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Shankar Manuel Aghito's picture

Shankar Manuel Aghito

PhD Candidate
  • E-mailshankar.aghito@uib.no
  • Visitor Address
    Allégaten 70
    5007 Bergen
  • Postal Address
    Postboks 7803
    5020 Bergen
Academic article
  • Show author(s) (2024). Modelling and validation of polycyclic aromatic hydrocarbons emissions from offshore oil production facilities. Science of the Total Environment. 1-12.
  • Show author(s) (2023). ChemicalDrift 1.0: an open-source Lagrangian chemical-fate and transport model for organic aquatic pollutants. Geoscientific Model Development. 2477-2494.
  • Show author(s) (2020). Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model. ISA transactions. 256-268.
  • Show author(s) (2020). Digitized uncertainty handling of pore pressure and mud-weight window ahead of bit: north sea example. SPE Journal. 529-540.
  • Show author(s) (2016). Real time model identification using multi-fidelity models in managed pressure drilling. Computers and Chemical Engineering. 76-84.
Lecture
  • Show author(s) (2019). Drilling Mud Process Control – A Step Change in Automatic Mud Management.
  • Show author(s) (2017). Statistical Method for Error Prediction in Decision Support and Control Systems.
  • Show author(s) (2017). Automatic Model Calibration for Drilling Automation.
Academic chapter/article/Conference paper
  • Show author(s) (2015). SPE/IADC-173145-MS A Dynamic Dual Gradient Model and its Use for Training Prior to DGD Operations in Norway and GoM. 13 pages.
  • Show author(s) (2015). Ensemble Model Predictive Control for Robust Automated Managed Pressure Drilling. 17 pages.
Article in business/trade/industry journal
  • Show author(s) (2020). Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model. ISA transactions.
  • Show author(s) (2019). Pilot test for automated mud management leverages real-time monitoring, hydraulic modeling. Drilling Contractor. 30-33.
Chapter
  • Show author(s) (2020). Hybrid Approach for Drilling Automation. . In:
    • Show author(s) (2020). Proceedings of SPE Norway Subsurface Conference. Society of Petroleum Engineers.
  • Show author(s) (2020). Autoviscosity - A Vital Step Towards Automation of the Drilling Fluid Process on Drilling Rigs. . In:
    • Show author(s) (2020). Proceedings of SPE Norway Subsurface Conference. Society of Petroleum Engineers.
  • Show author(s) (2017). Statistical Method for Error Prediction in Decision Support and Control Systems. 8 pages. In:
    • Show author(s) (2017). 2017 SPE/IADC - Drilling Conference and Exhibition - digital Proceedings. Society of Petroleum Engineers.
  • Show author(s) (2017). Automatic Model Calibration for Drilling Automation. 17 pages. In:
    • Show author(s) (2017). SPE Bergen One Day Seminar, 2017. Society of Petroleum Engineers.

More information in national current research information system (CRIStin)