and semantic meanings to enhance recommendation effectiveness. This parameter transformation allows the system to handle massive complex PE datasets more effectively.
2Loss of information
If data is scattered over many disparate sources and databases, then comprehensive data coverage is achieved, but data retrieval is time- and resource-consuming
Solution Approach 1:
The ontological framework provides universal semantic mappings that work across all disparate petroleum engineering data sources. A single ontology model can represent and query multiple data types and sources uniformly, eliminating the need for source-specific retrieval logic. This multi-functionality enables comprehensive data coverage while maintaining efficient retrieval through standardized semantic queries.
Solution Approach 2:
The ontology serves as an intermediary layer that sits between disparate data sources and query systems. It provides unified semantic access points that translate diverse data source structures into consistent ontological representations, enabling fast retrieval without sacrificing data completeness from multiple sources.
3Ease of manufacture
If commercial knowledge graph building tools are used, then knowledge graph construction is simplified, but only a small part of the challenge is addressed
Solution Approach 1:
The patent segments the knowledge engineering task into distinct ontological categories (Things, Events, Methods) with specific aggregation functions for each. This segmentation allows systematic construction of comprehensive ontological frameworks while maintaining ease of implementation through modular category-based approaches. Each category can be built independently using appropriate aggregation functions.
Solution Approach 2:
The ontological framework is designed to be dynamic and extensible, allowing it to adapt to different petroleum engineering domains and data types. The framework can evolve to incorporate new categories, relationships, and aggregation functions as needed, providing both ease of construction and comprehensive solution coverage across diverse PE applications.
Data Source
AI summary
Systems and methods include a method for aggregating source data to form ontological frameworks. Aggregation functions are defined for ontological frameworks modeling categories of components of a facility. Each aggregation function defines a target component selected from a Things category, an Events category, and a Methods category. Defining the target component includes aggregating information from one or more components selected from one or more of the Things category, the Events category, and the Methods category. Source data is received in real-time from disparate sources and in disparate formats. The source data provides information about the components of the facility and external systems with which the facility interacts. Using the aggregation functions, the source data is aggregated to form the ontological frameworks. Each ontological framework models a component of the Things category, a component of the a Events category, or a component of the Methods category.