Bucharest, Romania

Peter Schlicht


Average Co-Inventor Count = 7.0

ph-index = 1


Company Filing History:


Years Active: 2023

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1 patent (USPTO):

Title: The Innovative Contributions of Peter Schlicht

Introduction

Peter Schlicht is a notable inventor based in Bucharest, Romania. He has made significant contributions to the field of machine learning, particularly in the area of sedimentary facies prediction. His work has implications for various industries, including oil and gas exploration.

Latest Patents

Peter Schlicht holds a patent for a groundbreaking invention titled "Machine-learning for sedimentary facies prediction." This patent involves using machine learning techniques to predict sedimentary facies by analyzing one or more logs acquired in a borehole. The process includes performing a petrophysical clustering of borehole depths, where depths are grouped into clusters based on similarities in the logs. Additionally, the invention encompasses log inclusion optimization, which involves selecting parameters for the clustering process, such as the number and type of logs considered. The final step includes classifying the clusters into core depositional facies using predetermined rules.

Career Highlights

Peter Schlicht is currently employed at Schlumberger Technology Corporation, a leading company in the oil and gas industry. His role involves applying his expertise in machine learning to enhance predictive models for sedimentary facies, thereby improving exploration efficiency and accuracy.

Collaborations

Peter has collaborated with several talented individuals in his field, including Nadege Bize-Forest and Laura Lima Angelo Dos Santos. These collaborations have contributed to the advancement of his research and the successful development of his patented technology.

Conclusion

Peter Schlicht's innovative work in machine learning for sedimentary facies prediction showcases his expertise and commitment to advancing technology in the oil and gas sector. His contributions are paving the way for more efficient exploration methods and highlight the importance of interdisciplinary collaboration in driving innovation.

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