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|
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 - -
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.
Year Citation 2023 Aslannezhad, M., Ali, M., Kalantariasl, A., Sayyafzadeh, M., You, Z., Iglauer, S., & Keshavarz, A. (2023). A review of hydrogen/rock/brine interaction: Implications for Hydrogen Geo-storage. Progress in Energy and Combustion Science, 95, 35 pages.
2022 Arouri, Y., & Sayyafzadeh, M. (2022). An adaptive moment estimation framework for well placement optimization. Computational Geosciences, 26(4), 957-973.
DOI Scopus4 WoS4
2022 Arouri, Y., Sayyafzadeh, M., & Begg, S. (2022). Adaptive Rank-Based Selection of Geological Realizations for Optimum Field Development Planning. SPE Journal, 27(4), 1869-1886.
DOI Scopus2 WoS1
2022 Arouri, Y., Echeverría Ciaurri, D., & Sayyafzadeh, M. (2022). A study of simulation-based surrogates in well-placement optimization for hydrocarbon production. Journal of Petroleum Science and Engineering, 216, 14 pages.
2022 Arouri, Y., Lake, L. W., & Sayyafzadeh, M. (2022). Bilevel Optimization of Well Placement and Control Settings Assisted by Capacitance-Resistance Models. SPE JOURNAL, 27(6), 3829-3848.
2022 Zare Reisabadi, M., Sayyafzadeh, M., & Haghighi, M. (2022). Stress and permeability modelling in depleted coal seams during CO<inf>2</inf> storage. Fuel, 325, 10 pages.
2022 Koochak, R., Sayyafzadeh, M., Nadian, A., Bunch, M., & Haghighi, M. (2022). A variability aware GAN for improving spatial representativeness of discrete geobodies. Computers and Geosciences, 166, 14 pages.
2021 O'Reilly, D., Haghighi, M., Flett, M., & Sayyafzadeh, M. (2021). Pressure-Transient Analysis for Cold-Water Injection into a Reservoir Coupled with Wellbore-Transient-Temperature Effects. SPE PRODUCTION & OPERATIONS, 36(1), 197-215.
2021 O'Reilly, D., Haghighi, M., Sayyafzadeh, M., & Flett, M. (2021). Analytical rate-transient analysis and production performance of waterflooded fields with delayed injection support. SPE Reservoir Evaluation and Engineering, 24(3), 639-661.
DOI Scopus2 WoS1
2021 Movassagh, A., Haghighi, M., Zhang, X., Kasperczyk, D., & Sayyafzadeh, M. (2021). A fractal approach for surface roughness analysis of laboratory hydraulic fracture. Journal of Natural Gas Science and Engineering, 85, 103703-1-103703-16.
DOI Scopus14 WoS10
2021 Zare Reisabadi, M., Haghighi, M., Sayyafzadeh, M., & Khaksar, A. (2021). Stress distribution and permeability modelling in coalbed methane reservoirs by considering desorption radius expansion. Fuel, 289, 1-11.
DOI Scopus5 WoS5
2020 Sayyafzadeh, M., & Alrashdi, Z. (2020). Well controls and placement optimisation using response-fed and judgement-aided parameterisation: Olympus optimisation challenge. Computational Geosciences, 24(6), 2001-2025.
DOI Scopus10 WoS8
2020 Zare Reisabadi, M., Haghighi, M., Salmachi, A., Sayyafzadeh, M., & Khaksar, A. (2020). Analytical modelling of coal failure in coal seam gas reservoirs in different stress regimes. International Journal of Rock Mechanics and Mining Sciences, 128, 11 pages.
DOI Scopus13 WoS9
2020 Arouri, Y., & Sayyafzadeh, M. (2020). An accelerated gradient algorithm for well control optimization. Journal of Petroleum Science and Engineering, 190, 1-18.
DOI Scopus8 WoS7
2020 Reisabadi, M. Z., Haghighi, M., Sayyafzadeh, M., & Khaksar, A. (2020). Effect of matrix shrinkage on wellbore stresses in coal seam gas: An example from Bowen Basin, east Australia. Journal of Natural Gas Science and Engineering, 77, 12 pages.
DOI Scopus8 WoS6
2019 O'Reilly, D., Haghighi, M., Flett, M., & Sayyafzadeh, M. (2019). Productivity determination for cyclic production using steady state harmonic theory – Application to artificial lift wells. Journal of Petroleum Science and Engineering, 172, 787-805.
2019 Alrashdi, Z., & Sayyafzadeh, M. (2019). (μ + Λ) Evolution strategy algorithm in well placement, trajectory, control and joint optimisation. Journal of Petroleum Science and Engineering, 177, 1042-1058.
DOI Scopus19 WoS13
2018 Koochak, R., Haghighi, M., Sayyafzadeh, M., & Bunch, M. (2018). Rock typing and facies identification using fractal theory and conventional petrophysical logs. The APPEA Journal, 58(1), 102.
2018 Movassagh, A., Haghighi, M., Kasperczyk, D., Sayyafzadeh, M., & Zhang, X. (2018). An experimental investigation into surface roughness of a hydraulic fracture. The APPEA Journal, 58(2), 728-732.
2018 Nguyen, H. T., Sayyafzadeh, M., & Haghighi, M. (2018). Low permeability coal seam gas productivity enhancement by cyclic nitrogen injection technique (an adsorption simulation study). The APPEA Journal, 58(1), 159-167.
2018 Akhondzadeh, H., Keshavarz, A., Sayyafzadeh, M., & Kalantariasl, A. (2018). Investigating the relative impact of key reservoir parameters on performance of coalbed methane reservoirs by an efficient statistical approach. Journal of Natural Gas Science and Engineering, 53, 416-428.
DOI Scopus19 WoS17
2017 Sayyafzadeh, M. (2017). Reducing the computation time of well placement optimisation problems using self-adaptive metamodelling. Journal of Petroleum Science and Engineering, 151, 143-158.
DOI Scopus35 WoS29
2017 Keshavarz, A., Sakurovs, R., Grigore, M., & Sayyafzadeh, M. (2017). Effect of maceral composition and coal rank on gas diffusion in Australian coals. International Journal of Coal Geology, 173, 65-75.
DOI Scopus79 WoS72
2016 Sarkar, S., Haghighi, M., Sayyafzadeh, M., Cooke, D., Pokalai, K., & Mohamed Ali Sahib, F. (2016). A Cooper Basin simulation study of flow-back after hydraulic fracturing in tight gas wells. The APPEA Journal, 56(1), 369-392.
2016 O'Reilly, D., Haghighi, M., Flett, M., & Sayyafzadeh, M. (2016). Pressure and rate transient analysis of artificially lifted drawdown tests using cyclic Pump Off Controllers. Journal of Petroleum Science and Engineering, 139, 240-253.
DOI Scopus8 WoS4
2016 Sayyafzadeh, M., & Keshavarz, A. (2016). Optimisation of gas mixture injection for enhanced coalbed methane recovery using a parallel genetic algorithm. Journal of Natural Gas Science and Engineering, 33, 942-953.
DOI Scopus32 WoS28
2015 Sayyafzadeh, M., Keshavarz, A., Alias, A., Dong, K., & Manser, M. (2015). Investigation of varying-composition gas injection for coalbed methane recovery enhancement: A simulation-based study. Journal of Natural Gas Science and Engineering, 27, 1205-1212.
DOI Scopus44 WoS36
2014 Salmachi, A., Bonyadi, M., Sayyafzadeh, M., & Haghighi, M. (2014). Identification of potential locations for well placement in developed coalbed methane reservoirs. International Journal of Coal Geology, 131, 250-262.
DOI Scopus17 WoS16
2014 Sayyafzadeh, M., Mamghaderi, A., Pourafshary, P., & Haghighi, M. (2014). A fast simulator for hydrocarbon reservoirs during gas injection. Petroleum Science and Technology, 32(20), 2434-2442.
2013 Salmachi, A., Sayyafzadeh, M., & Haghighi, M. (2013). Infill well placement optimization in coal bed methane reservoirs using genetic algorithm. Fuel, 111, 248-258.
DOI Scopus52 WoS46
2012 Sayyafzadeh, M., Haghighi, M., Bolouri, K., & Arjomand, E. (2012). Reservoir characterisation using artificial bee colony optimisation. APPEA Journal, 52(1), 115-128.
2011 Sayyafzadeh, M., Pourafshary, P., Haghighi, M., & Rashidi, F. (2011). Application of transfer functions to model water injection in hydrocarbon reservoir. Journal of Petroleum Science and Engineering, 78(1), 139-148.
DOI Scopus16 WoS12
Year Citation 2018 Keshavarz, A., Akhondzadeh, H., Sayyafzadeh, M., & Zargar, M. (2018). Enhanced Gas Recovery Techniques From Coalbed Methane Reservoirs. In A. Bahadori (Ed.), Fundamentals of Enhanced Oil and Gas Recovery from Conventional and Unconventional Reservoirs (pp. 233-268). USA: Elsevier.
Year Citation 2022 Sayyafzadeh, M., & Guérillot, D. (2022). Rapid permeability upscaling using convolutional neural networks. In European Conference on the Mathematics of Geological Reservoirs 2022, ECMOR 2022. European Association of Geoscientists & Engineers.
2020 Arouri, Y., & Sayyafzadeh, M. (2020). Adaptive moment estimation framework for well placement optimization. In ECMOR 2020 - 17th European Conference on the Mathematics of Oil Recovery. virtual online: European Association of Geoscientists & Engineers.
2018 Sayyafzadeh, M., Koochak, R., & Barley, M. (2018). Accelerating CMA-ES in history matching problems using an ensemble of surrogates with generation-based management. In Proceedings of the 16th European Conference on the Mathematics of Oil Recovery 2018 (ECMOR XVI) (pp. 1592-1606). Netherlands: European Association of Geoscientists & Engineers (EAGE).
2018 Sayyafzadeh, M., & Alrashdi, Z. (2018). Well control, field development and joint optimization using (μ+λ) evolutionary strategy algorithm and a stochastic rank. In EAGE-TNO Workshop on OLYMPUS Field Development Optimization 2018. EAGE Publications BV.
2017 Sayyafzadeh, M. (2017). Uncertainty quantification using a multi-objectivised randomised maximum likelihood method. In EAGE (pp. 1-5). The Netherlands: European Association of Geoscientists & Engineers.
2017 O'Reilly, D. I., Haghighi, M., Flett, M. A., & Sayyafzadeh, M. (2017). Pressure transient analysis for cold water injection into a reservoir with a coupled analytic wellbore model. In Society of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2017 Vol. 2017-January. SPE.
2015 Sayyafzadeh, M., Keshavarz, A., Mohd Alias, A. R., Dong, K. A., & Manser, M. (2015). Enhancing coal bed methane recovery by varying-composition gas injection. In Society of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition, APOGCE 2015. SPE.
2015 Sayyafzadeh, M. (2015). A self-adaptive surrogate-assisted evolutionary algorithm for well placement optimization problems. In Society of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition, APOGCE 2015. SPE.
2015 Wang, K., Peng, X., Du, Z., Haghighi, M., & Sayyafzadeh, M. (2015). DFN model for flow simulation in hydraulically fractured wells with pre-existing natural fractures using unstructured quadrilateral grids. In Proceedings Asia Pacific Unconventional Resources Conference and Exhibition (pp. SPE-177020-MS-1-SPE-177020-MS-20). Brisbane, QLD: Society of Petroleum Engineers.
2015 Sayyafzadeh, M. (2015). History matching by online metamodeling. In Society of Petroleum Engineers - SPE Reservoir Characterisation and Simulation Conference and Exhibition, RCSC 2015 (pp. 513-531). SPE.
2015 Sayyafzadeh, M. (2015). A self-adaptive surrogate-assisted evolutionary algorithm for well placement optimization problems. In Society of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition, APOGCE 2015.
2015 Sayyafzadeh, M., Keshavarz, A., Mohd Alias, A., Dong, K., & Manser, M. (2015). Enhancing coal bed methane recovery by varying-composition gas injection. In Society of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition, APOGCE 2015.
2013 Sayyafzadeh, M. (2013). High-resolution reservoir modeling using image fusion technique in history matching problems. In EAGE Annual Conference and Exhibition incorporating SPE Europec (pp. 1-20). UK: SPE- Society of Petroleum Engineers.
2013 Sayyafzadeh, M., & Haghighi, M. (2013). Assessment of different model-management techniques in history matching problems for reservoir modelling. In 2013 APPEA Conference and Exhibition Vol. 53 (pp. 391-406). Australia: Australian Petroleum Production and Exploration Association.
2013 Salmachi, A., Sayyafzadeh, M., & Haghighi, M. (2013). Optimisation and economical evaluation of infill drilling in CSG reservoirs using a multi-objective genetic algorithm. In 2013 APPEA Conference and Exhibition Vol. 53 (pp. 381-389). Australia: Australian Petroleum Production and Exploration Association.
2012 Sayyafzadeh, M., Haghighi, M., & Carter, J. (2012). Regularization in history matching using multi-objective genetic algorithm and Bayesian framework. In Proceedings of the EAGE Annual Conference & Exhibition incorporating SPE Europec (pp. 1-18). USA: SPE.
2012 Sayyafzadeh, M., & Haghighi, M. (2012). Regularization in history matching using Multiobjective genetic algorithm and Bayesian framework (SPE 154544). In 74th European Association of Geoscientists and Engineers Conference and Exhibition 2012 Incorporating SPE EUROPEC 2012: Responsibly Securing Natural Resources (pp. 2564-2582).
2011 Sayyafzadeh, M., Mamghaderi, A., Pourafshary, P., & Haghighi, M. (2011). A new method to forecast reservoir performance during immiscible and miscible gas-flooding via transfer functions approach. In Society of Petroleum Engineers - SPE Asia Pacific Oil and Gas Conference and Exhibition 2011 Vol. 1 (pp. 464-477). Jakarta: SPE International.
2011 Sayyafzadeh, M., Pourafshary, P., & Rashidi, F. (2011). A novel method to model water-flooding via transfer function approach. In Society of Petroleum Engineers - Middle East Turbomachinery Symposium 2011, METS - 1st SPE Project and Facilities Challenges Conference at METS (pp. 143-154). Doha: Society of Petroleum Engineers.
2010 Sayyafzadeh, M., Pourafshary, P., & Rashidi, F. (2010). Increasing Ultimate Oil Recovery by Infill Drilling and Converting Weak Production Wells to Injection Wells Using Streamline Simulation. In International Oil and Gas Conference and Exhibition in China, 8-10 June, Beijing, China Vol. 4 (pp. 2865-2871). China: Society of Petroleum Engineers.
Year Citation 2016 Sayyafzadeh, M. (2016). Uncertainty quantification using a self-supervised surrogate-Assisted parallel Metropolis-Hastings algorithm. Poster session presented at the meeting of 15th European Conference on the Mathematics of Oil Recovery, ECMOR 2016. EAGE Publications BV.
- 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 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
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