Farshad Sadeghpour | Data prediction | Best Researcher Award

Dr. Farshad Sadeghpour | Data prediction | Best Researcher Award

Farshad Sadeghpour (b. 1996) šŸ‡®šŸ‡· is a Petroleum Engineer and Data Scientist šŸ’»šŸ›¢ļø with expertise in reservoir engineering, petrophysics, and AI applications in the energy sector. Based in Tehran, Iran šŸ“, he holds a Master’s and Bachelor’s in Petroleum Exploration. With extensive experience in EOR, SCAL/RCAL analysis, and machine learning, Farshad has contributed to both academic and industrial R&D at RIPI, NISOC, and PVP. He has published multiple research articles šŸ“š, won international awards šŸ†, and participated in key petroleum projects. He served in the military šŸŖ– and actively collaborates with academia and industry on AI-driven energy solutions.

Profile

Education šŸŽ“

šŸ§‘ā€šŸŽ“ Master’s in Petroleum Engineering (Petroleum Exploration), Petroleum University of Technology, Abadan šŸ‡®šŸ‡· (2019–2022) | GPA: 18.82/20
šŸŽ“ Bachelor’s in Petroleum Engineering, Islamic Azad University (Science & Research Branch), Tehran šŸ‡®šŸ‡· (2015–2019) | GPA: 19.14/20
šŸ“š Courses covered include reservoir engineering, geomechanics, well-logging, and advanced data analytics.
šŸ› ļø Projects include COā‚‚ storage modeling, permeability prediction via AI, and LWD-based mud loss forecasting.
šŸ“Š Developed key industry collaborations with NISOC, RIPI, and OEID through thesis, internships, and military service projects.
šŸ’” Honed computational and simulation skills using MATLAB, Python, COMSOL, Petrel, and ECLIPSE.
šŸ›ļø Academic mentors: Dr. Seyed Reza Shadizadeh, Dr. Bijan Biranvand, Dr. Majid Akbari.

Experience šŸ‘Øā€šŸ«


šŸ”¬ Computer Aided Process Engineering (CAPE) – Petroleum Reservoir Engineer (Nov 2024–Present)
šŸ›¢ļø Petro Vision Pasargad – Reservoir Engineer & Lab Operator (Sep 2023–May 2024)
🧠 Research Institute of Petroleum Industry (RIPI) – Petroleum Engineer, Data Scientist (Mar 2023–Apr 2024)
šŸ­ National Iranian South Oil Company (NISOC) – Petroleum Engineer, Petrophysicist (Mar 2021–Nov 2024)
🧪 Internships: NIOC – Exploration Management, Oil & Energy Industries Development (OEID)
šŸ“Š Key contributions include EOR analysis, SCAL/RCAL lab testing, permeability modeling, machine learning pipelines, and field data analysis.
🧾 Delivered reports, simulations, and AI models supporting production optimization and reservoir characterization.

Awards & Recognitions šŸ…

šŸ„‰ 3rd Prize Winner – EAGE Laurie Dake Challenge 2022 (Madrid, Spain) šŸŒ
šŸŽ–ļø Recognized for thesis excellence in AI-driven mud loss prediction with NISOC collaboration
šŸ“Œ Acknowledged during military service project with RIPI for developing ANN-based well log models
šŸ… Published in high-impact journals such as Energy, Geoenergy Science and Engineering, and JRMGE
āœļø Co-author of multiple peer-reviewed papers and under-review articles across petroleum engineering disciplines
šŸ”¬ Worked alongside top researchers including Dr. Ostadhassan, Dr. Gao, and Dr. Hemmati-Sarapardeh
šŸ› ļø Actively participated in multidisciplinary teams combining AI, geomechanics, and petrophysics
šŸ“¢ Regular presenter and contributor at petroleum conferences and AI-in-energy seminars.

Research Interests šŸ”¬

šŸ“Œ AI & ML applications in petroleum engineering šŸ§ šŸ›¢ļø – including ANN, genetic algorithms, and deep learning
šŸ“Š Mud loss zone prediction, formation permeability modeling, COā‚‚ storage feasibility using ML
🧪 Experimental rock mechanics: nanoindentation, geomechanical upscaling, SCAL/RCAL testing
šŸ“ˆ Petrophysical property estimation in carbonate and unconventional reservoirs
šŸŒ Reservoir simulation, LWD analysis, and smart data integration using Python, Petrel, COMSOL
šŸ“– Notable studies include: elastic modulus upscaling, kerogen behavior under pyrolysis, RQI/FZI modeling
šŸ”¬ Interdisciplinary projects bridging data science with geoscience and reservoir engineering
šŸ¤ Collaboration with academic and industry leaders to develop practical, AI-driven solutions for energy challenges.

PublicationsĀ 
  • Elastic Properties of Anisotropic Rocks Using an Stepwise Loading Framework in a True Triaxial Testing Apparatus

    Geoenergy Science and Engineering
    2025-04 |Ā Journal article
    CONTRIBUTORS:Ā Farshad Sadeghpour;Ā Hem Bahadur Motra;Ā Chinmay Sethi;Ā Sandra Wind;Ā Bodhisatwa Hazra;Ā Ghasem Aghli;Ā Mehdi Ostadhassan
  • Storage Efficiency Prediction for Feasibility Assessment of Underground CO2 Storage: Novel Machine Learning Approaches

    Energy
    2025-04 |Ā Journal article
    CONTRIBUTORS:Ā Farshad Sadeghpour
  • A new petrophysical-mathematical approach to estimate RQI and FZI parameters in carbonate reservoirs

    Journal of Petroleum Exploration and Production Technology
    2025-03 |Ā Journal article
    CONTRIBUTORS:Ā Farshad Sadeghpour;Ā Kamran Jahangiri;Ā Javad Honarmand
  • Effect of stress on fracture development in the Asmari reservoir in the Zagros Thrust Belt

    Journal of Rock Mechanics and Geotechnical Engineering
    2024-11 |Ā Journal article
    CONTRIBUTORS:Ā Ghasem Aghli;Ā Babak Aminshahidy;Ā Hem Bahadur Motra;Ā Ardavan Darkhal;Ā Farshad Sadeghpour;Ā Mehdi Ostadhassan
  • Comparison of geomechanical upscaling methods for prediction of elastic modulus of heterogeneous media

    Geoenergy Science and Engineering
    2024-08 |Ā Journal article
    CONTRIBUTORS:Ā Farshad Sadeghpour;Ā Ardavan Darkhal;Ā Yifei Gao;Ā Hem B. Motra;Ā Ghasem Aghli;Ā Mehdi Ostadhassan

Quanying Lu | Forecasting | Best Researcher Award

Dr. Quanying Lu | Forecasting | Best Researcher Award

Dr. Quanying Lu is an Associate Professor at Beijing University of Technology, specializing in energy economics, forecasting, and systems engineering. šŸŽ“ She completed her Ph.D. at the University of Chinese Academy of Sciences and has published 30+ papers in top journals, including Nature Communications and Energy Economics. šŸ“š She has held postdoctoral and research positions in prestigious institutions and actively contributes to policy research. šŸŒ

Profile

Education šŸŽ“

  • Ph.D. (2017-2020): University of Chinese Academy of Sciences, School of Economics and Management, supervised by Prof. Shouyang Wang.
  • M.Sc. (2014-2017): International Business School, Shaanxi Normal University, supervised by Prof. Jian Chai.
  • B.Sc. (2010-2014): International Business School, Shaanxi Normal University, Department of Economics and Statistics.

Experience šŸ‘Øā€šŸ«

  • Associate Professor (06/2022–Present), Beijing University of Technology, supervising Ph.D. students.
  • Postdoctoral Fellow (07/2020–05/2022), Academy of Mathematics and Systems Science, Chinese Academy of Sciences.
  • Research Assistant (08/2018–10/2018), Department of Management Sciences, City University of Hong Kong.

Awards & Recognitions šŸ…

  • Outstanding Young Talent, Phoenix Plan, Chaoyang District, Beijing (2024).
  • Young Scholar of Social Computing, CAAI-BDSC (2024).
  • Young Scholar of Forecasting Science, Frontier Forum on Forecasting Science (2024).
  • Young Elite Scientists Sponsorship, BAST (2023).
  • Excellent Mentor, China International “Internet Plus” Innovation Competition (2023).

Research Interests šŸ”¬

Dr. Lu specializes in energy economics, environmental policy analysis, economic forecasting, and systems engineering. šŸ“Š Her research addresses crude oil price dynamics, carbon reduction strategies, and financial market interactions. šŸ’” She integrates machine learning with forecasting models, contributing to sustainable energy and environmental policies. šŸŒ

PublicationsĀ 

[1] Liang, Q., Lin, Q., Guo, M., Lu, Q., Zhang, D. Forecasting crude oil prices: A
Gated Recurrent Unit-based nonlinear Granger Causality model. International
Review of Financial Analysis, 2025, 104124.
[2] Wang, S., Li, J., Lu, Q. (2024) Optimization of carbon peaking achieving paths in
Chinas transportation sector under digital feature clustering. Energy, 313,133887
[3] Yang, B., Lu, Q.*, Sun, Y., Wang, S., & Lai, K. K. Quantitative evaluation of oil
price fluctuation events based on interval counterfactual model (in Chinese).
Systems Engineering-Theory & Practice, 2023, 43(1):191-205.
[4] Lu, Q.*, Shi, H., & Wang, S. Estimating the shock effect of ā€œBlack Swanā€ and
ā€œGray Rhinoā€ events on the crude oil market: the GSI-BN research framework (in
Chinese). China Journal of Econometrics, 2022, 1(2): 194-208.
[5] Lu, Q., Duan, H.*, Shi, H., Peng, B., Liu, Y., Wu, T., Du, H., & Wang, S*. (2022).
Decarbonization scenarios and carbon reduction potential for China’s road
transportation by 2060. npj Urban Sustainability, 2: 34. DOI:
https://www.nature.com/articles/s42949-022-000.
[6] Lu, Q., Sun, Y.*, Hong, Y., Wang, S. (2022). Forecasting interval-valued crude
oil prices via threshold autoregressive interval models. Quantitative Finance,
DOI: 10.1080/14697688.2022.2112065
Page 3 / 6
[7] Guo, Y., Lu, Q.*, Wang, S., Wang, Q. (2022). Analysis of air quality spatial
spillover effect caused by transportation infrastructure. Transportation Research
Part D: Transport & Environment, 108, 103325.
[8] Wei, Z., Chai, J., Dong, J., Lu, Q. (2022). Understanding the linkage-dependence
structure between oil and gas markets: A new perspective. Energy, 257, 124755.
[9] Chai, J., Zhang, X.*, Lu, Q., Zhang, X., & Wang, Y. (2021). Research on
imbalance between supply and demand in China’s natural gas market under the
double -track price system. Energy Policy, 155, 112380.
[10]Lu, Q., Sun, S., Duan, H.*, & Wang, S. (2021). Analysis and forecasting of crude
oil price based on the variable selection-LSTM integrated model. Energy
Informatics, 4 (Suppl 2):47.
[11]Shi, H., Chai, J.*, Lu, Q., Zheng, J., & Wang, S. (2021). The impact of China’s
low-carbon transition on economy, society and energy in 2030 based on CO2
emissions drivers. Energy, 239(1):122336, DOI: 10.1016/j.energy.2021.122336.
[12]Jiang, S., Li, Y., Lu, Q., Hong, Y., Guan, D.*, Xiong, Y., & Wang, S.* (2021).
Policy assessments for the carbon emission flows and sustainability of Bitcoin
blockchain operation in China. Nature Communications, 12(1), 1-10.
[13]Jiang, S., Li Y., Lu, Q., Wang, S., & Wei, Y*. (2021). Volatility communicator or
receiver? Investigating volatility spillover mechanisms among Bitcoin and other
financial markets. Research in International Business and Finance,
59(4):101543.
[14]Lu, Q., Li, Y., Chai, J., & Wang, S.* (2020). Crude oil price analysis and
forecasting :A perspective of ā€œnew triangleā€. Energy Economics, 87, 104721.
DOI: 10.1016/j.eneco.2020.104721.
[15]Chai, J., Shi, H.*, Lu, Q., & Hu, Y. (2020). Quantifying and predicting the
Water-Energy-Food-Economy-Society-Environment Nexus based on Bayesian
networks – a case study of China. Journal of Cleaner Production, 256, 120266.
DOI: 10.1016/j.jclepro.2020.120266.
[16]Lu, Q., Chai, J., Wang, S.*, Zhang, Z. G., & Sun, X. C. (2020). Potential energy
conservation and CO2 emission reduction related to China’s road transportation.
Journal of Cleaner Production, 245, 118892. DOI:
10.1016/j.jclepro.2019.118892.
[17]Chai, J., Lu, Q.*, Hu, Y., Wang, S., Lai, K. K., & Liu, H. (2018). Analysis and
Bayes statistical probability inference of crude oil price change point.
Technological Forecasting & Social Change, 126, 271-283.
[18]Chai, J., Lu, Q.*, Wang, S., & Lai, K. K. (2016). Analysis of road transportation
consumption demand in China. Transportation Research Part D: Transport &
Environment, 2016, 48:112-124.