I am interested in applying state-of-the-art deep neural network architectures in seismic interpretation and inversion, to have the better evaluating criterion for mapping sub-surface architecture.
Thesis Supervisors
Dr. Animesh Mandal, IIT Kanpur, India https://home.iitk.ac.in/~animeshm/
Dr. Shib S Ganguli, Deep Seismic Research Group, CSIR-NGRI, Hyderabad, India https://shibganguli.wixsite.com/shib
Seismic multi-attribute analysis can efficiently delineate the sub-surface target such as, salt body, fault network, gas chimney etc.
Due to their exceptional nonlinear mapping technique and data-driven approach, deep neural networks have been widely used in seismic inversion.
Since, the objective function for seismic inversion is non-convex in nature, then its highly important to ensure the convergence at global minima.
Enrolled in a Ph.D. program in July 2020 at the Department of Earth Sciences, IIT Kanpur
Current CPI- 8.6/10
Received a prestigious PMRF in December 2020 cycle
Enrolled in M.Tech in "Geological Technology" in July 2018 at the department of the earth sciences, IIT Kanpur
CPI - 8.6/10
Awarded Academic excellence award.
*M.Tech program has been converted to Ph.D. since Aug 2020
Received B.Tech degree from UPES, Dehradun
CPI- 3.43/4
Graduated S.S.E (CBSE board) from Sumit Rahul Memorial SR. SEC school, Agra with 81.8%
Graduated H.S.E (State board) from Queen Victoria Inter college, Agra with 61.6%
• Worked as a domain expert, who understands the petroleum value chain.
• Led the knowledge sharing team, educated colleagues on operations in oil & gas industry.
• Rewarded for conducting knowledge sharing programs for members of Asia-Pacific delivery center.
Workflow Automation for stresses computation in Shale Formation
• Value addition - This automated workflow will assert the service quality & save time ~2hrs./job
• Approach – Developed a python script to automate the stress model under Techlog software.
• Achievement – Published a detailed instruction manual for above work into Schlumberger’s content repository.
Regional Study of Pore pressure of Barmer basin and its application
• Value addition – This regional pore pressure study will mitigate the risk factor in exploring future prospects.
• Approach – Identified the overpressure regions then using Predict software performed detailed analysis.
• Achievement – Computed hydrocarbon column height and designed a safer mud window for an undrilled well.
Basic Well Logging Technique & Seismic Interpretation
Description: Understood concepts of various well logging tools and then interpreted the well logging data. Also learnt about the seismic method of Geophysical exploration & the basics of seismic interpretation.
Winner of the regional Debate competition, on the theme “Fossil Fuels will remain a dominant component of Nation's energy basket”, Association of Petroleum Geologist (APG), Delhi chapter, 2022.
Recipient of Anni Talwani Memorial Grant for Women Researcher Award, Indian Geophysical Union (IGU), 2022.
Received a Technical Program registration grant from the Society of Exploration Geophysicists (SEG), 2021.
Awarded a prestigious Prime Minister Research Fellowship (PMRF) fellowship, Dec 2020 cycle.
Received an academic excellence award, IIT Kanpur 2019-20.
Won 1st prize in case study competition on “Safe mud window in depleted reservoir” conducted by Schlumberger, October 2019
Received recognition for conducting the knowledge sharing (in Oil and Gas domain) program for CGI members of Asia pacific delivery center.
Attended an intensive training school on "Python for data science, machine learning, and deep learning", Organised by Prof. Aditya K. Jagannatham, department of Electrical Engineering, IIT Kanpur, from 6th to 26th May 2023.
Successfully completed an online course on "OpendTect course: Machine Learning Python Programming", dGB, Earth Sciences.
Successfully completed a short course on "ML in Geosciences" by Prof. Gerard Schuster, KAUST.
Attended a lecture on "Carbon Capture Utilization & Storage (CCUS)" organised by IMAGE 2021.
Successfully completed a course on "Neural Networks and Deep Learning" offered through Coursera.
Attended a lecture on "Geomechanical Issues Affecting Long-Term Storage of CO2" by prof. Mark D. Zoback.