Hi, this is Jassem Abbasi!
Scientific ML | Scientific Computing | Reservoir Engineering
Wellcome to my blog!
I am a Scientific Software Developer, Machine Learning Engineer and Reservoir Engineer with experience in working close with university and industry, professional in numerical analysis of physical processes, especially flow in porous media and four years of research, in the field of Scientific Machine Learning, and Deep Learning.
NEWS
>> [Jan. 2025] I am joining CONSIGLI AS, as a Senior Machine Learning Engineer
>> [Jan. 2025] Check out my recent Medium article about solving PDEs by minimization of discrte PDE residuals >>
>> [Jan. 2025] I have presented the results of my recent work in đťť… - Seminar Series at Param-Intelligence Lab [WPI, MA, USA]
>> [Dec. 2024] Our paper has been accepted for presentation at the ML4PS workshop during NeurIPS 2024! >>
>> [Nov. 2024] I had a two-days workshop on Scientific Machine Learning (SciML) and PINNs at the University of Campinas, Brazil. The teaching material is available on SCIML2024workshop
>> [Dec. 2024] I will be presenting at the Interpore Norway Annual Meeting in December 2024, held at the University of Stavanger. >>
>> [Sep. 2024] Awarded a prestigious research commercialization fund (Qualification Project) from Research Council of Norway (~0.5 MNOK)!
>> [May. 2024] Hot!!! I have been awarded the 'Best PhD Student of the Year' award by SPE Stavanger Section! >>
>> [Jun. 2024] I will (co)chair two technical sessions (ML & AI for Geological Characterization) at EAGE Annual Conference 2024 in Oslo.
>> [Feb. 2024] I am going to present in NCS2030 Monthly Webinar Series.
>> [Apr. 2024] I am going to present in ENERGY NORWAY 2024 Conference, in Stavanger, Norway. >>
>> [Nov. 2023] I have presented at OTD conference in Stavanger, Norway.
>> [Jun. 2023] I have attended in PINNs summer school at KTH, Stockholm, Sweden. >>
>> [Feb. 2023] I have been awarded an PLOGEN innovation fund from Validé. >>
>> [Feb. 2023] I attended in in CSD (Coupled Subsurface Dynamics 2023) Winter School in Bergen, Norway. >>
Skills [Computer Science]
- Python
- C#
- MATLAB
- PyTorch
- JAX
- TensorFlow
- MLOps
- Sklearn, SciPy, (…)
- Git
- Azure ML
- Databases (MySQL)
- GPU Computing
- PowerBI
- Adobe Photoshop
- OOP (Object Oriented Programming)
Skills [Reservoir Engineering]
- Reservoir Simulation [ECLIPSE, CMG, MRST, ...]
- History-Matching [Inverse Evaluation]
- Special Core Analysis [SCAL]
- Improved/Enhanced Oil Recovery [EOR]
- Core to Field Upscaling
- Computational Fluid Dynamics [COMSOL, OpenFoam]
- Petrel RE
- PVTi, PVTsim (…)
- Pipesim
- Material Balance [MBAL]
- PTA (welltesting)
EXPERIENCES
Halliburton, Norway [2025 - Present]
Senior Technical Software Developer
CONSIGLI AS [2025]
Senior Machine Learning Engineer
ETH Zurich, Switzerland [Summer 2024]
Visiting Researcher at Department of Applied Mathematics
UNIVERSITY OF STAVANGER, Norway [2021-2025]
PhD Research Fellow in Petroleum Technology/ Artificial Intelligence
EQUINOR ASA, Norway [2022]
Subsurface Engineer | Reservoir Simulation (intern)
ZODAN SOLUTIONS LTD., UK [2018-2020]
Scientific Software Developer
SHIRAZ UNIVERSITY/PETROAZMA OIL COMPANY [2016-2018]
Reservoir Simulation Engineer | Research Assistant
PETROTIRAZIS OIL COMPANY PTED. [2016]
Scientific Software Developer (intern)
Publications [selected]
ML4PS at NeurIPS 2024
History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs)
- Multiscale Flow in Porous Media
- Physics Informed Neural Networks
- Inverse Calculations
Under Review
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
- Multiscale Flow in Porous Media
- Physics Informed Neural Networks
- Inverse Calculations
SPE Journal [2024]
Application of Physics-Informed Neural Networks for Estimation of Saturation Functions from Countercurrent Spontaneous Imbibition Tests
- Reservoir Simulation
- Physics Informed Neural Networks
- Inverse Calculations
Neurocomputing [2024]
Physical activation functions (PAFs): An approach for more efficient induction of physics into physics-informed neural networks (PINNs)
- Activation Functions
- Physics Informed Neural Networks
- Inverse Calculations
Energy and Fuels [2023]
Simulation and Prediction of Spontaneous Imbibition at Early and Late Times Using Physics-Informed Neural Networks
- PINNs
- Python
- PyTorch
Heliyon [2022]
Theoretical comparison of two setups for capillary pressure measurement by centrifuge
- Numerical Simulation
- Python
SPE Europec [2022]
Improved Initialization of Non-linear Solvers in Numerical Simulation of Flow in Porous Media with a Real-time Deep Learning Approach
- Python
- Numerical Simulation
- TensorFlow
Korean Journal of Chemical Engineering [2020]
A multiscale study on the effects of dynamic capillary pressure in two-phase flow in porous media
- Image Analysis
- Data Evaluation
Petroleum Science [2020]
On the Impact of Solutal Marangoni Convection during Chemical Flooding for Improved Oil Recovery
- Image Analysis
- Data Evaluation
Journal of Petroleum Science and Engineering [2018]
A new numerical approach for investigation of the effects of dynamic capillary pressure in imbibition process
- Numerical Simulation