天然气与石油2026，Vol.44Issue(4)：70-81,12.DOI:10.3969/j.issn.1006-5539.2026.04.009
基于多输出RBF神经网络的油藏参数反演方法
Reservoir parameter inversion method based on multi-output radial basis function neural network
摘要
Abstract
In order to improve the inversion accuracy and efficiency of well test analyis,this paper proposes a reservoir parameter inversion method based on a multi-output radial basis function(RBF)neural network.First,the uncertain parameters to be inverted and their ranges are selected to generate sampling cases.Then,a surrogate model that approximates the true functional relationship,i.e.,the multi-output RBF neural network model,is trained using gradient descent.An objective function is constructed based on the deviation between surrogate model predictions and actual observations,and particle swarm optimization(PSO)is employed to search for the minimum of the objective function to obtain a set of optimal parameter values.Compared with single-output RBF neural networks,the multi-output RBF neural network achieves parameter sharing and synchronous output of complete pressure response sequences,avoiding repetitive training required for modeling pressure at each time point separately.Comparative experiments demonstrate that the training time of the multi-output RBF neural network is only approximately 2%of that of the single-output RBF model,with higher model accuracy and smoother prediction curves.It is known from the verification with actual measured data from oilfied sites that the reservoir parameter inversion method based on the multi-output RBF neural network can effectively fit both bottomhole pressure variations and pressure derivatives in complex heterogeneous reservoir models.This method provides an efficient and practical new method for intelligent reservoir parameter inversion with significant application value.关键词
RBF神经网络/多输出/油藏参数反演/历史拟合/试井分析Key words
RBF neural network/Multi-output/Reservoir parameter inversion/History matching/Well test analysis引用本文复制引用
沈婷,付宁,王凯,张康,李道伦..基于多输出RBF神经网络的油藏参数反演方法[J].天然气与石油,2026,44(4):70-81,12.基金项目
国家自然科学基金"基于流动方程的致密油气藏深度学习数值模拟方法"(12372244) （12372244）
