Australian School of Petroleum & Energy Resources
Faculty of Engineering, Computer and Mathematical Sciences
I joined Australian School of Petroleum as a lecturer in 2013. I have a B.Sc. in Chemical engineering from Tehran Polytechnic, an M.Sc. in Reservoir Engineering from Tehran Polytechnic and a Ph.D. in Petroleum Engineering from the University of Adelaide. My main field of research is Applied and Computational Mathematics targeting Reservoir Engineering problems, such as history matching, field development optimisation and uncertainty quantification, and my teaching interest includes mathematical and numerical modelling of fluid flow in reservoir rocks, inverse modelling, optimisation and geostatistics.
My main field of research is Applied and Computational Mathematics targeting Reservoir & Production Engineering problems. That includes
- Computer-assisted algorithms for history matching
History matching is a nonlinear and computationally intensive inverse problem in which it is sought to tune (calibrate) the coefficients, initial and/or boundary conditions of nonlinear PDEs corresponding to multi-phase flow in porous media, based on observed data.
- Techniques for uncertainty quantification
The propagation of uncertainty in the parameters of interest (e.g., reservoir performance forecasts) can be obtained by drawing samples from a nonlinear and high-dimensional posterior probability density function (in a Bayesian framework).
- Robust optimisation algorithms for field development planning and reservoir flooding improvement under geological uncertainties
These are numerical optimisation exercises with a highly nonlinear and uncertain (noisy) objective (fitness) function. Each function evaluation needs multiple execution of computationally expensive reservoir simulation.
- Surrogate (proxy) modelling techniques for reducing computational costs
Reservoir production optimisation problems are computationally intensive, due to the nature of PDEs solved numerically for production forecasting. The computational costs associated with optimisation and calibration problems can be reduced by applying properly an approximation functions in the workflow.
- Linear and nonlinear Algebraic transformation methods for dimensionality reduction
A dimensionality reduction technique can improve the calibration problems, given the fact that the data are correlated to some extent and finding a basis that spans the search space will improve the process.
- Modelling of unconventional resources (such as CBM and tight-sand reservoirs), ECBM, CO2 sequestration and uncharacteristic phenomena in conventional reservoirs
In order to simulate reservoir performance in some cases, e.g., unconventional plays, sometimes, it is required to tweak the existing tools or solve numerically a new set of governing PDEs that replicates the phenomenon.
- Data Analytics (DA) for fast decision making
DA is gaining popularity in Oil & Gas industry in the recent years, due to the massive information gathered everyday with less time to analyse. DA can be an alternative to the physical-based simulators for making fast decisions.
Date Position Institution name 2013 Lecturer The 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 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 Scopus2 WoS1
2019 Sayyafzadeh, M., & Alrashdi, Z. (2019). Well controls and placement optimisation using response-fed and judgement-aided parameterisation: Olympus optimisation challenge. Computational Geosciences, 25 pages.
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.
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 Scopus3 WoS2
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 Scopus11 WoS9
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 Scopus29 WoS25
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, 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 Scopus4 WoS1
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 Scopus17 WoS15
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 Scopus23 WoS21
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 Scopus11 WoS10
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 Scopus26 WoS23
2012 Sayyafzadeh, M., Haghighi, M., Bolouri, K., & Arjomand, E. (2012). Reservoir characterisation using artificial bee colony optimisation. APPEA Journal, 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 Scopus11 WoS8
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 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. 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 16th European Conference on the Mathematics of Oil Recovery, ECMOR 2018 (pp. 1592-1606). Netherlands: European Association of Geoscientists & Engineers (EAGE). 2017 Sayyafzadeh, M. (2017). Uncertainty quantification using a multi-objectivised randomised maximum likelihood method. In EAGE (pp. 1-5). Paris, France: EarthDoc.
2017 O'Reilly, D., Haghighi, M., Flett, M., & 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. 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).
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 (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 (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., 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.
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.
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.
- Full-parameterised history matching by stochastic wavelet bases for highly heterogeneous reservoirs (Lead Investigator), SANTOS Ltd, 79k. (2016-2018)
- Enhanced gas recovery using improved flow-back in fracture treatment in tight gas reservoirs, Cooper Basin, (Co-Investigator), client: SANTOS Ltd, 80k. (2014-2017)
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. Introduction to Petroleum Engineering (Production Engineering Part): The aim of the course is to provide students with a broad overview of introduction to petroleum engineering in order that advanced courses in subsequent years can be understood within a broader petroleum engineering context.
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 2018 Co-Supervisor Optimisation of Well Placement, Trajectory and Control Under Uncertainty Doctor of Philosophy Doctorate Full Time Mr Yazan Arouri 2018 Co-Supervisor Innovative Techniques for Reservoir Surveillance and Interpretation in the Mature Windalia Waterflood Doctor of Philosophy Doctorate Part Time Mr Daniel O'Reilly 2016 Co-Supervisor CO2 as an Agent for Enhanced Oil Recover: A Reservoir and Geomechanical Analysis Doctor of Philosophy Doctorate Part Time Mr Abbas Movassagh 2014 Co-Supervisor The Potentiality of Enhanced Oil Recovery by Alkaline, Sunfactant and Polymen Flooding Based on Applied Reservoir Simulation Doctor of Philosophy Doctorate Part Time Mrs Sume Sarkar
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