M
Manojkumar Gudala
Researcher at Indian Institute of Technology Madras
Publications - 32
Citations - 176
Manojkumar Gudala is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Geology & Geothermal gradient. The author has an hindex of 6, co-authored 13 publications receiving 86 citations. Previous affiliations of Manojkumar Gudala include Indian Institute of Technology Dhanbad & Indian Institutes of Technology.
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Kinetics of methane hydrate formation and its dissociation in presence of non-ionic surfactant Tergitol
TL;DR: In this article, the kinetics of methane hydrate formation and its dissociation in the presence of a nonionic surfactant Tergitol was studied in water and the experimental results showed that a certain degree of sub-cooilng is required to initiate the formation of hydrate.
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Hydrodynamics and energy analysis of heavy crude oil transportation through horizontal pipelines using novel surfactant
Manojkumar Gudala,Shirsendu Banerjee,Tarun Kumar Naiya,Ajay Mandal,T. Subbaiah,T. Rama Mohan Rao +5 more
TL;DR: In this article, the authors investigated the impact of diameter, temperature, and the addition of novel surfactant extracted from Madhuca longifolia (Mahua) on pressure drop, shear viscosity, pumping power saving and flow increment during heavy crude oil flow in horizontal pipelines.
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Numerical Modeling of Coupled Fluid Flow and Geomechanical Stresses in a Petroleum Reservoir
TL;DR: In this article, a fully coupled hydro and geomechanical model has been used to predict the transient pressure disturbance, reservoir deformation, and effective stress distribution in both homogeneous and heterogeneous reservoirs.
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Rheological modeling and drag reduction studies of Indian heavy crude oil in presence of novel surfactant
Manojkumar Gudala,Shirsendu Banerjee,Amit Kumar,Rama Mohan Rao T,Ajay Mandal,Tarun Kumar Naiya +5 more
TL;DR: In this article, a 2.5 m horizontal pipeline at different temperatures and flow rates with and without naturally extracted surfactant Mahua was used for viscosity and rheology modeling of heavy crude oil.
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Numerical investigations on a geothermal reservoir using fully coupled thermo-hydro-geomechanics with integrated RSM-machine learning and ARIMA models
TL;DR: In this article, an integrated machine learning (ML)-response surface model (RSM)-autoregressive integrated moving average (ARIMA) model was used to enhance the heat production from a geothermal reservoir.