Flow Network Assessment Using Steady-State Data Compression
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Solution Overview
Problem
Existing methods for assessing oil and gas flow networks are cumbersome, require significant computing power, and often provide only a 'computer-assisted guess' for optimal settings, due to the complexity of modeling unpredictable inputs and varying capacities.
Innovation Solution
A method that gathers historical and live data from multiple control points in an oil and gas flow network, identifies steady state intervals, and extracts statistical data to represent the data in a compact form, allowing for assessment and optimization of the flow network without the need for specific excitations.
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 transforms the complex simulation problem into a simpler statistical analysis problem by changing the approach from physics-based modeling to data-driven parameter extraction. Instead of using complex flow dynamics models, the system extracts statistical parameters (mean, standard deviation, autocorrelation) from historical data to characterize well behaviors, thereby achieving prediction capability with reduced model complexity
Solution Approach 2:
The patent creates simplified statistical representations (copies) of the complex flow network behavior. By extracting statistical parameters that capture the essential variability and correlation structures from historical data, the system creates lightweight models that replicate key network responses without requiring full-physics simulations
2Manufacturing precision
If complex simulations are used to optimize production settings, then optimization accuracy is improved, but computational resources and time requirements increase
Solution Approach 1:
The patent changes the optimization approach from complex constraint-based simulation optimization to statistical parameter-based optimization. By using extracted statistical parameters (means, standard deviations, autocorrelations) to represent well behaviors, the system enables faster optimization calculations while maintaining practical accuracy for production decision-making
Solution Approach 2:
The patent employs lightweight statistical models that are computationally inexpensive compared to full simulations. These simplified models can be rapidly evaluated and updated, providing adequate optimization guidance without requiring significant computational resources or long calculation times
3Measurement precision
If large volumes of historical and live data are collected, then assessment accuracy is improved, but data processing and storage requirements increase
Solution Approach 1:
The patent extracts essential statistical parameters from large volumes of historical and live data, separating the critical information needed for assessment from the redundant raw data. By calculating means, standard deviations, and autocorrelation functions, the system extracts the core statistical characteristics that drive assessment accuracy while eliminating unnecessary data volume
Solution Approach 2:
The patent transforms raw time-series data into statistical parameter representations, changing the data form from voluminous raw measurements to compact statistical descriptors. This parameter transformation maintains the essential variability and correlation information needed for accurate assessment while dramatically reducing data storage and processing requirements
Data Source
AI summary
A method for assessment of an oil and gas flow network comprises: (1) gathering historical data and/or live data relating to the status of multiple control points at different branches 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 d; (2) identifying time intervals in the data during which the control points and the flow parameters are in a steady state; and (3) extracting statistical data representative of some or all steady state intervals identified in step (2) to thereby represent the original data from step (1) in a compact form.