Optimization of Near Wellbore Divertors: A Machine Learning Approach
A research project focusing on the optimization of near wellbore diverters in Hydraulic Fracturing with the help of data collected through lab experiments and the development machine learning model to predict the pressure for given concentrations of Bead, Powder, and Flakes. The project was primarily focused on the end goal of web app development that lets users predict the pressure on custom observations of Beads, Powder, and Flakes in Hydraulic Fracturing.
Comparative Study of Oil Recovery Performance under Regular and LoSal Ionic Waterflooding
A B.Tech degree final year major project at Pandit Deendayal Energy University (PDEU) with a prior focus on the comparative study of oil recovery performance between Regular and LoSal Waterflooding. The project involved field modeling and simulation using CMG software (IMEX Simulator) through the development of static and dynamic models to analyze the oil recovery performance under different injection & waterflooding scenarios and study the key factors influencing the oil recovery in LoSal Waterflooding scenarios.
Development of Coconut Husk Based Drilling Fluid
The significance of using coconut husk in drilling fluid lies in its ability to provide an effective, sustainable, and cost-efficient solution for lost circulation and related challenges. The project purely focused on the development of different variants of coconut husk-based drilling fluid (based on the concentration and sizes of the husk) and a comparative study of the same to use them as a Loss Circulation Material (LCM) in drilling operations. Different lab experiments were conducted on the prepared mud samples and the comparative study was performed by analyzing different properties such as Fluid Loss, Shear Stress vs. Shear Rate, Gel strength, Viscosity, etc.