Senior Research Engineer III
Dr. Deniz Paker is a Senior Research Engineer with IPB&F and CCUS Consortiums at Texas A&M University. Her research focuses on reservoir multiphase flow simulation, enhanced and improved oil recovery, CCS, in-situ hydrogen generation, complex rock–fluid interactions, PVT characterization, and nanofluid systems. She has made significant contributions to advancing the understanding of EOR and carbon storage processes through the integration of experimental and numerical methodologies.
- Postdoctoral Research Associate – University of Houston
- Ph.D. in Petroleum Engineering – Istanbul Technical University
- M.Sc. in Petroleum Engineering – Istanbul Technical University
- B.Sc. in Petroleum Engineering – Istanbul Technical University
Her expertise includes laboratory management; experimental design and execution such as core flooding, swelling, wettability, and miscibility tests, as well as numerical reservoir modeling, fluid characterization, equation-of-state (EOS) tuning, and geostatistical analysis.
Research Interests
- Enhanced and Improved Oil Recovery for Conventional and Unconventionals
- Carbon Capture and Storage
- Thermodynamics and PVT
- Interaction of phase behavior and flow in porous media
- Reservoir Simulation, Optimization and History Matching
- Heavy Oil Recovery
- Geothermal Energy
Tianjia Huang, Ph.D.
Postdoctoral Researcher
Dr. Huang is a postdoctoral researcher at the IPB&F Consortium, where he specializes in coupled thermal–hydrological–mechanical–chemical (THMC) processes, fracture modeling, and high-performance parallel computing. Specific applications include underground hydrogen storage, particle transport, and geologic waste disposal, with extensions in carbon sequestration, natural gas hydrate systems, geothermal energy, and enhanced petroleum recovery.
- Ph.D. in Petroleum Engineering – Texas A&M University, 2025
- M.S. in Mechanical Engineering – University of Chinese Academy of Sciences, 2020
- B.S. in Civil Engineering – Xi’an JiaoTong University, 2017
His expertise includes numerical reservoir modeling, high-performance simulator development, modeling fracture systems, and optimizing numerical schemes for robust, scalable multi-physics simulations.
Research Interests
- Numerical Modeling of Coupling THMC Dynamics
- Particle Transport in Porous Media
- Parallel Computing
- Reactive Transport Modeling of Geothermal Systems
- Advanced Compositional Simulation
Sardar Asadov, Ph.D.
Postdoctoral Researcher
Dr. Asadov is a postdoctoral researcher at the IPB&F Consortium. His research focuses on advanced reservoir characterization and closed-loop reservoir management, multiphase-flow simulation, and data-driven Machine Learning based optimization for conventional & unconventional oil and gas systems.
- Ph.D. in Petroleum Engineering – The University of Tulsa, 2025
- M.S. in Petroleum Engineering – Istanbul Technical University, 2017
- B.S. in Petroleum Engineering – Azerbaijan State Oil and Industry University, 2014
His expertise includes numerical reservoir modeling, CO₂ and gas Huff-and-Puff process design, hydraulic-fracture simulation, fluid characterization and equation-of-state (EOS) tuning, and gradient-based optimization. Dr. Asadov integrates machine-learning techniques with classical reservoir-engineering methods to automate history matching and improve production forecasting accuracy.
Research Interests
- Reservoir Modeling and Simulation
- Closed Loop Reservoir Management (Numerical Optimization and History Matching)
- Flowing Material Balance Analysis
- Fluid Characterization and EOS Tuning
- CO₂ Capture, Utilization, and Sequestration
- Data Analytics and Machine Learning in Reservoir Engineering
Musa E.M. Ahmed, Ph.D.
Postdoctoral Researcher
Dr. Ahmed is a Postdoctoral Researcher | Subsurface Energy Systems Harold Vance Department of Petroleum Engineering, Texas A&M University. His research focuses on carbon capture, utilization, and storage (CCUS), injectivity assurance, reservoir geochemistry, petrophysics, and formation of damage mitigation in complex reservoir systems. He integrates high-pressure experimental petrophysics with predictive and data-driven modeling to improve understanding of rock–fluid interactions and evaluate long-term storage integrity. His research emphasizes understanding and mitigating injectivity risks in subsurface energy storage systems.
- Ph.D. in Petroleum Engineering – University of Houston, 2025
- M.S. in Petroleum Engineering – King Fahd University of Petroleum & Minerals (KFUPM), 2017
- B.S. in Petroleum Engineering – University of Khartoum, 2013
Dr. Ahmed completed his doctoral studies under the supervision of Dr. Birol Dindoruk, investigating the physicochemical and mechanical impacts of CO₂ storage in saline formations. His research provided experimental constraints on relative permeability hysteresis, capillary trapping, and interfacial tension evolution, contributing to improved assessment of long-term containment security in geological carbon storage.
In addition to his fundamental research, Dr. Ahmed has contributed to the invention and development of subsurface technologies. During his work in the Middle East, he developed a patented green formulation (U.S. Patent 11,591,509) for iron sulfide scale removal, offering a biodegradable alternative to conventional treatments. His graduate research also led to the development of a novel emulsified acid system utilizing nanoparticles for stimulation of deep carbonate reservoirs.
His technical expertise includes HPHT core analysis, geochemical reaction kinetics, and the design of environmentally responsible treatment fluids. He combines experimental measurements with machine-learning approaches to characterize reservoir heterogeneity and support effective fluid injection strategies for carbon storage and advanced recovery applications.
Research Interests
- Carbon Capture, Utilization, and Storage (CCUS)
- Injectivity Assurance and Formation Damage
- Reservoir Geochemistry and Rock–Fluid Interactions
- Relative Permeability and Multiphase Flow
- Chemical Technologies for Scale Management and Stimulation
- Predictive and Data-Driven Modeling in Petrophysics
PHD Student
Cenk Temizel
Cenk Temizel is an energy professional and reservoir engineer with over 20 years of international experience across Saudi Aramco, Aera Energy LLC (Shell–ExxonMobil affiliate), Halliburton, and Schlumberger. His work spans conventional and unconventional reservoirs, secondary and tertiary recovery, IOR/EOR processes, full-field development planning, reservoir simulation, reserves estimation, hydraulic fracturing modeling, and geomechanics.
- M.S. in Petroleum Engineering – University of Southern California, 2005
- B.S. in Petroleum Engineering – Middle East Technical University, 2003
He has authored more than 100 technical publications in reservoir management, enhanced oil recovery, reservoir simulation, unconventional resources, CO₂ storage, geothermal systems, capacitance–resistance modeling, and AI applications in petroleum engineering, and holds several U.S. patents.
His honors include the Saudi Aramco Technical Contribution Award (2021), Halliburton Award for Technology Innovation (2012), SPE Regional Reservoir Description and Dynamics Award, and multiple industry recognitions.
Research Interests
- Physics-based and AI-driven modeling and optimization of subsurface processes
- Reactive and non-reactive transport dynamics in porous media
- Pore-scale modeling and molecular dynamics (MD) of subsurface systems
- Subsurface CO₂ and H₂ storage and enhanced recovery processes
- Geothermal energy systems
- Reservoir simulation, optimization, and intelligent field applications
Jose Augusto Benavides Arancibia
Jose is Graduate Research Assistant at IPB&F Consortium.
- Ph.D. in Petroleum Engineering – Texas A&M University (currently)
- M.S. in Petroleum Engineering – University of Houston
- B.S. in Petroleum Engineering – Middle East Technical University, 2003
Research Interests
- Numerical Reservoir Modeling
- Machine learning applied to CCS and EOR processes.
Alok Kumar
Alok Kumar is a Ph.D. researcher at the University of Houston working under the supervision of Prof. Birol Dindoruk. His research focuses on the development of polymer-grafted nanoparticles, aqueous CO₂ nanobubbles, and silica nanopore systems for EOR and sustainable process design. He integrates experimental methods (rheology, TGA, DLS, etc.) with molecular simulations (LAMMPS, DFT) and data-driven modeling to understand interfacial transport and structure–property relationships in complex fluids.
- Ph.D. in Chemical Engineering – University of Houston (2023-present)
- B.Tech. in Chemical Engineering – Rajiv Gandhi Institute of Petroleum Technology (RGIPT), 2023
His expertise includes nanofluid design, polymer grafting, rheological characterization, CO₂ utilization, and non-equilibrium molecular dynamics simulations.
Research Interests
- Nanobubble and Nanofluid Engineering for EOR
- Polymer-Grafted Nanoparticles and Hybrid Systems
- Rheology and Interfacial Phenomena
- Molecular Dynamics and DFT Simulations
- CO₂ Utilization and Storage
- AI/ML for Sustainable Materials Design
Research Associate
Utkarsh Sinha
Utkarsh Sinha is a Senior Research Engineer at Xecta Digital Labs and a Volunteer Research Associate with the Interaction of Phase Behavior and Flow (IPB&F) Consortium, working closely with Prof. Birol Dindoruk. He specializes in hybrid physics–data-driven reservoir modeling, integrating first-principles physics with machine learning to develop scalable and deployment-ready subsurface solutions.
- M.E. in Petroleum Engineering – University of Houston, 2017
- B.S. in Chemical Engineering – Vellore Institute of Technology, 2016
His research focuses on physics-informed machine learning to enhance compositional and black-oil PVT modeling, CO₂ injection and CCUS processes, reservoir simulation and forecasting and production forecasting and optimization.
He completed a Special Research Project under Prof. Birol Dindoruk (Jan–Nov 2018) focused on developing viscosity correlations to quantify the impact of asphaltene volume on heavy oil systems, which laid the foundation for his subsequent work in scientific machine learning and hybrid modeling for energy applications.
Research Interests
- Hybrid Physics–Data-Driven Modeling for Subsurface Energy Systems
- Reduced-Order and Surrogate Modeling for High-Fidelity Reservoir Simulation
- Generative AI and Large-Scale Learning Frameworks for Energy Systems Engineering
- Reservoir Fluid Phase Behavior and Miscible Gas Injection
- Production Optimization and Artificial Lifts Engineering