Smart Needle Valve Optimization for Gas Well Liquid Loading
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Solution Overview
Problem
Liquid loading in gas wells, particularly in tight gas wells controlled by needle valves, leads to productivity loss and costly workovers due to insufficient gas velocity to carry liquids to the surface, resulting in inefficient and imprecise production control.
Innovation Solution
A Smart Needle Valve (SNV) optimization system that collects and analyzes historical pressure buildup and gas production data to train machine learning models, predicting gas production and pressure trends over production cycles and recommending optimal timing for opening and closing the needle valve to maximize production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual methods are used to control needle valves, then operational flexibility is maintained, but prediction accuracy and control precision deteriorate
Solution Approach 1:
The patent replaces manual mechanical control methods with an automated machine learning-based prediction system. The system uses historical pressure and production data to train models that automatically predict optimal needle valve timing, substituting human expert judgment with computational algorithms that provide higher prediction accuracy and consistency.
Solution Approach 2:
The system enables self-service by allowing the machine learning models to automatically analyze historical data, predict production trends, and determine optimal valve timing without requiring continuous human intervention. The models learn from past performance and continuously improve their predictions, making the system self-optimizing over time.
2Productivity
If site-specific expert analysis is used, then local conditions are considered, but time consumption and operational efficiency worsen
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical data before actual production cycles. The models are prepared in advance to quickly predict optimal valve timing for upcoming production periods, eliminating the need for time-consuming manual analysis during critical production windows.
Solution Approach 2:
The patent replaces time-consuming manual expert analysis with automated computational models that can process and analyze historical data rapidly. The machine learning system performs predictions in minutes or seconds compared to the hours or days required for manual site-specific expert evaluation.
3Adaptability or versatility
If fixed needle valve schedules are used, then operational simplicity is maintained, but production optimization and adaptability deteriorate
Solution Approach 1:
The system implements dynamics by transitioning from fixed, static needle valve schedules to dynamic, adaptive timing recommendations. The machine learning models continuously analyze historical performance data and adjust predictions based on changing production conditions, reservoir characteristics, and well-specific parameters, enabling the system to adapt to varying operational scenarios.
Solution Approach 2:
The patent applies parameter changes by using machine learning models to optimize multiple variables including valve timing, production cycle duration, and pressure management parameters. The system adjusts these parameters based on learned patterns from historical data, transforming fixed operational parameters into dynamically optimized values that maximize production efficiency.
Data Source
AI summary
This disclosure relates to methods, systems, and computer-readable media for collecting and analyzing historical data related to pressure buildup and gas production for use in training and implementing one or more models for predicting the relationship between gas production and needle valves. In particular, this disclosure relates to training an implementing a pressure buildup and production model to be used in connection with determining when to open a needle valve that controls the flow of gas within a wellsite environment. The following disclosure describes features and characteristics related to specifically training a number of machine learning models and, based on a combination of predictions and real-time production and pressure data, further training the machine learning models to recommend a timing when the needle valve should be open to maximize production of the wellsite over time.