油水两相相对渗透率曲线对油藏含水上升规律和产量变化规律有重要影响,是油藏开发的基础数据。为更准确预测相对渗透率曲线端点值、建立储层物性和相对渗透率曲线端点值之间的关系、提高油藏数值模拟精度,以149条相对渗透率曲线的渗透率和孔隙度为输入变量,以相对渗透率曲线端点值(束缚水饱和度、残余油饱和度、残余油饱和度下的水相渗透率)为输出变量,建立了一种基于储层物性预测相对渗透率曲线端点值的BP神经网络预测模型。经未参与建模的14条相对渗透率曲线数据检验,新模型预测绝对误差在0.03以内,相对误差小于9%,满足相对渗透率曲线计算要求;利用该模型预测的BZ油藏不同物性级别下的相对渗透率曲线能够反映BZ油藏的渗流特征。该研究成果对相对渗透率预测及提高数值模拟精度具有一定借鉴意义。

The oil-water two-phase relative permeability curve has important influence on water cut and the oil production decline, which is the basic data of reservoir development.Correcting and predicting the endpoint value of relative permeability curve based on reservoir physical property is one of the keys to improve the accuracy of reservoir numerical simulation.In order to accurately predict the end value of the relative permeability curve,the relationship between the reservoir physical property and the end value of the relative permeability curve was established which is important to improve the reservoir numerical simulation accuracy.Porosity and permeability of the 149 relative permeability curves are taken as the input variables,while the endpoint values of relative permeability curves (irreducible water saturation, residual oil saturation and residual oil saturation of water phase permeability) as the output variables.On the above basis,a BP neural network prediction model is established to predict the end point of relative permeability curve upon reservoir physical property.The accuracy of the model is tested with 14 other relative permeability curve data.The results show that the absolute error of the model is within 0.03 and the relative error is within 9%,which meets the calculation requirements of the relative permeability curve.This method is used to predict the relative permeability curves of BZ reservoir under different physical properties,and the results can reflect the seepage characteristics of actual oil fieldand provide reference for the prediction of relative permeability and improvement of numerical simulation accuracy.