{"title":"利用测井曲线和三维地震资料预测岩石物性的新神经网络算法","authors":"S. Egorov, I. Priezzhev","doi":"10.3997/2214-4609.201802335","DOIUrl":null,"url":null,"abstract":"The article is dedicated to a new neural network algorithm which is aimed to spatial well log curves prediction using seismic data. The specificity of proposed method is usage of variety random functions instead of weight coefficients and activation function in neural networks. As an example of effectivity of the new approach the results of density prediction using two neural network algorithms are demonstrated.","PeriodicalId":288128,"journal":{"name":"20th conference on oil and gas geological exploration and development","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"New Neural Network Algorithm Aimed to Physical Properties of Rocks Prediction Using Log Curves and 3D Seismic Data\",\"authors\":\"S. Egorov, I. Priezzhev\",\"doi\":\"10.3997/2214-4609.201802335\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article is dedicated to a new neural network algorithm which is aimed to spatial well log curves prediction using seismic data. The specificity of proposed method is usage of variety random functions instead of weight coefficients and activation function in neural networks. As an example of effectivity of the new approach the results of density prediction using two neural network algorithms are demonstrated.\",\"PeriodicalId\":288128,\"journal\":{\"name\":\"20th conference on oil and gas geological exploration and development\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"20th conference on oil and gas geological exploration and development\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.201802335\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"20th conference on oil and gas geological exploration and development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201802335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
New Neural Network Algorithm Aimed to Physical Properties of Rocks Prediction Using Log Curves and 3D Seismic Data
The article is dedicated to a new neural network algorithm which is aimed to spatial well log curves prediction using seismic data. The specificity of proposed method is usage of variety random functions instead of weight coefficients and activation function in neural networks. As an example of effectivity of the new approach the results of density prediction using two neural network algorithms are demonstrated.