T. M. Ponjiger, S. Šešum, M. V. Naugolnov, O. Pilipenko
{"title":"基于沉积环境和测井资料的XGBoost算法岩性分类","authors":"T. M. Ponjiger, S. Šešum, M. V. Naugolnov, O. Pilipenko","doi":"10.3997/2214-4609.202156006","DOIUrl":null,"url":null,"abstract":"Summary The aim of this paper is to obtain an automatic lithology prediction model by using machine learning (ML) algorithms, with selected well log curves, core description data and sedimentary environment information. This model is applicable for several depositional systems for fields in Pannonian Basin and it’s locally integrated in standard software platform for petrophysicist in Company „Naftna Industrija Srbije“.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Lithology Classification by Depositional Environment and Well Log Data Using XGBoost Algorithm\",\"authors\":\"T. M. Ponjiger, S. Šešum, M. V. Naugolnov, O. Pilipenko\",\"doi\":\"10.3997/2214-4609.202156006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary The aim of this paper is to obtain an automatic lithology prediction model by using machine learning (ML) algorithms, with selected well log curves, core description data and sedimentary environment information. This model is applicable for several depositional systems for fields in Pannonian Basin and it’s locally integrated in standard software platform for petrophysicist in Company „Naftna Industrija Srbije“.\",\"PeriodicalId\":266953,\"journal\":{\"name\":\"Data Science in Oil and Gas 2021\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Science in Oil and Gas 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.202156006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science in Oil and Gas 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.202156006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lithology Classification by Depositional Environment and Well Log Data Using XGBoost Algorithm
Summary The aim of this paper is to obtain an automatic lithology prediction model by using machine learning (ML) algorithms, with selected well log curves, core description data and sedimentary environment information. This model is applicable for several depositional systems for fields in Pannonian Basin and it’s locally integrated in standard software platform for petrophysicist in Company „Naftna Industrija Srbije“.