Drilling Data Aggregation with Synchronized Context Analysis
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Solution Overview
Problem
Current oilfield operations face challenges in effectively aggregating and analyzing data from various sources to optimize drilling operations, leading to inefficiencies and potential deviations from planned trajectories due to lack of real-time synchronization and contextual understanding.
Innovation Solution
A method for aggregating data from multiple sources, synchronizing timing, determining a drilling context, and analyzing synchronized data to generate insights for presenting to users, enabling real-time monitoring, optimization, collaboration, and automated control of drilling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple sources is collected for drilling operations, then the quantity and variety of information increases, but the complexity of data aggregation and synchronization increases
Solution Approach 1:
The patent segments data from multiple sources into distinct data streams, each processed and synchronized independently before aggregation. This modular approach manages complexity by handling each data source separately while maintaining overall data completeness through systematic integration.
Solution Approach 2:
The patent introduces a centralized data aggregation system that acts as an intermediary between multiple data sources and the drilling operation control system. This mediator synchronizes timing, standardizes formats, and integrates data from diverse sources, reducing the complexity burden on individual sources while ensuring complete information capture.
2Measurement precision
If real-time data synchronization is implemented, then drilling operation monitoring accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary timestamping of data as it enters the aggregation system, establishing a common time reference before processing. This preliminary action enables accurate synchronization without requiring intensive real-time computational adjustments, reducing processing time while maintaining timing precision.
Solution Approach 2:
The patent transforms multiple data streams into a standardized format with unified timing parameters. By changing the parameter representation to a common time-reference framework, the system achieves accurate synchronization without the computational overhead of complex real-time alignment algorithms.
3Loss of information
If contextual analysis of drilling data is performed, then operational insights and decision-making quality improve, but the complexity of data analysis increases
Solution Approach 1:
The patent creates a contextual framework that serves multiple analysis functions simultaneously. The drilling context structure can accommodate various types of operational data and analysis queries, providing comprehensive contextual understanding without requiring separate complex analysis systems for each function.
Solution Approach 2:
The patent implements a nested contextual structure where drilling context contains operational parameters, which in turn contain specific measurement data. This hierarchical nesting organizes complex information in manageable layers, enabling comprehensive contextual analysis while maintaining system simplicity through structured decomposition.
4Reliability
If multiple data sources are integrated into a unified system, then operational visibility and control improve, but the system complexity and integration challenges increase
Solution Approach 1:
The patent merges multiple data sources into a unified data aggregation system that maintains reliable operational monitoring. By combining data collection, synchronization, contextualization, and distribution functions into a single integrated platform, the system achieves comprehensive visibility and control while managing integration complexity through functional consolidation.
Data Source
AI summary
A method for aggregating data for a drilling operation. The method includes acquiring the data from a number of data sources associated with the drilling operation, synchronizing a timing of the data for aggregating the data to generate synchronized aggregated data, determining a drilling context based on the synchronized aggregated data, and assigning the determined drilling context to the synchronized aggregated data. The method further includes analyzing the synchronized aggregated data in the drilling context to generate an analysis and presenting the analysis to at least one user.