Virtual Flow Meter Optimization for High-Count Well Management
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Solution Overview
Problem
Existing Production Surveillance and Optimization (PSO) workflows and models face challenges in managing high well counts, dynamic and complex well behaviors, and the prediction of manipulative variables such as choke, artificial lift, and workover settings in unconventional shale wells.
Innovation Solution
A method executed by a processor that includes receiving input data for a system of wells, predicting virtual flow rates using a trained virtual flow meter, generating production optimization recommendations based on predicted uplift and received rules, and adjusting manipulative parameters of wells accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional PSO workflows are used to manage well production, then production optimization can be achieved, but the system cannot handle high well counts efficiently
Solution Approach 1:
The patent creates virtual copies of flow meters (virtual flow meters) that simulate the behavior of physical flow meters. These virtual instruments are deployed for each well, allowing the system to monitor and optimize thousands of wells simultaneously without requiring proportional physical infrastructure or manual intervention, thus scaling productivity while managing complexity.
Solution Approach 2:
The system implements a universal optimization platform that handles multiple functions: data collection from various sources (flow meters, pressure sensors, temperature sensors), virtual flow meter deployment, production forecasting, and optimization recommendation generation. This multi-functional approach allows a single system to manage high well counts across different well types and conditions.
2Reliability
If traditional PSO models are used, then production optimization is possible, but they cannot predict dynamic and complex well behaviors in real-time
Solution Approach 1:
The system performs preliminary actions by deploying virtual flow meters that continuously simulate and predict well behavior before actual production changes occur. These virtual instruments pre-calculate expected outcomes based on current conditions, allowing the system to provide real-time predictions and recommendations without waiting for actual production data to reflect changes.
Solution Approach 2:
The patent replaces traditional mechanical/physical measurement systems with computational models. Instead of relying solely on physical flow meters and manual analysis, the system uses virtual flow meters and machine learning algorithms to predict well behavior, substituting physical measurement limitations with computational speed and flexibility.
3Measurement precision
If physical flow meters are deployed for each well, then accurate flow rate measurement is achieved, but the cost and complexity increase significantly for high well counts
Solution Approach 1:
The patent creates virtual copies of flow meters that run computational models on existing data infrastructure. These virtual flow meters provide the same measurement functionality as physical instruments but without the need for physical deployment at each well location, significantly reducing complexity while maintaining measurement precision through sophisticated algorithms.
Solution Approach 2:
The system introduces virtual flow meters as an intermediary between physical sensors and production optimization decisions. Rather than directly connecting physical flow meters to every well, the virtual instruments mediate by processing data from various sources and providing optimized measurements, reducing the need for extensive physical infrastructure.
Data Source
AI summary
A methodology for optimizing the recovery from a system of wells is provided. The method is executed via a processor of a computing system. The method includes receiving input data for a system of wells. The method also further includes predicting, via a trained virtual flow meter, virtual flow rates for the system of wells for a scenario using a predicted pressure and temperature for the scenario. The predicted pressure and temperature are generated based on the input data. The method includes generating a production optimization recommendation based on the predicted virtual flow rates and a received rule for the system of wells. The method includes adjusting a manipulative parameter of a well in the system of wells based on production optimization recommendation.