Stanford, CA, United States of America

Rayan Kanfar

USPTO Granted Patents = 1 

Average Co-Inventor Count = 5.0

ph-index = 1


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Rayan Kanfar: Innovator in Geological Engineering

Introduction

Rayan Kanfar is a notable inventor based in Stanford, CA. He has made significant contributions to the field of geological engineering through his innovative approaches. His work focuses on utilizing advanced technologies to enhance the understanding of geological formations.

Latest Patents

Rayan Kanfar holds 1 patent for his invention titled "Determination of mechanical properties of a geological formation using deep learning applied to data acquired while drilling." This patent outlines methods for determining the mechanical properties of geological formations by leveraging data collected during drilling operations. The process involves a computer system that generates features from the drilling data, which are indicative of the mechanical properties of the geological formation. The system segments these features into sequences that can be processed by a trained temporal convolutional network (TCN). Ultimately, the TCN determines the mechanical properties, and a display device generates a graphical representation of these properties.

Career Highlights

Rayan Kanfar is currently employed at the Saudi Arabian Oil Company, where he applies his expertise in geological engineering. His work is instrumental in advancing the methodologies used in the oil and gas industry, particularly in understanding the mechanical characteristics of geological formations.

Collaborations

Rayan collaborates with esteemed colleagues, including Robert Smith and Andrey Bakulin. Their combined efforts contribute to the innovative research and development within their field.

Conclusion

Rayan Kanfar's contributions to geological engineering through his patent and work at the Saudi Arabian Oil Company highlight his role as an innovator in the industry. His use of deep learning techniques to analyze geological data represents a significant advancement in the field.

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