張華清

1. 特徵選擇

2. 神經網絡

3. 機器學習

4. 油田大數據 ^[1] 

1996.9-2000.7，曲阜師範大學數學系，學士

2000.9-2003.7，中國石油大學（華東）計算機學院，碩士

2018.9-2022.12，中國石油大學（華東）控制理論與控制工程，博士研究生

2003.7-2011.5，中國石油大學（華東），數學與計算科學學院，講師

2011.5-至今，中國石油大學（華東）理學院，講師 ^[1] 

主講本科生必修課《程序設計語言(C++)》《數據結構》《數學實驗》《數學基礎實踐》《大數據基礎實訓》《Hadoop大數據處理》《神經網絡與深度學習》等，研究生必修課《Python語言與數據分析》等。 ^[1] 

1. 2023-01-01~2026-12-31，基於強化學習的離線-在線交互式油藏開發生產實時優化方法，國家自然科學基金_面上項目，排名2；

2. 2022-01-01~2025-12-01，面向井間連通性的可演化物理導向網絡模型研究，國家自然科學基金_面上項目，排名3；

3. 2021-12-01~2022-12-01，基於模型驅動的油藏擬合與優化軟件開發服務，橫向項目，排名4；

4. 2021-09-08~2021-12-31，基於代理模型的老油田開發指標預測方法技術服務合同，橫向項目，排名3；

5. 2021-11-30~2022-01-15，套損井數據庫管理系統，橫向項目，排名4；

6. 2021-12-06~2021-12-31，非常規地質工程一體化大數據智能建模與優化算法 設計、模塊代碼加工及測試，橫向項目，排名3；

7. 2021-09-08~2021-12-31，基於代理模型的老油田開發指標預測方法，橫向項目，排名3；

8. 2021-07-01~2023-06-30，融合滲流機理構建在線機器學習模型的井間連通性研究，省部級其他項目（理工科），排名4；

9. 2020-07-10~2020-11-30，基於油藏數值模擬器代理模型的注採優化方法，橫向項目，排名3；

10. 2020-05-01~2022-12-31，融合物理模型及神經網絡的可解釋油藏連通性研究，自主創新科研計劃項目（理工科）_科技專項，排名3；

11. 2020-05-01~2022-12-31，融合物理模型及神經網絡的可解釋油藏連通性研究，中石油重大科技合作項目，排名4；

12. 2019-12-06~2023-12-31，深層碳酸鹽巖油氣藏提高儲量控制動用方法與技術研究，中石油重大科技合作項目，排名6；

13. 2019-01-01~2022-12-01，基於金銀納米探針的毒害氣體富集與比色傳感一體化研究與應用，國家自然科學基金_面上項目，排名3；

14. 2015-09-25~2017-06-01，雲遊網絡科技，校級自主創新科研計劃項目（理工科），排名1。 ^[1] 

指導學生榮獲全國大學生數學建模競賽山東賽區二等獎，省部級，2015-10-01。 ^[1] 

[1] Huaqing Zhang, Yunqi Jiang, Jian Wang, Kai Zhang,Nikhil R. Pal. Bilateral Sensitivity Analysis: A Better Understanding of a Neural Network and Its Application to Reservoir Engineering. International Journal of Machine Learning and Cybernetics, 13, 2135-2152, 2022. (SCI 三區)

[2] Huaqing Zhang, Yunqi Jiang, Jian Wang, Kai Zhang, Nikhil R. Pal. Interpretable Neural Networks and Their Application to Inferring Inter-well Connectivity. 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), pp. 487-491, Hangzhou, China, March 25-27, 2022. (EI)

[3] Yunqi Jiang, Huaqing Zhang, Kai Zhang, Jian Wang, Jianfa Han, Shiti Cui, Liming Zhang, Hanjun Zhao, Piyang Liu, Honglin Song. Waterflooding Interwell Connectivity Characterization and Productivity Forecast with Physical Knowledge Fusionn and Model Structure Transfer. Water, 15(2), 218, 2023. (SCI 三區)

[4] Yunqi Jiang, Huaqing Zhang, Kai Zhang, Jian Wang, Shiti Cui, Jianfa Han, Liming Zhang, Jun Yao. Reservoir Characterization and Productivity Forecast Based on Knowledge Interaction Neural Network. Mathematics, 10(9), 1614, 2022. (SCI 二區)

[5] Xiaopeng Ma, Kai Zhang, Hanjun Zhao, Liming Zhang, Jian Wang, Huaqing Zhang, Piyang Liu, Xia Yan, Yongfei Yang. A vector-to-sequence based multilayer recurrent network surrogate model for history matching of large-scale reservoir. Journal of Petroleum Science and Engineering, 214: 110548, 2022. (SCI 二區 TOP 期刊)

[6] Jiamin Li, Xiangyu Wang, Guangdong Xue, Huaqing Zhang, Jian Wang. Sparse Broad Learning System via a Novel Competitive Swarm Optimizer. IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1697-1701, Beijing, China, October 3-5, 2022.

[7] Haochen Wang, Kai Zhang, Xingliang Deng, Shiti Cui, Xiaopeng Ma, Zhongzheng Wang, Ji Qi, Jian Wang, Chuanjin Yao, Liming Zhang, Yongfei Yang, Huaqing Zhang. Highly Accurate Oil Production Forecasting under Adjustable Policy by a Physical Approximation Network. Energy Reports, 8:14396-14415, 2022.(SCI 二區)

[8] Chao Zhong, Kai Zhang, Xiaoming Xue, Ji Qi, Liming Zhang, Xia Yan, Huaqing Zhang, Yongfei Yang, Historical Window-Enhanced Transfer Gaussian Process for Production Optimization, SPE Journal, 27 (05): 2895–2912, 12 October 2022. (SCI 三區 TOP 期刊)

[9] Huaqing Zhang, Yi-Fei Pu, Xuetao Xie*, Bingran Zhang, Jian Wang*, Tingwen Huang. A global neural network learning machine: Coupled integer and fractional calculus operator with an adaptive learning scheme. Neural Networks, 143: 386-399, 2021. (SCI 二區)

[10]Haochen Wang, Jianfa Han, Kai Zhang, Chuanjin Yao, Xiaopeng Ma, Liming Zhang, Yongfei Yang,Huaqing Zhang, Jun Yao, An Interpretable Interflow Simulated Graph Neural Network for Reservoir Connectivity Analysis, SPE J. 26 (04): 1636–1651,DOI: https://doi.org/10.2118/205024-PA, August 2021. (SCI二區)

[11]Jian Wang#; Huaqing Zhang#; Junze Wang; Yi-Fei PU*; Nikhil R. Pal; Feature Selection using a Neural Network With Group Lasso Regularization and Controlled Redundancy, IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(3): 1110-1123. (SCI一區，共同一作)

[12]Xuetao Xie#, Huaqing Zhang#, Junze Wang, Qin Chang, Jian Wang*, Nikhil R. Pal, Learning optimized structure of neural networks by hidden node pruning with L1 regularization, IEEE Transactions on Cybernetics, 2020, 50(3): 1333-1346. (SCI一區，共同一作)

[13]Huaqing Zhang, Jian Wang*, Zhanquan Sun, Jacek M. Zurada, and Nikhil R. Pal; Feature Selection for Neural Networks Using Group Lasso Regularization, IEEE Transactions on Knowledge and Data Engineering, 2020, 32(4): 659-673. (SCI二區)

[14]張凱, 趙興剛, 張黎明, 張華清, 王浩臣, 陳國棟, 趙孟傑, 姜雲啓, 姚軍, 智能油田開發中的大數據及智能優化理論和方法研究現狀及展望, 中國石油大學學報(自然科學版), 2020, 44(04): 28-38. (EI)

[15]Tao Gao, Jian Wang*, Bingjie Zhang, Huaqing Zhang, Peng Ren, Nikhil R. Pal.A Polak-Ribiere-Polyak Conjugate Gradient-Based Neuro-Fuzzy Network and Its Convergence. IEEE Access, 6: 41551-41565, 2018. (SCI二區)

[16]Bingjie Zhang, Junze Wang, Shujun Wu, Jian Wang, Huaqing Zhang*, Fully Complex-Valued Wirtinger Conjugate Neural Networks with Generalized Armijo Search, International Conference on Intelligent Computing (ICIC 2018), 10956: 123-133, Bengaluru, India, 2018-10-25至2018-10-27. (EI)

[17]Qin Chang, Junze Wang, Huaqing Zhang, Lina Shi, Jian Wang, Nikhil R. Pal. Structure Optimization of Neural Networks with L1 Regularization on Gates. IEEE Symposium Series on Computational Intelligence, Bangalore, India, pp. 196-203, 2018.(EI)

[18]Qin Liu, Zhaoyang Sang, Hua Chen, Jian Wang, Huaqing Zhang*, An Efficient Algorithm for Complex-Valued Neural Networks Through Training Input Weights, International Conference on Neural Information Processing (ICONIP), 10637: 150-159, Guangzhou, China, 2017-11-14至2017-11-18.(EI)

[19]Hongmin Gao, Yichen Yang, Bingyin Zhang, Long Li, Huaqing Zhang, Shujun Wu. Feature Selection Using Smooth Gradient L1/2 Regularization. International Conference on Neural Information Processing, 10637: 160-170, 2017. (EI)

[20]Huaqing Zhang, Zongmin Li, Yujie Liu, Fractional Orthogonal Fourier-Mellin Moments for Pattern Recognition. Pattern Recognition. CCPR 2016. Communications in Computer and Information Science (2016), vol. 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_62. (EI) ^[1] 

參與出版《數據結構與算法》、《數學基礎實踐》。 ^[1] 

1.張華清，王健，張凱，姜雲啓，龔曉玲，薛廣東，基於神經網絡敏感性分析的井間連通性判斷方法及系統，CN202110486296.2，2021年。

2.張凱，姜雲啓，姚軍，劉均榮，張黎明，王健，張華清，姚傳進，基於雙並聯神經網絡的機器學習的注採連通性確定方法，CN202011339272.6，2021年。

3.桑兆陽，劉芹，龔曉玲，張華清，陳華，王健，基於梯度下降法與廣義逆的復值神經網絡訓練方法，CN201710091587.5，2017年。 ^[1]