College Station, TX, United States of America

Samuel F Noynaert

USPTO Granted Patents = 2 

 

Average Co-Inventor Count = 2.9

ph-index = 1


Company Filing History:


Years Active: 2023-2025

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2 patents (USPTO):Explore Patents

Title: Innovations by Samuel F Noynaert

Introduction

Samuel F Noynaert is an accomplished inventor based in College Station, TX (US). He has made significant contributions to the field of drilling operations through his innovative patents. With a total of 2 patents, Noynaert's work focuses on optimizing drilling processes using advanced algorithms and machine learning techniques.

Latest Patents

Noynaert's latest patents include the "Application of marsh funnel through use of trained algorithm." This method involves obtaining a fluid's density and Marsh funnel time, which are then processed to derive the fluid's properties. A machine-learning algorithm is applied to determine the plastic viscosity and yield point of the fluid, with the output stored for future use. Another notable patent is "Methods for real-time optimization of drilling operations." This method is performed by a drilling rig control center and includes receiving raw data related to drilling operations, deriving state measurements, and generating control responses based on correlations of the data.

Career Highlights

Samuel F Noynaert is affiliated with The Texas A&M University System, where he continues to advance research in drilling technologies. His innovative approaches have the potential to significantly enhance the efficiency and effectiveness of drilling operations.

Collaborations

Noynaert has collaborated with notable colleagues such as Enrique Zarate Losoya and Ibrahim S El-sayed, contributing to a dynamic research environment focused on practical applications of technology in drilling.

Conclusion

Samuel F Noynaert's contributions to the field of drilling operations through his innovative patents demonstrate his commitment to advancing technology. His work not only enhances operational efficiency but also showcases the potential of machine learning in real-time applications.

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