Abqaiq, Saudi Arabia

Abdullah Abdulaziz AlGhamdi

USPTO Granted Patents = 1 

Average Co-Inventor Count = 3.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: Innovations by Abdullah Abdulaziz AlGhamdi

Introduction

Abdullah Abdulaziz AlGhamdi is a notable inventor based in Abqaiq, Saudi Arabia. He has made significant contributions to the field of hydrocarbon production through his innovative patent. His work focuses on utilizing machine learning to address challenges in hydrocarbon wells.

Latest Patents

AlGhamdi holds a patent titled "Calcium carbonate scale prediction and inhibition in hydrocarbon wells using machine learning." This patent outlines methods for predicting and inhibiting calcium carbonate scale in hydrocarbon wells. The process involves extracting training data from aqueous samples collected from various hydrocarbon wells. The training data is classified based on the production conditions of each well. It is then labeled according to whether calcium carbonate scale has formed within a specific time frame. A feature vector is created from this labeled data, which indicates the presence of calcium carbonate scale in the respective wells. A trained machine learning model is generated to predict the number of wells containing calcium carbonate scale over the designated period.

Career Highlights

AlGhamdi is currently employed at the Saudi Arabian Oil Company, where he applies his expertise in machine learning and hydrocarbon production. His innovative approach has the potential to enhance efficiency and reduce costs in the industry.

Collaborations

Some of his notable coworkers include Nasser Mubarak Al-Hajri and Fahad Muhammed Al-Meshal. Their collaboration contributes to the advancement of technology in hydrocarbon production.

Conclusion

Abdullah Abdulaziz AlGhamdi's work exemplifies the intersection of technology and innovation in the hydrocarbon industry. His patent on calcium carbonate scale prediction and inhibition showcases the potential of machine learning to solve real-world problems.

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