Houston, TX, United States of America

Ratnanabha Sain

USPTO Granted Patents = 6 


 

Average Co-Inventor Count = 2.4

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2020-2023

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6 patents (USPTO):Explore Patents

Title: Ratnanabha Sain: Innovator in Petrophysical Inversion

Introduction

Ratnanabha Sain is a prominent inventor based in Houston, Texas, known for his significant contributions to the field of petrophysical inversion. With a total of 6 patents to his name, Sain has developed innovative methods that leverage machine learning to enhance subsurface modeling and fluid saturation analysis.

Latest Patents

One of Sain's latest patents is titled "Petrophysical inversion with machine learning-based geologic priors." This invention outlines a method and system for modeling a subsurface region by applying a trained machine learning network to an initial petrophysical parameter estimate. The process predicts a geologic prior model and performs a petrophysical inversion using this model, geophysical data, and parameters to generate a rock type probability model and an updated petrophysical parameter estimate. The invention also includes managing hydrocarbons with the rock type probability model and checking for convergence of the updated estimates through iterative applications of the machine learning network.

Another notable patent is the "Fluid saturation model for petrophysical inversion." This method involves generating a fluid saturation model for a subsurface region by obtaining a model of the region and flooding it with various fluid types to create flood models. The process includes running trial petrophysical inversions to identify potential fluid contact regions, partitioning the subsurface model, and constructing the fluid saturation model from these partitions.

Career Highlights

Ratnanabha Sain has worked with notable companies such as ExxonMobil Upstream Research Company and ExxonMobil Technology and Engineering Company. His experience in these organizations has allowed him to refine his expertise in petrophysical modeling and machine learning applications in geosciences.

Collaborations

Sain has collaborated with esteemed colleagues, including Jan Schmedes and David D McAdow, contributing to advancements in their shared field of expertise.

Conclusion

Ratnanabha Sain's innovative work in petrophysical inversion and fluid saturation modeling showcases his commitment to advancing geoscience through technology. His contributions are paving the way for more efficient and accurate subsurface analysis, benefiting the energy sector and beyond.

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