Liquid Loading Identification Using Recursive Neural Networks
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Solution Overview
Problem
Liquid loading in gas wells often results in productivity loss and costly workovers due to insufficient gas velocity to carry liquids to the surface, and conventional methods are inefficient and site-specific, failing to timely identify and mitigate liquid loading instances.
Innovation Solution
A liquid loading classification model trained using historical data and real-time sensor data, employing a recursive neural network (RNN) to predict liquid loading instances, which can be fine-tuned for specific well sites and implemented across multiple sites, utilizing pre-processing techniques to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional static models and expert analysis are used to identify liquid loading, then domain-specific knowledge can be applied, but the identification is not timely and requires significant time and energy
Solution Approach 1:
The patent replaces the mechanical system of manual expert analysis with an automated machine learning classification model that processes sensor data to identify liquid loading events. This substitution eliminates the time-consuming manual review process while maintaining or improving identification accuracy through algorithmic pattern recognition.
Solution Approach 2:
The system enables self-service by allowing the classification model to autonomously identify liquid loading events without requiring continuous expert intervention. The model processes sensor data automatically, providing timely detections that reduce both time loss and energy consumption compared to conventional expert-based methods.
2Measurement precision
If site-specific expert models are used, then domain knowledge is applied, but the models are ineffective at other sites and lack scalability
Solution Approach 1:
The patent creates a universal classification model that can be deployed across multiple well sites with different characteristics. The model processes sensor data from various sites using the same algorithmic framework, enabling cross-site applicability while maintaining detection accuracy through standardized feature extraction and classification procedures.
Solution Approach 2:
The system adapts to different sites by adjusting model parameters and thresholds based on site-specific sensor data characteristics. This allows the same classification model to effectively identify liquid loading events across diverse well conditions while maintaining scalability to new sites without requiring complete model redesign.
3Reliability
If conventional identification methods are used, then expert analysis is performed, but liquid loading cannot be identified in time to avoid production interruption
Solution Approach 1:
The classification model performs preliminary identification of liquid loading events by continuously monitoring sensor data and detecting patterns before they cause significant production disruptions. This advance detection enables timely mitigation actions, maintaining production continuity while minimizing detection delays compared to conventional reactive expert analysis.
Data Source
AI summary
This disclosure relates to collecting and analyzing data associated with a well site to predict instances of liquid loading that occurs (or that will occur) in a particular well environment. Systems herein involve training a liquid loading classification model based on historical data to predict whether or not liquid loading is occurring or will occur based on real-time sensor data that is collected by a plurality of sensors at the well site. The data is preprocessed and labeled in a manner that provides an efficient training process and which enables a machine learning model, such as a recursive neural network (RNN), to accurately predict when liquid loading will occur such that a presentation may be generated and presented to an individual with the ability to prevent or otherwise mitigate the liquid loading at a time when inefficiencies and other issues can be prevented.