Graph Representation Learning for Petroleum Network Data
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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 limitations of traditional recommendation algorithms and data integration techniques.
Innovation Solution
The implementation of a computer-implemented method that aggregates data from disparate sources into ontological frameworks, creating an abstraction layer for unified semantic querying and knowledge discovery, using graph/network computation and representation learning for recommendation and advisory systems, which includes smart agents for decision optimization and reservoir modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional recommendation algorithms (rule-based reasoning and collaborative filtering) are used for petroleum exploration data, then implementation is straightforward, but performance is suboptimal on extremely massive and complex PE data sets
Solution Approach 1:
The patent transforms the data representation parameters by converting relational database records into knowledge graph structures with entities, relationships, and properties. This parameter change enables more effective processing of massive PE datasets by representing complex relationships (spatial, temporal, causal) in a structured format that supports advanced querying and reasoning, thereby improving both productivity and reliability of decision-making
Solution Approach 2:
The patent introduces an intermediary layer (knowledge graph and semantic modeling framework) between the raw petroleum exploration data and the recommendation algorithms. This intermediary transforms disparate data from heterogeneous sources into a unified semantic representation, enabling more accurate and efficient recommendation systems that can handle the complexity of massive PE datasets while maintaining implementation feasibility
2Quantity of substance
If data is stored in disparate sources and databases, then data coverage is comprehensive, but data retrieval is time- and resource-consuming
Solution Approach 1:
The patent merges data from multiple disparate sources into a unified knowledge graph structure that preserves comprehensive data coverage while enabling efficient retrieval. By integrating entities, relationships, and properties from heterogeneous databases into a single semantic framework, the system maintains complete data coverage but reduces retrieval time through optimized graph-based querying and pre-computed relationships
Solution Approach 2:
The patent creates a universal knowledge graph framework that serves multiple functions simultaneously: data storage, data integration, semantic querying, and relationship analysis. This multi-functional system handles comprehensive data from disparate sources while providing unified access points and optimization mechanisms that reduce retrieval time across all data types and query patterns
3Ease of operation
If ontological frameworks are created to unify data, then semantic querying capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the ontological framework into distinct layers: data model layer (entities, relationships, properties), semantics layer (schemas, vocabularies, constraints), and application layer (queries, reasoning, visualization). This segmentation improves semantic querying capability by providing clear abstraction levels while managing system complexity through modular design that allows independent development and optimization of each layer
Data Source
AI summary
Systems and methods include a method for providing recommendations and advisories associated with a facility. Source data is received in real-time from disparate sources and in disparate formats. The source data provides information about a facility and external systems with which the facility interacts. The source data is aggregated to form ontological frameworks. Each ontological framework models a category of components selected from components of a Things category, components of an Events category, and components of a Methods category. An abstraction layer is created based on the ontological frameworks. The abstraction layer includes abstractions that support queries, ontologies, metadata, and data mapping. A knowledge discovery layer for discovering knowledge from the abstraction layers is provided. Discovering the knowledge includes graph/network computation, graph/network training and validation, and graph representation learning. A recommendation and advisory systems layer is provided for providing recommendations and advisories associated with the facility.