Approaches to needle valve optimization

The Smart Needle Valve optimization system addresses the challenge of liquid loading in gas wells by using machine learning models to predict gas production and pressure trends, thereby optimizing needle valve operation and enhancing production efficiency.

US20250101859A1Pending Publication Date: 2025-03-27SCHLUMBERGER TECH CORP
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Patent Information

Application Number
US18/895459
Authority / Receiving Office
US · United States
Patent Type
Applications(United States)
Current Assignee / Owner
Priority Date
2023-09-26
Filing Date
2024-09-25
Publication Date
2025-03-27

AI Technical Summary

Technical 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.

Method used

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.

Benefits of technology

The SNV optimization system improves the accuracy of predicting gas production and pressure trends, enabling more precise control of the needle valve, which leads to increased productivity and reduced operational costs by optimizing gas production over multiple production cycles.

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Abstract

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.
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