Mohammad Sayyafzadeh

Mohammad Sayyafzadeh

School of Physics, Chemistry and Earth Sciences

Faculty of Sciences, Engineering and Technology

Mohammad has developed research interests in the field of computational mathematics, in particular, optimisation, machine learning, uncertainty quantification, spatial statistics and numerical simulation. 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 and coal seam gas) and carbon storage. Mohammad also actively promotes the fourth industrial revolution (industry 4.0), in particular, artificial intelligence (AI), with applications to sustainable natural resources.

Mohammad 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 has 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 over 50 papers in reputable journals and conferences in collaboration with different institutions/organisations. He has successfully secured several research grants as the lead investigator and many 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 over honours students and several 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
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

  • Journals
    Year Citation
    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 Scopus21 WoS20
    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.
    DOI
    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 Scopus64 WoS49
    2012 Sayyafzadeh, M., Haghighi, M., Bolouri, K., & Arjomand, E. (2012). Reservoir characterisation using artificial bee colony optimisation. APPEA Journal, 52(1), 115-128.
    DOI
    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 Scopus17 WoS12

  • Conference Papers
    Year Citation
    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.
    Scopus1
    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.
    DOI
    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.
    DOI
    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.
    DOI
    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.
    DOI Scopus3
    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.
    DOI Scopus4
    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.
    Scopus17
  • Multi-scale modelling of geochemical and bio-reactive transport in sedimentary rocks for underground hydrogen storage 
  • Formation micro-imaging log automated interpretation using deep learning and image segmentation
  • A mathematical model for the stress analysis of CO2 storage in coal seams
  • Deployment of model calibration algorithm on Cloud as an API 
  • Full-parameterised history matching by stochastic wavelet bases for highly heterogeneous reservoirs 
  • Enhanced gas recovery using improved flow-back in fracture treatment in tight gas reservoirs, Cooper Basin

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
    2025 External Supervisor Underground Hydrogen Storage in Sedimentary Rocks - Multi Scale Geochemical and Bio-reactive Transport Modelling Doctor of Philosophy Doctorate Full Time Mr Hassan Golghanddashti

  • Past Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2019 - 2023 Co-Supervisor Multi-scale, Multi-dimensional Reservoir Characterization Using Advanced Analytics and Machine Learning Doctor of Philosophy Doctorate Part Time Mr Roozbeh Koochak
    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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