University of Houston

  • Sinha, Utkarsh, Birol Dindoruk, and Mohamed Soliman. “Estimation of Dead Oil Viscosity Utilizing Physics Based Correlative Principles and Predictive Machine Learning Techniques.” SPE Annual Technical Conference and Exhibition. OnePetro, 2019.
  • Sinha, Utkarsh, Birol Dindoruk, and Mohamed Y. Soliman. “Development of a new correlation to determine relative viscosity of heavy oils with varying asphaltene content and temperature.” Journal of Petroleum Science and Engineering 173 (2019): 1020-1030.
  • To the 2020-2025 section:
  • Sinha, Utkarsh, Birol Dindoruk, and Mohamed Y. Soliman. “Physics Augmented Correlations and Machine Learning Methods to Accurately Calculate Dead Oil Viscosity Based on the Available Inputs.” SPE Journal (2022): 1-14.
  • Sinha, Utkarsh, Birol Dindoruk, and Mohamed Soliman. “Prediction of CO2 Minimum Miscibility Pressure Using an Augmented Machine-Learning-Based Model.” SPE Journal 26.04 (2021): 1666-1678.
  • Sinha, Utkarsh, Birol Dindoruk, and Mohamed Soliman. “Machine learning augmented dead oil viscosity model for all oil types.” Journal of Petroleum Science and Engineering 195 (2020): 107603.
  • Sinha, Utkarsh, Birol Dindoruk, and Mohamed Soliman. “Prediction of CO2 Minimum Miscibility Pressure MMP Using Machine Learning Techniques.” SPE Improved Oil Recovery Conference. OnePetro, 2020.
  • Thitaree Lertliangchai, Birol Dindoruk, Ligang Lu, and Xi Yang. “A Comparative Analysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machine Learning Algorithms.” SPE Annual Technical Conference and Exhibition. OnePetro, 2021.
  • You can add them to the tops of each section, just make sure it’s the same format as the pervious ones.