Farshad Sadeghpour | Data prediction | Best Researcher Award

Dr. Farshad Sadeghpour | Data prediction | Best Researcher Award

Farshad Sadeghpour (b. 1996) 🇮🇷 is a Petroleum Engineer and Data Scientist 💻🛢️ with expertise in reservoir engineering, petrophysics, and AI applications in the energy sector. Based in Tehran, Iran 📍, 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 📚, won international awards 🏆, and participated in key petroleum projects. He served in the military 🪖 and actively collaborates with academia and industry on AI-driven energy solutions.

Profile

Education 🎓

🧑‍🎓 Master’s in Petroleum Engineering (Petroleum Exploration), Petroleum University of Technology, Abadan 🇮🇷 (2019–2022) | GPA: 18.82/20
🎓 Bachelor’s in Petroleum Engineering, Islamic Azad University (Science & Research Branch), Tehran 🇮🇷 (2015–2019) | GPA: 19.14/20
📚 Courses covered include reservoir engineering, geomechanics, well-logging, and advanced data analytics.
🛠️ Projects include CO₂ storage modeling, permeability prediction via AI, and LWD-based mud loss forecasting.
📊 Developed key industry collaborations with NISOC, RIPI, and OEID through thesis, internships, and military service projects.
💡 Honed computational and simulation skills using MATLAB, Python, COMSOL, Petrel, and ECLIPSE.
🏛️ Academic mentors: Dr. Seyed Reza Shadizadeh, Dr. Bijan Biranvand, Dr. Majid Akbari.

Experience 👨‍🏫


🔬 Computer Aided Process Engineering (CAPE) – Petroleum Reservoir Engineer (Nov 2024–Present)
🛢️ 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)
🏭 National Iranian South Oil Company (NISOC) – Petroleum Engineer, Petrophysicist (Mar 2021–Nov 2024)
🧪 Internships: NIOC – Exploration Management, Oil & Energy Industries Development (OEID)
📊 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 🏅

🥉 3rd Prize Winner – EAGE Laurie Dake Challenge 2022 (Madrid, Spain) 🌍
🎖️ Recognized for thesis excellence in AI-driven mud loss prediction with NISOC collaboration
📌 Acknowledged during military service project with RIPI for developing ANN-based well log models
🏅 Published in high-impact journals such as Energy, Geoenergy Science and Engineering, and JRMGE
✍️ Co-author of multiple peer-reviewed papers and under-review articles across petroleum engineering disciplines
🔬 Worked alongside top researchers including Dr. Ostadhassan, Dr. Gao, and Dr. Hemmati-Sarapardeh
🛠️ Actively participated in multidisciplinary teams combining AI, geomechanics, and petrophysics
📢 Regular presenter and contributor at petroleum conferences and AI-in-energy seminars.

Research Interests 🔬

📌 AI & ML applications in petroleum engineering 🧠🛢️ – including ANN, genetic algorithms, and deep learning
📊 Mud loss zone prediction, formation permeability modeling, CO₂ storage feasibility using ML
🧪 Experimental rock mechanics: nanoindentation, geomechanical upscaling, SCAL/RCAL testing
📈 Petrophysical property estimation in carbonate and unconventional reservoirs
🌍 Reservoir simulation, LWD analysis, and smart data integration using Python, Petrel, COMSOL
📖 Notable studies include: elastic modulus upscaling, kerogen behavior under pyrolysis, RQI/FZI modeling
🔬 Interdisciplinary projects bridging data science with geoscience and reservoir engineering
🤝 Collaboration with academic and industry leaders to develop practical, AI-driven solutions for energy challenges.

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

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

    Energy
    2025-04 | Journal article
    CONTRIBUTORS: 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 | Journal article
    CONTRIBUTORS: Farshad Sadeghpour; Kamran Jahangiri; 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 | Journal article
    CONTRIBUTORS: Ghasem Aghli; Babak Aminshahidy; Hem Bahadur Motra; Ardavan Darkhal; Farshad Sadeghpour; Mehdi Ostadhassan
  • Comparison of geomechanical upscaling methods for prediction of elastic modulus of heterogeneous media

    Geoenergy Science and Engineering
    2024-08 | Journal article
    CONTRIBUTORS: Farshad Sadeghpour; Ardavan Darkhal; Yifei Gao; Hem B. Motra; Ghasem Aghli; 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. 🎓 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. 📚 She has held postdoctoral and research positions in prestigious institutions and actively contributes to policy research. 🌍

Profile

Education 🎓

  • 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 👨‍🏫

  • 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 🏅

  • 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 🔬

Dr. Lu specializes in energy economics, environmental policy analysis, economic forecasting, and systems engineering. 📊 Her research addresses crude oil price dynamics, carbon reduction strategies, and financial market interactions. 💡 She integrates machine learning with forecasting models, contributing to sustainable energy and environmental policies. 🌍

Publications 

[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 “Black Swan” and
“Gray Rhino” 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 “new triangle”. 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.