副教授

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查文舒

  • 职称 ：副教授
  • 邮箱 ：wszha@hfut.edu.cn
  • 所属系 ：统计系
  • 主讲课程 ：数据挖掘、深度学习、概率论与数理统计
  • 研究领域 ：深度学习方法、油气大数据技术、油气流动模型与数值模拟方法等
教育经历
  • 1999年9月–2004年7月，中国科学技术大学，力学，学士
    2004年9月–2009年5月，中国科学技术大学，计算机应用，博士
工作经历
  • 2014-09至今，合肥工业大学，数学学院，副教授
    2012-06至2014-08，中国电子科技集团公司第三十八研究所
    2009-06至2012-05，中国科学技术大学，博士后
科研项目
  • [1] 国家自然科学基金面上项目，基于流动方程的致密油气藏深度学习数值模拟方法，2024-01-01至2027-12-31，主持
    [2] 国家自然科学基金面上项目，基于深度学习的渗流方程求解方法研究，2022-01-01 至 2025-12-31，主要成员
    [3] 中国科学院战略性先导科技专项，页岩气产能预测模拟方法与分析软件，2015/01-2018/12，主要成员
    [4] 科技部重家科技重大专项，复杂边界自动网格划分与非线性方程求解方法研究，2011/01-2015/12，主要成员
    [5] 国家自然科学基金重点项目，多孔介质复杂流动细观机理与方法研究，2010/01-2013/12，主要成员
研究成果
  • 专著 [1] 数值试井理论与方法，李道伦，查文舒，石油工业出版社，2013 [2] 非常规油气流动机理研究与应用，李道伦，查文舒,王磊，石油工业出版社，2018 期刊论文： [1] Wang Q, Li X, Li D, et al. Asymptotic solution neural network for solving multi-well seepage equation with deep integration of superposition principle and Laplacian distance decay weighting in reservoir simulation[J]. Journal of Computational Physics, 2026, 558: 114845.
    [2] Wang Q, Zha W, Li D, et al. Parameterized analytical solution of seepage equation for reservoir simulation using physics-informed kolmogorov-arnold network without labels[J]. Journal of Computational Physics, 2026, 548: 114599.
    [3] Zha W, Chen D, Li D, et al. Causally enhanced initial conditions: A novel soft constraints strategy for physics informed neural networks[J]. Chinese Physics B, 2025, 34(4): 40701.
    [4] Li D, Wang Q, Zha W, et al. Inversion of Multiple Reservoir Parameters Based on Deep Neural Networks Guided by Lagrange Multipliers[J]. SPE JOURNAL, 2025, 30(5): 2632-2652.
    [5] Lv S, Li D, Zha W, et al. Physics-informed radial basis function neural network for efficiently modeling oil-water two-phase Darcy flow[J]. PHYSICS OF FLUIDS, 2025, 37(1).
    [6] Li D, Chen E, Xu Y, et al. Sim-Net: Simulation Net for Solving Seepage Equation Under Unsteady Boundary[J]. International Journal for Numerical Methods in Fluids, 2025, 97(3): 345-358.
    [7] Wang Q, Li D, Zha W, et al. Scaled asymptotic solution nets for unlabeled seepage equation solutions with variable well flow[J]. PHYSICS OF FLUIDS, 2025, 37(1).
    [8] 李道伦, 沈路航, 查文舒, 等. 基于神经算子与类物理信息神经网络智能求解新进展[J]. 力学学报, 2024, 56(4): 875-889.
    [9] Lv S, Li D, Zha W, et al. Solving seepage equation using physics-informed residual network without labeled data[J]. Computer Methods in Applied Mechanics and Engineering, 2024, 418: 116563.
    [10] Li D, Wang H, Zha W, et al. Learning a CNN with the finite volume method for modelling fluid seepage flow under closed boundary[J]. Geoenergy Science and Engineering, 2024, 243: 213388.
    [11] Li D, Lv S, Zha W, et al. A nonlinear solver based on residual network for seepage equation[J]. Engineering Applications of Artificial Intelligence, 2023, 126: 106850.
    [12] Liu Z, Hao Y, Li D, et al. Multiparameter Inversion of Reservoirs Based on Deep Learning[J]. SPE Journal, 2024, 29(01): 126-137.
    [13] Shen L, Li D, Zha W, et al. Physical Asymptotic-Solution nets: Physics-driven neural networks solve seepage equations as traditional numerical solution behaves[J]. Physics of Fluids, 2023, 35(2): 23603.
    [14] Li D, Shen L, Zha W, et al. Deep learning‐based method for solving seepage equation under unsteady boundary[J]. International Journal for Numerical Methods in Fluids, 2023, 96: 87-101.
    [15] Liu X, Zha W, Li D, et al. Automatic well test interpretation method for circular reservoirs with changing wellbore storage by using one-dimensional convolutional neural network[J]. Journal of Energy Resources Technology, 2022, 145: 1-14.
    [16] 查文舒, 李道伦, 沈路航, 等. 基于神经网络的偏微分方程求解方法研究综述[J]. 力学学报, 2022, 54(3): 543-556.
    [17] Zha W, Zhang W, Li D, et al. Convolution-Based Model-Solving Method for Three-Dimensional, Unsteady, Partial Differential Equations[J]. Neural Computation, 2022, 34(2): 518-540.
    [18] Shen L, Li D, Zha W, et al. Surrogate modeling for porous flow using deep neural networks[J]. Journal of Petroleum Science and Engineering, 2022, 213: 110460.
    [19] Liu X, Zha W, Qi Z, et al. Automatic reservoir model identification method based on convolutional neural network[J]. Journal of Energy Resources Technology, 2022, 144(4).
    [20] Daolun L, Luhang S, Wenshu Z, et al. Physics-constrained deep learning for solving seepage equation[J]. Journal of Petroleum Science and Engineering, 2021, 206: 109046.
    [21] 李道伦, 刘旭亮, 查文舒, 等. 基于卷积神经网络的径向复合油藏自动试井解释方法[J]. 2020, 47(3): 583-591.
所获荣誉
  • [1] 化学驱和低渗油气藏诊断理论与软件及其工业化应用，2020年获安徽省科技进步二等奖
    [2] 油气藏智能反演理论与应用，2025年获安徽省工业与应用数学学会二等奖

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