马小鹏，讲师

电子邮箱：xiaopengma23@126.com，电话:15909298801，QQ:1273779481

通讯地址: 西安市雁塔区电子二路东段18号

教育经历：

2018.09至2022.06：中国石油大学（华东），油气田开发工程，博士

2016.09至2018.07：中国石油大学（华东），油气田开发工程，硕士（硕博连读）

2012.09至2016.07：中国石油大学（华东），石油工程，学士

工作经历：

2022.08至今：44118太阳成城集团,44118太阳成城集团,讲师

研究方向：

主要从事油藏数值模拟与油气田开发工程理论与方法；智能油藏（自动历史拟合、注采优化等）；智能优化理论（大规模优化、多目标优化、代理辅助优化算法、多模态优化、约束优化等）；机器学习理论（强化学习、迁移学习、图学习、卷积神经网络等）。

代表性论著：

[1]X Ma, K Zhang, C Yao, et al. Multiscale-Network Structure Inversion of Fractured Media Based on a Hierarchical-Parameterization and Data-Driven Evolutionary-Optimization Method [J]. SPE Journal. 2020, 25(05): 2729-2748.

[2]X Ma, K Zhang, L Zhang, et al. Data-Driven Niching Differential Evolution with Adaptive Parameters Control for History Matching and Uncertainty Quantification [J]. SPE Journal, 2021, 26(02): 993-1010.

[3]X Ma, K Zhang, J Wang, et al. An Efficient Spatial-Temporal Convolution Recurrent Neural Network Surrogate Model for History Matching [J]. SPE Journal, 2022, 27(02): 1160-1175.

[4]X Ma, K Zhang, L Zhang, et al. A distributed surrogate system assisted differential evolutionary algorithm for computationally expensive history matching problems [J]. Journal of Petroleum Science and Engineering, 2022, 210: 110029.

[5]X Ma, K Zhang, J Zhang, et al. A Novel Hybrid Recurrent Convolutional Network for Surrogate Modeling of History Matching and Uncertainty quantification [J]. Journal of Petroleum Science and Engineering. 2022, 210: 110109.

[6]X Ma, K Zhang, H Zhao, et al. A Vector-to-Sequence based Multilayer Recurrent Network Surrogate Model for History Matching of Large-scale Reservoir [J]. Journal of Petroleum Science and Engineering. 2022, 214: 110548.

[7] K Zhang,X Ma, Y Li, et al. Parameter prediction of hydraulic fracture for tight reservoir based on micro-seismic and history matching [J]. Fractals. 2018, Vol.26 (02): 1840009.

[8]马小鹏,张凯,陈昕晟,等.基于集合光滑的深度学习自动历史拟合方法[J].中国石油大学学报(自然科学版), 2020, 44(4): 68-76.

[9]张凯,马小鹏,王增飞,等.一种强非均质性油藏自动历史拟合混合求解方法[J].中国石油大学学报(自然科学版), 2018, 42(5): 89-97.

[10] K Zhang, J Zhang,X Ma, et al. History matching of naturally fractured reservoirs using a deep sparse autoencoder [J]. SPE Journal, 2022, 26 (04): 1700-1721.

[11] K Zhang, X Wang,X Ma, et al. The prediction of reservoir production-based proxy model considering spatial data and vector data [J]. Journal of Petroleum Science and Engineering, 2022, 208, 109694.

[12] K Zhang, H Yu,X Ma, et al. Multi-source information fused generative adversarial network model and data assimilation-based history matching for reservoir with complex geologies [J]. Petroleum Science, 2022, 19(2): 707-719.

[13] L Zhang, C Cui,X Ma, Z Sun, F Liu, K Zhang. A fractal discrete fracture network model for history matching of naturally fractured reservoirs[J]. Fractals, 2018, 27 (01), 1940008.

[14] Y Wang, K Zhang,X Ma, et al. A physics-guided autoregressive model for saturation sequence prediction[J]. Geoenergy Science and Engineering, 2023, 221: 211373.

[15] W Fu, K Zhang,X Ma, et al. Deep Conditional Generative Adversarial Network Combined with Data-Space Inversion for Estimation of High-dimensional Uncertain Geological Parameters[J]. Water Resources Research, 2023, 59(3): e2022WR032553.