Mohammad Sayyafzadeh

Dr Mohammad Sayyafzadeh

Senior Lecturer

School of Chemical Engineering

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

Mohammad has developed research interests in the field of computational mathematics, in particular, optimisation, machine learning, uncertainty quantification, spatial statistics, numerical simulation and image processing. Mohammad's scientific activities include developing new theories and translating research into impact to address industry and community concerns. He enjoys solving problems and developing prototype software in an expansive range of engineering and sciences subjects, including energy (responsible oil&gas, hydrogen, Li-ion batteries and coal seam gas), climate change mitigation (carbon storage) and hydrology (groundwater). Mohammad also actively promotes the fourth industrial revolution (industry 4.0), in particular, artificial intelligence (AI), with applications to sustainable natural resources.

Mohammad is currently a senior lecturer in the faculty of sciences, engineering and technology at the University of Adelaide. He holds a B.Sc. in Chemical Engineering and an M.Sc. in Advanced Reservoir Engineering, both from Amirkabir University of Technology (formerly known as Tehran Polytechnic), and have a PhD from the University of Adelaide, completed in 2013 with a prestigious award, Dean's Commendation for Doctoral Thesis Excellence.

Mohammad has co-authored 48 papers in reputable journals and conferences in collaboration with different institutions/organisations. He has successfully secured four research grants as the lead investigator and two projects as a chief investigator. In addition, Mohammad has supervised many joint projects with small and large companies.

Mohammad has a well-demonstrated passion for education and mentorship, and as a supervisor, he always goes above and beyond to support his students. He has supervised 51 honours students and three PhD students to the completion, with many prestigious awards. Mohammad has also initiated several educational activities, e.g., developing new courses and implementing active learning in his teaching. He has built materials for several core courses, e.g., (1)- data analytics, (2)- uncertainty modelling and (3)- advanced reservoir simulation. Along with teaching and coordinating these courses, He gives lectures on geostatistics and reservoir simulation.

My field of research is computational mathematics with applications to subsurface fluid flow and storage to model transport phenomena in porous rocks and characterise heterogeneous media.

 

The main areas of technical expertise are:

Artificial neural networks              Convolutional | Multilayer perceptron | Autoencoder | GAN | Recurrent
Machine learning Clustering | Dimensionality reduction | Classification | Predictive models
Optimisation Stochastic optimisation | Gradient-based | Metaheuristic | Surrogates | Pareto
Uncertainty quantification Bayesian | Markov chain Monte Carlo | Data assimilation
Multiphysics simulation Finite element method (FEM)
Spatial statistics Multiple-point statistics | Random fields | Gaussian process | Variogram
Inverse problem theory Adjoint gradient | Regularisation | Dimensionality reduction | Quasi-Newton
Reservoir simulation Black-oil | Compositional |  Finite Volume Method

    • Appointments
      Date Position Institution name
      2022 - ongoing Senior Lecturer University of Adelaide
      2013 - 2022 Lecturer University of Adelaide
      2008 - 2010 Research Fellow Institute of Petroleum Engineering

    • Awards and Achievements
      Date Type Title Institution Name Country Amount
      2019 Award Commercial Accelerator Scheme (CAS) Adelaide Enterprise Australia -
      2013 Research Award Dean's Commendation for Doctoral Thesis Excellence, - Australia -
      2009 Scholarship Scholarship for international students SANTOS Ltd Australia -
      2009 Scholarship Scholarship for international students The University of Adelaide - -

    • Education
      Date Institution name Country Title
      2010 - 2013 The University of Adelaide Australia Ph.D.
      2007 - 2010 Amirkabir University of Technology (Tehran Polytechnic) Iran M.Sc.
      2003 - 2007 Amirkabir University of Technology (Tehran Polytechnic) Iran B.Sc.

    • Research Interests
      Artificial Intelligence Numerical and Computational Mathematics Optimisation Petroleum and Reservoir Engineering Statistics & Probability
    • Multi-scale modelling of geochemical and bio-reactive transport in sedimentary rocks for underground hydrogen storage (iPhD project)
    • Formation micro-imaging log automated interpretation using deep learning and image segmentation (Research contract)
    • A mathematical model for the stress analysis of CO2 storage in coal seams (Seed fund)
    • Deployment of model calibration algorithm on Cloud as an API (commercialisation award)
    • Full-parameterised history matching by stochastic wavelet bases for highly heterogeneous reservoirs (Research contract)
    • Enhanced gas recovery using improved flow-back in fracture treatment in tight gas reservoirs, Cooper Basin (Research contract)

    My teaching interest includes mathematical and numerical modelling of fluid flow in reservoir rocks, inverse problem theory, uncertainty quantification, numerical optimisation and geostatistics. I currently teach the following courses: 

    1. Reservoir Simulation: The course gives the theoretical basis and practical fundamentals for mathematical modelling and numerical simulation of fluid flow in petroleum reservoirs. The governing laws and equations required for the modelling of single-phase and multi-phase flow in porous media, such as mass conservation, Darcy, equation of state, rock compressibility, capillary pressure and relative permeability, are reviewed. By combining these laws and equations, the corresponding partial differential equations are derived. The numerical methods for solving the governing partial differential equations using finite difference methods are presented.

    2. Reservoir Characterisation and Modelling: The course has three main components. 1) Data sources, quality and analysis, including spatial analysis. 2) Generating 3D models of reservoir properties - classical gridding and mapping, kriging as a data-driven (variogram) form of classical mapping (estimation) and a means of data integration. Simulation techniques are introduced as a means of assessing uncertainty resulting from heterogeneity. 3) Scaling of grids and property models for the purpose of reservoir simulation is the final topic.

    3. Advanced Topics in Numerical Reservoir Simulation: This course reviews the governing PDEs of multi-phase flow in porous media derived with a black-oil phase-behaviour approach, and presents the derivation of the PDEs with a compositional phase-behaviour approach (using both 2-parameter and 3-parameter equation of state). A commonly-used numerical method (finite volume method) for solving the governing PDEs is discussed, and space discretisation (27-point and 7-point) using quadrilateral grids, nonorthogonal (corner-points) and orthogonal (block-centred), is reviewed. An overview of Newton-Raphson linearisation methods in fully-implicit, IMPES and AIM scheme, is given. Iterative linear solvers for sparse matrixes are reviewed, and a few techniques for paralleling and tuning the solvers are discussed. The course, in addition to the fundamentals, covers several practical and special topics in reservoir simulation, such as, placement of deviated and multilateral wells, group controls for constraint handling, local grid refinement and coarsening, miscible and immiscible gas flooding, gas condensate, regionalisation (PVT, equilibrium and SCAL), dual porosity model for naturally fractured rocks, adsorption models, aquifer models, rock compaction/swelling and history matching. 

    4. Uncertainty Modelling: The course gives the theoretical basis and practical fundamentals for uncertainty quantification and modelling (forward and backward), inverse problems and numerical optimisation. It outlines the types and sources of uncertainty, and the importance of uncertainty modelling in decision-making processes. The forward propagation of the uncertainty in the parameters of interest using different techniques, such as Monte Carlo simulation and experimental design methods, is discussed, and techniques used for drawing samples (unconditioned or directly conditioned) from multivariate distributions are reviewed. A particular attention is paid to inverse modelling (in linear and nonlinear problems) with a Bayesian approach. Popular calibration algorithms, gradient-based (steepest descent and quasi-Newton) and derivative-free used for approximating/estimating Maximum a Posteriori (MAP) and Maximum Likelihood (ML) are discussed. Gradient computation/approximation techniques in high-dimensional problems are also reviewed. The fundamentals of Markov chain Monte Carlo (MCMC) are discussed, and different techniques used for the approximation (sampling) of posterior probability density function, such as Metropolis–Hastings algorithm, data assimilation (ensemble Kalman filter) and reduced-order-model-assisted and surrogate (metamodel)-assisted algorithms, are presented and discussed. This course also reviews the algorithms and techniques used to optimise noisy single and multi-objective functions (with and without constraints), such as might be found field development and production optimisation under geological uncertainty problems.

    5. Data Analytics: The aim of the course is to provide students with a broad overview of machine learning to oil and gas. The theory and fundamentals, as well as understanding data driven methods are covered. Real field examples will equip students to apply data analytics and machine learning methods in petroleum engineering.

     

    • Current Higher Degree by Research Supervision (University of Adelaide)
      Date Role Research Topic Program Degree Type Student Load Student Name
      2023 Co-Supervisor Machine learning assisted carbon capture and storage: modeling and optimization Doctor of Philosophy Doctorate Full Time Mr Afshin Tatar
      2019 Co-Supervisor Reservoir characterization using stochastic wavelet basis Doctor of Philosophy Doctorate Part Time Mr Roozbeh Koochak

    • Past Higher Degree by Research Supervision (University of Adelaide)
      Date Role Research Topic Program Degree Type Student Load Student Name
      2018 - 2022 Co-Supervisor Computationally efficient techniques for well control and placement optimization under geological uncertainty Doctor of Philosophy Doctorate Full Time Mr Yazan Arouri
      2018 - 2021 Co-Supervisor Analytical Models for Managing and Predicting the Performance of Mature Waterflood Reservoirs Doctor of Philosophy Doctorate Part Time Mr Daniel O'Reilly
      2016 - 2022 Co-Supervisor Integrating Surface Texture Mapping and Roughness Analysis in Hydraulic Fracturing Simulation Doctor of Philosophy Doctorate Part Time Mr Abbas Movassagh
    • Position: Senior Lecturer
    • Phone: 83138023
    • Email: mohammad.sayyafzadeh@adelaide.edu.au
    • Campus: North Terrace
    • Building: Santos Petroleum Engineering, floor 2
    • Org Unit: Mining and Petroleum Engineering
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