基于TensorFlow的均质数字岩心渗透率预测方法及应用

Method and application of homogeneous digital core permeability prediction based on TensorFlow

目前岩心渗透率的获取为室内试验方法,针对该方法工作效率低、试验操作繁琐、耗时较长的问题,提出一种基于机器学习的均质数字岩心渗透率预测方法。首先随机生成大量均质数字岩心,通过孔隙网络模型的方法对其进行孔隙度和渗透率的计算,将所得结果作为机器学习的样本库,然后基于BP人工神经网络方法,对岩心的孔隙度和渗透率数据进行提取和处理,通过训练得到相应的机器学习模型,最后通过对比机器学习结果和室内试验结果,验证机器学习模型的准确性。结果表明,通过机器学习技术预测渗透率的方法准确高效,与岩心的实测渗透率误差仅为3.1%,可在实际生产中进行应用,避免大量的试验操作,提高了岩心渗透率的计算效率。

The permeability of core samples is usually measured in laboratory using conventional techniques,which is inefficient,tedious and time-consuming.In this study,a permeability prediction method for homogeneous digital cores was proposed based on machine learning.Firstly,a large number of homogeneous digital cores were randomly generated.Their porosity and permeability were calculated by a pore network model,and the results were taken as the sample database for establishing a machine learning model.Then,based on the BP artificial neural network method,the porosity and permeability data of the cores were extracted and analyzed,and used for training the corresponding machine learning model.The accuracy of the machine learning model was verified in comparison with laboratory experiments.The results show that the machine learning model can provide an accurate and efficient method for permeability prediction.The error between the permeability calculated by the model and that measured by experiment is only 3.1%.The machine learning method can be applied in oilfield for core analysis,which can avoid a large number of core testing,and improve the calculation efficiency of core permeability.