Hydrocarbon Well Visualization via Network Graph Clustering
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Solution Overview
Problem
Conventional approaches face challenges in integrating disparate datasets from various sources to generate a comprehensive visualization of hydrocarbon well operations, due to issues like non-matching data, missing information, and unstructured metadata.
Innovation Solution
The implementation utilizes a network graph to connect entities from different datasets by tokenizing well attributes and generating a similarity index, allowing for the identification of clusters representing potential hydrocarbon wells or groups of wells. This is further refined using geo-spatial autocorrelation and temporal analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches are used to integrate datasets from various sources, then data integration is attempted, but the integration fails due to non-matching data, missing information, and unstructured metadata
Solution Approach 1:
The patent employs an intermediary processing layer that includes tokenization and similarity index generation. This intermediary system acts as a mediator between disparate data sources and the final visualization, transforming unstructured metadata into standardized tokens and calculating similarity indices to match entities across datasets, thereby resolving integration failures without increasing overall system complexity
Solution Approach 2:
The patent changes the parameter state of metadata by tokenizing unstructured data into standardized discrete units. This transformation converts variable-length unstructured metadata into fixed-format tokens that can be systematically compared and matched, enabling reliable data integration across different sources with previously incompatible data structures
2Loss of information
If a comprehensive visualization of hydrocarbon well operations is generated, then better management and decision-making are enabled, but the complexity of integrating and processing disparate datasets increases
Solution Approach 1:
The patent segments the complex data integration process into distinct modular steps: tokenization of metadata, generation of similarity indices, entity matching across datasets, cluster identification, and temporal analysis. This segmentation allows each processing stage to be independently optimized and managed, reducing overall system complexity while maintaining comprehensive information integration
Solution Approach 2:
The patent replaces traditional mechanical data matching approaches with computational methods including tokenization algorithms and similarity index calculations. This substitution enables automated processing of large volumes of disparate data without manual intervention, maintaining information completeness while reducing processing complexity through algorithmic efficiency
Data Source
AI summary
A method for managing one or more hydrocarbon wells. The method comprises obtaining a well dataset for the one or more hydrocarbon wells and identifying one or more entities within the well dataset, wherein each of the one or more entities has at least one corresponding well attribute and corresponding entity dates. The method comprises generating a network graph of the one or more entities based on a similarity index between the one or more entities, wherein the similarity index utilizes the corresponding well attributes. The method comprises identifying a first cluster of the one or more entities within the network graph, wherein the one or more entities within the first cluster correspond to a first hydrocarbon well of the one or more hydrocarbon wells. The method comprises generating, on a display device, a visualization of the first hydrocarbon well including the one or more entities of the first cluster.