Flow Network Event Segmentation for Compact Production Records
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Solution Overview
Problem
Optimizing the performance of oil and gas flow networks is challenging due to unpredictable variations in inputs and the complexity of modeling multiple wells, leading to cumbersome simulations and suboptimal production settings, with existing methods requiring excitations and having limited application.
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 without the need for specific excitations, enabling better predictive maintenance and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulations and models are used to predict flow network response, then prediction capability 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 each interval as stable or transient. This segmentation allows the system to apply different analysis methods to different operational phases, reducing overall model complexity while maintaining prediction accuracy for critical events.
Solution Approach 2:
Instead of continuously applying complex simulations to all data, the patent applies detailed analysis only to identified transient events and stable periods, using simpler methods for other intervals. This partial action approach reduces computing power requirements while capturing the most significant operational characteristics.
2Loss of information
If continuous data monitoring is implemented, then operational insight is improved, but data volume and storage requirements increase
Solution Approach 1:
The patent extracts and isolates specific transient events and stable periods from the continuous data stream, separating these critical intervals from the rest of the data. This extraction allows focused analysis on important operational phases while reducing the volume of data that requires detailed processing and storage.
Solution Approach 2:
The system performs preliminary categorization of data into stable and transient intervals before detailed analysis. This preliminary action identifies which data segments require intensive processing and which can be handled more efficiently, reducing overall computational and storage requirements.
3Measurement precision
If excitations are applied to identify flow network variations, then system identification accuracy is improved, but operational disruption and complexity increase
Solution Approach 1:
The patent enables the flow network to identify its own characteristics by analyzing naturally occurring transient events and stable periods during normal operation. This self-service approach eliminates the need for external excitations, as the system uses its own operational variations to build accurate system identification models without causing operational disruption.
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).