Dr Mohammad Sayyafzadeh

Lecturer
Dr Mohammad Sayyafzadeh
  Org Unit Australian School of Petroleum
  Email mohammad.sayyafzadeh@adelaide.edu.au
  Telephone +61 8 8313 8023
  Location Floor/Room 3 02a ,  Santos Petroleum Engineering Building ,   North Terrace

Ph.D.: The University of Adelaide, Petroleum Engineering, 2010-2013

M.Sc.: Amirkabir University of Technology (Tehran Polytechnic), Petroleum Engineering – Hydrocarbon Reservoir, 2007-2010

B.Sc.: Amirkabir University of Technology (Tehran Polytechnic), Chemical Engineering - Petrochemical, 2003-2007

a.    Dean's Commendation for Doctoral Thesis Excellence

b.    Living Expenses Scholarship for international students from SANTOS Co. to undertake PhD

c.    Tuition Fee Scholarship for international students from the University of Adelaide to undertake PhD

d.    SPE SA chapter scholarship 2011 

e.    SPE SA chapter scholarship 2013 

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

1. Reservoir Simulation, (third year level subject): 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, (third year level subject): 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. Introduction to Petroleum Engineering (Production Engineering Part), (first year level subject): 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. 

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
    This is a numerical optimisation exercise 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), 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.

  • Full-parameterised history matching by stochastic wavelet bases for highly heterogeneous reservoirs (Lead Investigator), SANTOS Ltd, 79,000 AUD.
  • Enhanced gas recovery using improved flow-back in fracture treatment in tight gas reservoirs, Cooper Basin, (Co-Investigator), client: SANTOS Ltd, 80,000 AUD.

Journals:

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:10.1016/j.coal.2017.02.005

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:10.1016/j.petrol.2016.12.015

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:10.1016/j.jngse.2016.06.032

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:10.1016/j.petrol.2016.01.030

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. Retrieved from http://www.publish.csiro.au/AJ/AJ15027

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:10.1016/j.jngse.2015.09.071

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:10.1016/j.coal.2014.06.018

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:10.1080/10916466.2013.833939

Salmachi, A., Sayyafzadeh, M., & Haghighi, M. (2013). Optimisation and economical evaluation of infill drilling in CSG reservoirs using a multi-objective genetic algorithm. 2013 APPEA Conference and Exhibition, 381-389. Retrieved from 

Salmachi, A., Sayyafzadeh, M., & Haghighi, M. (2013). Infill well placement optimization in coal bed methane reservoirs using genetic algorithm. Fuel, 111, 248-258. doi:10.1016/j.fuel.2013.04.022

Sayyafzadeh, M., Haghighi, M., Bolouri, K., & Arjomand, E. (2012). Reservoir characterisation using artificial bee colony optimisation. APPEA Journal, 115-128.

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:10.1016/j.petrol.2011.05.009

Conferences:

Sayyafzadeh, M. (2016). Uncertainty quantification using a self-supervised surrogate-Assisted parallel Metropolis-Hastings algorithm. In 15th European Conference on the Mathematics of Oil Recovery, ECMOR 2016.

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).

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.

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. doi:10.2118/177020-MS

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.

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.

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).

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:10.2118/154544-MS

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:10.2118/141379-MS

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:10.2118/145384-MS

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.

Serve as a reviewer of

·         Fuel Journal 

·         SPE Reservoir Evaluation and Engineering Journal

·         Journal of Petroleum Science and Engineering

·         Environmental Earth Sciences

·         EAGE/SPE 77th & 78th Annual Conference (EUROPEC)

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Entry last updated: Thursday, 25 May 2017

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