西南石油大学学报（自然科学版）2023，Vol.45Issue(6)：69-79,11.DOI:10.11885/j.issn.1674-5086.2021.06.11.03
迁移深度神经网络的页岩总孔隙度预测
Prediction for Total Porosity of Shale Based on Transfer Deep Neural Network
摘要
Abstract
Porosity is one of the key indicators to characterize the por structure of shale reservoirs.Quantitative prediction research on porosity is an important step in reservoir evaluation.The accurate value of shale porosity must be obtained through core analysis.How to obtain an accurate prediction of the porosity of the entire well based on a very small amount of coring well data is a significant problem.This paper proposes a new transfer deep neural network model,based on a small amount of core and logging data,to achieve accurate prediction of porosity.Firstly,according to Pearson correlation coefficient method,the logging parameters suitable for the source well deep neural network are selected as the input for the model.Secondly,a new method is proposed to calculate the similarity of the well logging data distribution between the source well and the target well,quantitatively measure the geological difference between two wells.Thridly,retrain the source well prediction network with a small amount of target well logging data similar to the source well logging data distribution,and build a transfer deep neural network for predicting the porosity migration of the target well.The test results of A2 and B2 show that:1)This method requires only 10％of the data volume,and reaches the performance of an absolute mean error of 0.032 9 and a coefficient of determination of 0.841 6;2)The proposed method for calculating the similarity of two wells can effectively measure the difference between wells.The more similar the distribution of the source well logging data and the target well logging data,the higher the accuracy of porosity prediction of the transfer learning network.The proposed model can effectively reduce the dependence on logging and core data,and greatly reduce shale gas exploration and development costs.关键词
页岩气/测井/页岩孔隙度预测/井间差异/深度迁移学习Key words
shale gas/well logging/shale porosity prediction/difference between wells/deep transfer learning分类
能源科技引用本文复制引用
汪敏,杨桃,唐洪明,闫建平,廖纪佳..迁移深度神经网络的页岩总孔隙度预测[J].西南石油大学学报（自然科学版）,2023,45(6):69-79,11.基金项目
国家自然科学基金(62006200) （62006200）
中国石油-西南石油大学创新联合体科技合作项目(2020CX020000) （2020CX020000）
