Production Metering Stability Evaluation for Multiphase Flow Optimization
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Solution Overview
Problem
Conventional systems in the hydrocarbon recovery industry lack effective methods for evaluating the stability of production metering data, leading to inaccurate decision-making and inefficiencies due to dynamical and non-stationary behavior in multiphase flowmeters.
Innovation Solution
A method for production metering optimization that involves obtaining data, determining quasi-periodicity, filtering periodical components, calculating a qualitative index of stability, analyzing trends, and conducting optimization based on identified instabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional measurement systems are used for production metering, then the system is simple to operate, but the measurement precision and stability evaluation are insufficient due to dynamical and non-stationary behavior
Solution Approach 1:
The patent segments the production metering data into different operational modes using clustering algorithms (e.g., k-means, Gaussian mixture models). This segmentation allows separate stability evaluation for each mode, improving measurement precision while managing complexity through modular analysis of data subsets rather than treating all data uniformly.
Solution Approach 2:
The patent introduces intermediate processing steps including data filtering, outlier detection, and mode classification as mediators between raw measurements and stability evaluation. These intermediary processes prepare the data appropriately for analysis, enhancing measurement precision without requiring direct complex processing of raw non-stationary data.
2Measurement precision
If detailed stability analysis methods are implemented, then the measurement precision improves, but the ease of operation decreases due to complex data processing requirements
Solution Approach 1:
The patent implements self-service through automated mode detection and classification algorithms that automatically identify operational modes and perform stability evaluation without requiring manual intervention. The system self-adjusts to different operational conditions and performs appropriate analysis, maintaining measurement precision while preserving ease of operation through automation.
Solution Approach 2:
The patent changes parameters dynamically based on detected operational modes, adjusting analysis methods, time windows, and evaluation criteria according to the specific mode identified. This adaptive parameter adjustment maintains high measurement precision across different conditions while simplifying operation by removing the need for manual parameter tuning.
3Reliability
If production decisions are made based on overall engineer experience rather than quantitative stability analysis, then the ease of operation is maintained, but the reliability of decisions deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that provide quantitative stability metrics and mode classifications to support decision-making. The system continuously monitors stability parameters and provides actionable feedback about operational conditions, improving decision reliability by replacing subjective experience with objective quantitative information while maintaining manageable complexity through focused key metrics.
Data Source
AI summary
Embodiments presented provide for an optimization approach for production metering. The optimization approach uses a stability evaluation with data sets to provide for accurate decision making by a user.