关键词: 测井, 渗透率, 统计模型, BP神经网络, 模型预测, 模型优化

Abstract: Based on a case study of the Paleogene Shahejie Formation of the KL oilfield in the Laizhou Bay Sag, this study compares the traditional statistical model of regression with the prediction model of artificial intelligence based on BP neural network to evaluate the reservoir permeability. The target reservoir is mainly of medium to high porosity and permeability. Lithology and porosity are the main influencing factors of reservoir permeability. Based on core data analysis, the porosityparticle size binary regression model and BP neural network were established. By comparing the accuracy of the test sample set, the influence of the network structure parameters such as the number of hidden layers and the number of hidden layer nodes on the prediction results of the BP neural network model was analyzed. The effects of different logging parameters on the prediction results of BP neural network model were analyzed with the focus. The optimized BP neural network model has the highest permeability prediction accuracy for the test sample set, with an average relative error of 37%, which is 26% higher than the traditional statistical model of binary regression. For the continuous treatment of three wells in the target field, the permeability prediction results of the BP neural network model are more reasonable, which can meet the production requirements such as the production capacity analysis of the development zone.

Key words: well log, permeability, statistic model, BP neural network, prediction model, model optimization