Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field

The reservoirs in the eastern depression of Liaohe basin are formed by multistage igneous eruption. The lithofacies and lithology are complex, and the lithology is mainly intermediate and basic igneous rocks. Based on the integration of debris data of igneous rocks and logging data, this article sel...

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Autores principales:	Wenhua Wang, Zhuwen Wang, Ruiyi Han, Fanghui Xu, Xinghua Qi, Yitong Cui
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	igneous rock machine learning lithology classification Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/29d8706fd58944db860009594c255717
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spelling	oai:doaj.org-article:29d8706fd58944db860009594c2557172021-12-05T14:10:49ZLithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field2391-544710.1515/geo-2020-0300https://doaj.org/article/29d8706fd58944db860009594c2557172021-10-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0300https://doaj.org/toc/2391-5447The reservoirs in the eastern depression of Liaohe basin are formed by multistage igneous eruption. The lithofacies and lithology are complex, and the lithology is mainly intermediate and basic igneous rocks. Based on the integration of debris data of igneous rocks and logging data, this article selected 6,462 continuous logging data with complete cuttings data and five conventional logging curves (RLLD, AC, DEN, GR, and CNL) from four wells in the eastern depression of Liaohe basin as the training set. A variety of lithologic identification schemes based on support vector machine and random forest are established to classify the pure igneous strata and actual strata. By comparing the classification results with the identification data of core slice and debris slice, a practical lithologic classification scheme for igneous rocks in the eastern depression of Liaohe basin is obtained, and the classification accuracy reaches 97.46%.Wenhua WangZhuwen WangRuiyi HanFanghui XuXinghua QiYitong CuiDe Gruyterarticleigneous rockmachine learninglithology classificationGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 1245-1258 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	igneous rock machine learning lithology classification Geology QE1-996.5
spellingShingle	igneous rock machine learning lithology classification Geology QE1-996.5 Wenhua Wang Zhuwen Wang Ruiyi Han Fanghui Xu Xinghua Qi Yitong Cui Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
description	The reservoirs in the eastern depression of Liaohe basin are formed by multistage igneous eruption. The lithofacies and lithology are complex, and the lithology is mainly intermediate and basic igneous rocks. Based on the integration of debris data of igneous rocks and logging data, this article selected 6,462 continuous logging data with complete cuttings data and five conventional logging curves (RLLD, AC, DEN, GR, and CNL) from four wells in the eastern depression of Liaohe basin as the training set. A variety of lithologic identification schemes based on support vector machine and random forest are established to classify the pure igneous strata and actual strata. By comparing the classification results with the identification data of core slice and debris slice, a practical lithologic classification scheme for igneous rocks in the eastern depression of Liaohe basin is obtained, and the classification accuracy reaches 97.46%.
format	article
author	Wenhua Wang Zhuwen Wang Ruiyi Han Fanghui Xu Xinghua Qi Yitong Cui
author_facet	Wenhua Wang Zhuwen Wang Ruiyi Han Fanghui Xu Xinghua Qi Yitong Cui
author_sort	Wenhua Wang
title	Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
title_short	Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
title_full	Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
title_fullStr	Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
title_full_unstemmed	Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
title_sort	lithology classification of volcanic rocks based on conventional logging data of machine learning: a case study of the eastern depression of liaohe oil field
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/29d8706fd58944db860009594c255717
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_version_	1718371710301896704

Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field

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