Identification and logging evaluation of poor reservoirs in X Oilfield

The reservoirs of X Oilfield have the characteristics of fine lithology particles, strong pore structure heterogeneity, and high argillaceous reservoirs and thin layers are generally developed. Conventional logging interpretation cannot make a fine evaluation, which results in serious discrepancies...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lu Shengyan, Deng Rui, Linghu Song, Wu Shengli
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	reservoir classification fzi method reservoir type identification chart resolution matching method deconvolution method Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/b3cf15e3e3034699a3278b27fad1f3da
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b3cf15e3e3034699a3278b27fad1f3da
record_format	dspace
spelling	oai:doaj.org-article:b3cf15e3e3034699a3278b27fad1f3da2021-12-05T14:10:49ZIdentification and logging evaluation of poor reservoirs in X Oilfield2391-544710.1515/geo-2020-0280https://doaj.org/article/b3cf15e3e3034699a3278b27fad1f3da2021-09-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0280https://doaj.org/toc/2391-5447The reservoirs of X Oilfield have the characteristics of fine lithology particles, strong pore structure heterogeneity, and high argillaceous reservoirs and thin layers are generally developed. Conventional logging interpretation cannot make a fine evaluation, which results in serious discrepancies between the interpretation results of some reservoirs and actual production performance, and reserves are underestimated. Improving poor reservoir identification and logging evaluation accuracy is of great significance to oilfield development. The flow zone indicator (FZI) is used to classify the reservoirs into three types, I, II, and III, and the classification results are combined to establish a reservoir type identification chart based on logging curves; the resolution matching method and the deconvolution method are used to improve the accuracy of thin-layer recognition. Finally, the logging interpretation model is reestablished. Logging evaluations were conducted on 20 wells in X oilfield, and Y core wells were used for verification. The application results show that this method can effectively improve the identification accuracy of thin oilfields and high argillaceous reservoirs; the results of fine logging interpretation of poor reservoirs are consistent with core analysis conclusions and actual production conditions, which are typical of the successful application of poor reservoir technology.Lu ShengyanDeng RuiLinghu SongWu ShengliDe Gruyterarticlereservoir classificationfzi methodreservoir type identification chartresolution matching methoddeconvolution methodGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 1013-1027 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	reservoir classification fzi method reservoir type identification chart resolution matching method deconvolution method Geology QE1-996.5
spellingShingle	reservoir classification fzi method reservoir type identification chart resolution matching method deconvolution method Geology QE1-996.5 Lu Shengyan Deng Rui Linghu Song Wu Shengli Identification and logging evaluation of poor reservoirs in X Oilfield
description	The reservoirs of X Oilfield have the characteristics of fine lithology particles, strong pore structure heterogeneity, and high argillaceous reservoirs and thin layers are generally developed. Conventional logging interpretation cannot make a fine evaluation, which results in serious discrepancies between the interpretation results of some reservoirs and actual production performance, and reserves are underestimated. Improving poor reservoir identification and logging evaluation accuracy is of great significance to oilfield development. The flow zone indicator (FZI) is used to classify the reservoirs into three types, I, II, and III, and the classification results are combined to establish a reservoir type identification chart based on logging curves; the resolution matching method and the deconvolution method are used to improve the accuracy of thin-layer recognition. Finally, the logging interpretation model is reestablished. Logging evaluations were conducted on 20 wells in X oilfield, and Y core wells were used for verification. The application results show that this method can effectively improve the identification accuracy of thin oilfields and high argillaceous reservoirs; the results of fine logging interpretation of poor reservoirs are consistent with core analysis conclusions and actual production conditions, which are typical of the successful application of poor reservoir technology.
format	article
author	Lu Shengyan Deng Rui Linghu Song Wu Shengli
author_facet	Lu Shengyan Deng Rui Linghu Song Wu Shengli
author_sort	Lu Shengyan
title	Identification and logging evaluation of poor reservoirs in X Oilfield
title_short	Identification and logging evaluation of poor reservoirs in X Oilfield
title_full	Identification and logging evaluation of poor reservoirs in X Oilfield
title_fullStr	Identification and logging evaluation of poor reservoirs in X Oilfield
title_full_unstemmed	Identification and logging evaluation of poor reservoirs in X Oilfield
title_sort	identification and logging evaluation of poor reservoirs in x oilfield
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/b3cf15e3e3034699a3278b27fad1f3da
work_keys_str_mv	AT lushengyan identificationandloggingevaluationofpoorreservoirsinxoilfield AT dengrui identificationandloggingevaluationofpoorreservoirsinxoilfield AT linghusong identificationandloggingevaluationofpoorreservoirsinxoilfield AT wushengli identificationandloggingevaluationofpoorreservoirsinxoilfield
_version_	1718371684734468096

Identification and logging evaluation of poor reservoirs in X Oilfield

Ejemplares similares