A new method for dynamic predicting porosity and permeability of low permeability and tight reservoir under effective overburden pressure based on BP neural network

Abstract

Under the influence of effective overburden pressure, dynamic prediction of porosity and permeability is very important for the formulation and dynamic adjustment of low permeability and tight reservoir development plan. However, the high heterogeneity and complex influencing factors of reservoirs bring great difficulties to the prediction. Firstly, the variation law of porosity and permeability with the increase of effective overburden pressure is determined by testing. On this foundation, data preprocessing is carried out, a sample database is established, and the main controlling factors of geology, fluid and lithology are identified by grey correlation. Finally, based on BP neural network, porosity and permeability prediction training is carried out, and prediction model is established. Considering the prediction error and whether the model is over or under fitting, the results show that the prediction error of the model established by 10 hidden layer neurons and trainbr training function is the best, and the prediction error of porosity and permeability are 3.22% and 8.67% respectively. The model is applied to the field data, and the prediction error of porosity and permeability are 4.18% and 15.85% respectively, which are in line with the oil production law.