论文
共发表学术论文30余篇,其中以第一作者或通讯作者发表SCI/EI论文20余篇,代表作包括:
1、非常规油气开发方向
[1]J. Zhang, Q. Feng*, et al., 2020.A two-stage step-wise framework for fast optimization of well placement in coalbed methane reservoirs. International Journal of Coal Geology, 225, 103479. SCI一区TOP
[2]Q. Feng, J. Zhang*, et al., 2014. The use of alternating conditional expectation to predict methane sorption capacity on coal. International Journal of Coal Geology, 121, 137–147. SCI一区TOP
[3]Q. Feng, J. Zhang*, X. Zhang, et al., 2012. Optimizing well placement in a coalbed methane reservoir using the particle swarm optimization. International Journal of Coal Geology, 104, 34-45.SCI一区TOP
[4]J. Zhang, Q. Feng*, X. Zhang, et al., 2020. Multi-fractured horizontal well for improved coalbed methane production in eastern Ordos basin, China: Field observations and numerical simulations. Journal of Petroleum Science and Engineering, 194, 107488. SCI二区TOP
[5]J. Zhang, Q. Feng*, et al., X. Zhang, 2015. Relative permeability of coal: A review. Transport in Porous Media, 106(3),563-594. SCI三区
[6]J. Zhang, B. Zhang, S. Xu, et al., 2021. Interpretation of gas/water relative permeability of coal using the hybrid Bayesian-assisted history matching: New insights. Energies, 14(3), 626. SCI三区
[7]Q. Feng, Jiaming Wang, J. Zhang*. Data-driven modeling of the methane adsorption isotherm on coal using supervised learning methods: a comparative study. 2021 International Conference on Modeling, Big Data Analytics and Simulation. EI
2、油气开发人工智能方向
[1]J. Zhang, Y. Sun, L. Shang*, 2020. A unified intelligent model for estimating the (gas+n-alkane) interfacial tension based on eXtreme gradient boosting (XGBoost) trees. Fuel,282, 118783. SCI一区TOP
[2]J. Zhang, Q. Feng*, et al., 2015. The use of an artificial neural network to estimate natural gas/water interfacial tension. Fuel, 157, 28-36.SCI一区TOP
[3]Q. Feng, J. Zhang*, et al., 2015. Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network. Fuel Processing Technology, 129, 120-129.SCI二区TOP
[4]J. Zhang, Q. Feng*, X. Zhang, et al., 2016. Estimation of CO2-brine interfacial tension using an artificial neural network. Journal of Supercritical Fluids, 107, 31-37. SCI二区
[5]J. Zhang, Q. Feng*, X. Zhang, et al., 2020. A supervised learning approach for accurate modeling of CO2–brine interfacial tension with application in identifying the optimum sequestration depth in saline aquifers. Energy Fuels, 34(6), 7353–7362. SCI三区
[6]J. Zhang, Q. Feng*, X. Zhang, et al., 2020. A novel data-driven method to estimate methane adsorption isotherm on coals using the gradient boosting decision tree: A case study in the Qinshui Basin, China. Energies, 13, 5369. SCI三区
[7]J. Zhang, Q. Feng*,2020. The use of machine learning methods for estimation of CO2-brine interfacial tension: a comparative study. 2020 International Conference on Machine Learning Technology. EI