Anisa Nugrahaningtyas joined PoreLab at the Department of Geosciences, NTNU, on July 1st, 2025, as PhD candidate. She completed her M.Sc degree in Petroleum Engineering from Khalifa University, UAE, in August 2023. The title of her master thesis was: Evaluation of Customized Polyacrylamide Based Co-Polymers for EOR Application through Integrated Screening Approach. She hold a B.Sc degree in Petroleum Engineering from Institut Teknologi Bandung (ITB), Indonesia, in July 2021.
Post-graduation, Anisa joined the ADNOC (Abu Dhabi National Oil Company) Research and Innovation Centre (ADRIC) as Research Associate. ADNOC is the biggest oil company in the United Arab Emirates (UAE). She contributed there to a pioneering project focusing on researching novel polymer and microgel formulations for UAE carbonate reservoirs. She led as well Nuclear Magnetic Resonance (NMR) research initiatives, integrating NMR techniques to study the impact of polymer and microgel flooding on EOR performance.
Anisa main supervisor is Associate Professor Antje van der Net, and her co-supervisor is Professor Carl Fredrik Berg.
Anisa presents her PhD topic as follow:
My PhD research focuses on how core handling and cleaning procedures influence wettability in porous media. Using advanced Mass Spectrometry (MS) techniques, I aim to reduce uncertainties in Special Core Analysis (SCAL) by investigating oil-rock interactions at the molecular level and improving the reliability of laboratory measurements under reservoir-representative conditions.
This project is part of the TeSCAL initiative and is a collaborative effort between NTNU, SINTEF’s Reservoir & Geology and Mass Spectrometry groups, and the University of Eastern Finland’s Department of Chemistry. Conducted in close partnership with Equinor and Aker BP, it targets strategic core studies relevant to the Norwegian Continental Shelf. By integrating experimental workflows with high-resolution chemical analysis, my research aims to redefine how wettability is characterized, enabling more accurate, reproducible, and field-representative SCAL data for improved reservoir evaluation and decision-making.