Deepthi Sen

Deepthi Sen

PhD student at Texas A&M University

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

portfolio

Pro-Bono Consulting

I’m currently exploring the management consulting space by working on short high-impact projects organized by Texas A&M Graduate Consulting Club

Other Stuff

Independent research, writings and all other things outside of work

publications

Multiway continuous hidden Markov model‐based approach for fault detection and diagnosis

Published in AIChE Journal, 2014

A fault detection and classification scheme that uses probabilistic inference based on multiway continuous hidden Markov models (MCHMM).

Recommended citation: Sen, D., Raihan, D., & Chidambaram, M. Multiway continuous hidden Markov model based approach for fault detection and diagnosis. AIChE Journal. doi:10.1002/aic.14386. https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.14386

Eagle Ford shale play economics: US versus Mexico

Published in Journal of Natural Gas Science and Engineering, 2017

A comparative study of hydrocarbon tax regimes in Texas and Mexico as applied to shale oil wells completed in the Eagle Ford formation

Recommended citation: Weijermars, R., Sorek, N., Sen, D., & Ayers, W. B. (2017). Eagle Ford shale play economics: US versus Mexico. Journal of Natural Gas Science and Engineering, 38, 345-372. https://www.sciencedirect.com/science/article/pii/S187551001630909X

Automatic Detection of Anomalous Density Measurements due to Wellbore Cave-in

Published in Petrophysics, 2020

A time-series clustering based approach for anomaly detection in density and caliper logs

Recommended citation: Sen, D., Ong, C., Kainkaryam, S., & Sharma, A. (2020, September 23). Automatic Detection of Anomalous Density Measurements due to Wellbore Cave-in. Society of Petrophysicists and Well-Log Analysts. https://www.onepetro.org/journal-paper/SPWLA-2020-v61n5a3https://www.sciencedirect.com/science/article/pii/S187551001630909X

Data-Driven Rate Optimization Under Geologic Uncertainty

Published in SPE Annual Technical Conference and Exhibition 2020 (Virtual) Proceedings, 2020

A machine-learning based workflow for predicting reservoir connectivity and performing rate optimization over multiple geologic realizations

Recommended citation: Sen, D., Chen, H., Datta-Gupta, A., Kwon, J., & Mishra, S. (2020, October). Data-Driven Rate Optimization Under Geologic Uncertainty. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. https://www.onepetro.org/conference-paper/SPE-201325-MS

talks

teaching