Flow Network Data Categorization for Compact Performance Recording
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Solution Overview
Problem
Optimizing the performance of oil and gas flow networks is challenging due to unpredictable variations in input conditions and the complexity of modeling multiple wells, leading to cumbersome simulations and suboptimal production settings.
Innovation Solution
A method for recording and categorizing data on oil and gas flow network performance by identifying stable and transient events over time intervals, allowing for compact statistical representation and analysis, which can be used to assess and improve network operations without the need for specific excitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed simulations and models are used to predict flow network response, then prediction accuracy is improved, but computing power requirements and model complexity increase significantly
Solution Approach 1:
The patent segments the continuous flow network operation into discrete time intervals, categorizing them as stable production intervals or transient events. This segmentation allows the system to apply different analysis methods to different intervals, using simplified statistical representations for stable intervals and targeted analysis for transient events, thereby reducing overall model complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent extracts and removes transient events from the continuous data stream, separating them from stable production intervals. By taking out the transient portions and analyzing them separately, the system avoids the need for complex continuous simulations during stable periods, reducing computing power requirements while preserving the ability to accurately predict network response when transients occur.
2Loss of information
If comprehensive data is collected for flow network analysis, then analysis completeness is improved, but data volume and processing requirements increase
Solution Approach 1:
The patent extracts only the essential features and statistical characteristics from large volumes of raw flow network data, rather than processing all raw data points. By taking out and retaining only the meaningful patterns and deviations, the system maintains analysis completeness while dramatically reducing data volume for subsequent processing and storage.
Solution Approach 2:
The patent transforms raw flow network data into statistical parameters and categorical representations. By changing the parameter form from continuous raw measurements to discrete statistical summaries and event categories, the system preserves the essential information needed for analysis while reducing data volume and simplifying processing requirements.
3Reliability
If continuous monitoring of flow network parameters is implemented, then system performance assessment is improved, but computational resources and time consumption increase
Solution Approach 1:
The patent segments continuous monitoring into discrete time intervals with categorical labels (stable production vs. transient events). This segmentation allows the system to perform lightweight classification at interval boundaries rather than continuous computation, maintaining reliable system performance assessment while significantly reducing computational time and resource consumption.
4Productivity
If excitation-based methods are used for flow network optimization, then performance improvement is achieved, but operational complexity and restrictions increase
Solution Approach 1:
The patent enables the flow network system to self-analyze and self-optimize by automatically categorizing its own operational data and identifying performance patterns. Instead of requiring external excitation inputs, the system uses its own operational variations and transitions to generate optimization insights, reducing operational complexity while maintaining the ability to achieve performance improvements.
Data Source
AI summary
A method for recording data relating to the performance of an oil and gas flow network uses statistical data to represent raw data in a compact form. Categories are assigned to time intervals in the data. The method comprises: (1) gathering data covering a period of time, wherein the data relates to the status of one or more control point(s) within the flow network and to one or more flow parameter(s) of interest in one or more flow path(s) of the flow network; (2) identifying multiple time intervals in the data during which the control points and the flow parameter(s) can be designated as being in a category selected from multiple categories; (3) assigning a selected category of the multiple categories to each one of the multiple datasets that are framed by the multiple time intervals; and (4) extracting statistical data representative of some or all of the datasets identified in step (2) to thereby represent the original data from step (1) in a compact form including details of the category assigned to each time interval in step (3).