{"title":"利用机器学习分解和解释石油中的非线性分子和同位素变化","authors":"Keyu Tao , Jian Cao , Yuce Wang , Wanyun Ma","doi":"10.1016/j.marpetgeo.2024.107175","DOIUrl":null,"url":null,"abstract":"<div><div>Nonlinear variations in the molecular and isotopic compositions of phases in complex geosystems greatly hinder the application of geochemical proxies. This study aims to disentangle the implicit nonlinear mathematical structures embedded in geochemical datasets, effectively disaggregating overlapping geological influences that drive the intricate variations in the geochemical signatures of phases. Employing a typical hybrid petroleum system as a case study, we utilize an unsupervised machine learning algorithm to visualize the effects of source disparities and distinct evolutionary processes, such as mixing, thermal maturation, biodegradation, and evaporative fractionation, on the molecular compositions among crude oils. We further investigate the regression relationship between molecular composition and bulk <em>δ</em><sup>13</sup>C signal in petroleum. Our findings reveal that by decomposing the regression model to solely reflect a specific dominant influence, the model could provide a precise geological interpretation. Accordingly, we unravel the subtle variations and underlying mechanisms of carbon isotopic fractionation in petroleum substances from different origins under the impact of maturation. Our results underscore the substantial potential of strategically applied machine learning techniques in reconstructing the geochemical evolution of complex geosystems, advocating for their broader application.</div></div>","PeriodicalId":18189,"journal":{"name":"Marine and Petroleum Geology","volume":"171 ","pages":"Article 107175"},"PeriodicalIF":3.7000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Disentangling and interpreting nonlinear molecular and isotopic variations in petroleum using machine learning\",\"authors\":\"Keyu Tao , Jian Cao , Yuce Wang , Wanyun Ma\",\"doi\":\"10.1016/j.marpetgeo.2024.107175\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Nonlinear variations in the molecular and isotopic compositions of phases in complex geosystems greatly hinder the application of geochemical proxies. This study aims to disentangle the implicit nonlinear mathematical structures embedded in geochemical datasets, effectively disaggregating overlapping geological influences that drive the intricate variations in the geochemical signatures of phases. Employing a typical hybrid petroleum system as a case study, we utilize an unsupervised machine learning algorithm to visualize the effects of source disparities and distinct evolutionary processes, such as mixing, thermal maturation, biodegradation, and evaporative fractionation, on the molecular compositions among crude oils. We further investigate the regression relationship between molecular composition and bulk <em>δ</em><sup>13</sup>C signal in petroleum. Our findings reveal that by decomposing the regression model to solely reflect a specific dominant influence, the model could provide a precise geological interpretation. Accordingly, we unravel the subtle variations and underlying mechanisms of carbon isotopic fractionation in petroleum substances from different origins under the impact of maturation. Our results underscore the substantial potential of strategically applied machine learning techniques in reconstructing the geochemical evolution of complex geosystems, advocating for their broader application.</div></div>\",\"PeriodicalId\":18189,\"journal\":{\"name\":\"Marine and Petroleum Geology\",\"volume\":\"171 \",\"pages\":\"Article 107175\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Marine and Petroleum Geology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0264817224004872\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Marine and Petroleum Geology","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0264817224004872","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Disentangling and interpreting nonlinear molecular and isotopic variations in petroleum using machine learning
Nonlinear variations in the molecular and isotopic compositions of phases in complex geosystems greatly hinder the application of geochemical proxies. This study aims to disentangle the implicit nonlinear mathematical structures embedded in geochemical datasets, effectively disaggregating overlapping geological influences that drive the intricate variations in the geochemical signatures of phases. Employing a typical hybrid petroleum system as a case study, we utilize an unsupervised machine learning algorithm to visualize the effects of source disparities and distinct evolutionary processes, such as mixing, thermal maturation, biodegradation, and evaporative fractionation, on the molecular compositions among crude oils. We further investigate the regression relationship between molecular composition and bulk δ13C signal in petroleum. Our findings reveal that by decomposing the regression model to solely reflect a specific dominant influence, the model could provide a precise geological interpretation. Accordingly, we unravel the subtle variations and underlying mechanisms of carbon isotopic fractionation in petroleum substances from different origins under the impact of maturation. Our results underscore the substantial potential of strategically applied machine learning techniques in reconstructing the geochemical evolution of complex geosystems, advocating for their broader application.
期刊介绍:
Marine and Petroleum Geology is the pre-eminent international forum for the exchange of multidisciplinary concepts, interpretations and techniques for all concerned with marine and petroleum geology in industry, government and academia. Rapid bimonthly publication allows early communications of papers or short communications to the geoscience community.
Marine and Petroleum Geology is essential reading for geologists, geophysicists and explorationists in industry, government and academia working in the following areas: marine geology; basin analysis and evaluation; organic geochemistry; reserve/resource estimation; seismic stratigraphy; thermal models of basic evolution; sedimentary geology; continental margins; geophysical interpretation; structural geology/tectonics; formation evaluation techniques; well logging.