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J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

Reservoir inversion method based on RBF neural network

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基于径向基神经网络的油藏反演方法

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), May.2023, Vol.46 No.5: 713-720

DOI: 10.3969/j.issn.1003-5060.2023.05.023

Authors

ZHOU Ziqi, ZHA Wenshu, LI Daolun, LIU Xuliang

(School of Mathematics, Hefei University of Technology, Hefei 230601, China)

Abstract and Keywords

Abstract: This paper proposes a reservoir inversion method based on radial basis function(RBF) neural network. In this method, the RBF neural network model is constructed by using the bottom hole pressure(BHP) data generated by sampling, and the objective function is defined by the deviation between the predicted value of the RBF neural network and the actual observed value, and then the particle swarm optimization(PSO) algorithm is used to optimize the objective function. Finally, the optimal solution of the uncertain parameters and the inversion parameters are obtained. Compared with the polynomial fitting method, the RBF neural network method has better fitting results and higher precision. Even when the polynomial fitting method fails, the RBF neural network method still works well. A practical example in oilfield shows that this method has good fitting effect, can greatly improve the inversion efficiency, and has a good application prospect.

Keywords: reservoir inversion; radial basis function (RBF) neural network; objective function; optimization algorithm; history matching

作者信息

周子琪，查文舒，李道伦，刘旭亮

（合肥工业大学数学学院，安徽合肥230601）

摘要和关键词

摘要: 文章提出一种基于径向基(radial basis function, RBF)神经网络的油藏反演方法。该方法利用抽样生成的井底压力数据构造RBF神经网络模型，由RBF神经网络预测值与实际观测值的偏差定义目标函数，再利用粒子群算法(particle swarm optimization, PSO)对其进行优化，最终得到不确定参数的最优解和反演参数。与多项式拟合方法相比，RBF神经网络方法具有更好的拟合结果和更高的精度，甚至在多项式拟合方法失效时，该方法也能得到很好的模拟结果。油田实际算例表明，该方法具有良好的拟合效果，能大幅提高反演效率，具有很好的应用前景。

关键词: 油藏反演；径向基（RBF）神经网络；目标函数；优化算法；历史拟合

Funding

国家科技重大专项资助项目（2017ZX05009005-002）

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