Dynamic Choke Flow Correlation for Well Metering
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Solution Overview
Problem
Current well metering technologies face challenges in accurately measuring and forecasting production rates without incurring high capital expenditures, as traditional flowmeters are not installed on every wellhead and existing empirical choke flow correlations rely on static coefficients that are not universally applicable.
Innovation Solution
A method using meta-heuristic estimation, specifically particle swarm optimization, to dynamically adjust coefficients in correlations between liquid flow rate and operational parameters like wellhead pressure and gas-liquid ratio, allowing for periodic or event-driven updates to improve accuracy without relying on contemporaneous wellhead flowmeters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional wellhead flowmeters are installed on every well to measure production rate, then measurement precision is improved, but capital expenditure and device complexity increase significantly
Solution Approach 1:
The patent uses an intermediary computational model that correlates readily measurable operational parameters (choke size, wellhead pressure, gas-liquid ratio) with liquid flow rate through empirically derived coefficients. This intermediary approach avoids direct flow measurement while achieving accurate production rate estimation through the correlation equation q = A1*D^A2*Pwh^A3*R^A4
Solution Approach 2:
The patent creates a virtual representation of the well production system using empirical correlations and computational models. Instead of physically measuring flow with flowmeters, the system copies the relationship between operational parameters and flow rate through calibrated coefficient sets that replicate actual production behavior
2Device complexity
If static coefficient values are used in empirical choke flow correlations, then device complexity is reduced, but measurement precision and adaptability to changing conditions deteriorate
Solution Approach 1:
The patent transforms static coefficient values into dynamic, adaptive parameters that automatically adjust to changing operational conditions. The system periodically updates correlation coefficients using well-test data and meta-heuristic optimization algorithms, allowing the model to adapt to reservoir depletion, changing fluid properties, and evolving production conditions without manual intervention
Solution Approach 2:
The patent systematically changes the parameter values of correlation coefficients based on observed production data and optimization results. By adjusting A1, A2, A3, and A4 coefficients in response to changing operational conditions and well-test measurements, the system maintains high measurement precision across different production stages
3Measurement precision
If well-test data is collected frequently to update correlation coefficients, then measurement precision is improved, but loss of time and productivity are worsened due to operational disruptions
Solution Approach 1:
The patent implements periodic well-testing schedules rather than continuous testing, updating correlation coefficients at optimized intervals based on production stability, reservoir characteristics, and coefficient drift rates. This periodic approach balances measurement precision needs with minimal operational disruption
Solution Approach 2:
The system uses existing operational data and automated meta-heuristic optimization to self-update correlation coefficients without requiring extensive manual well-tests. The optimization algorithms automatically calibrate coefficients using available production data, reducing the need for dedicated testing time
Data Source
AI summary
A system for, and method of, well metering a target well based on empirical operational parameter values for the target well is presented. The techniques include: obtaining a well-test data set, the well-test data set including field measurements of a well-test flow rate and well-test operational parameter values for at least one test well; performing, based on the well-test data set, a meta-heuristic estimation of a plurality of coefficient values in a correlation, where the correlation correlates liquid flow rate with operational parameters; measuring empirical operational parameter values for the target well; determining a liquid flow rate value of the target well based on the empirical operational parameter values for the target well and using the correlation with the plurality of coefficient values; and providing the liquid flow rate value.