Makpal Bektybayeva, N. Mendybaev, Asfandiyar Bigeldiyev, S. Basu, A. Abetov, Aidos Temirkhassov, Ranida Tyulebayeva, Aiyazhan Yermukhanbet
{"title":"卡拉干达煤田矿区岩石物理分析工作流程","authors":"Makpal Bektybayeva, N. Mendybaev, Asfandiyar Bigeldiyev, S. Basu, A. Abetov, Aidos Temirkhassov, Ranida Tyulebayeva, Aiyazhan Yermukhanbet","doi":"10.2118/206627-ms","DOIUrl":null,"url":null,"abstract":"\n For accurate coal bed methane (CBM) reserves estimation, it is necessary to evaluate reservoir characteristics. We present a workflow for formation evaluation of coalbed-methane wells, by interpretation of a limited number of legacy logs, including data preprocessing, lithology identification, proximate analysis and estimation of gas content of coal beds.\n This workflow allowed the estimation of ash content from the available logs, including selective log (analogue of photoelectric absorption), which was recorded only on the territory of the former Soviet Union and never used for such calculations before. Even though the logs were recorded by old tools with low vertical resolution, we were able to identify heterogeneity of coal seams, using the principle of core ash content distribution. Integrated analysis of old core data and recent laboratory measurements of samples from coal pillars allowed to calculate proximate properties of the coal, which showed good match with observed data and could be considered as input parameters for property distribution in the geological model.\n Also, it is worth to mention that an advanced plug-in was deployed to perform calculation of proximate properties and gas content for all available options and to significantly reduce time for screening different algorithms and rapidly analyzing results.","PeriodicalId":10970,"journal":{"name":"Day 1 Tue, October 12, 2021","volume":"57 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Workflow of Petrophysical Analysis Performed at Mine in Karaganda Coal Basin\",\"authors\":\"Makpal Bektybayeva, N. Mendybaev, Asfandiyar Bigeldiyev, S. Basu, A. Abetov, Aidos Temirkhassov, Ranida Tyulebayeva, Aiyazhan Yermukhanbet\",\"doi\":\"10.2118/206627-ms\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n For accurate coal bed methane (CBM) reserves estimation, it is necessary to evaluate reservoir characteristics. We present a workflow for formation evaluation of coalbed-methane wells, by interpretation of a limited number of legacy logs, including data preprocessing, lithology identification, proximate analysis and estimation of gas content of coal beds.\\n This workflow allowed the estimation of ash content from the available logs, including selective log (analogue of photoelectric absorption), which was recorded only on the territory of the former Soviet Union and never used for such calculations before. Even though the logs were recorded by old tools with low vertical resolution, we were able to identify heterogeneity of coal seams, using the principle of core ash content distribution. Integrated analysis of old core data and recent laboratory measurements of samples from coal pillars allowed to calculate proximate properties of the coal, which showed good match with observed data and could be considered as input parameters for property distribution in the geological model.\\n Also, it is worth to mention that an advanced plug-in was deployed to perform calculation of proximate properties and gas content for all available options and to significantly reduce time for screening different algorithms and rapidly analyzing results.\",\"PeriodicalId\":10970,\"journal\":{\"name\":\"Day 1 Tue, October 12, 2021\",\"volume\":\"57 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 1 Tue, October 12, 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/206627-ms\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 1 Tue, October 12, 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/206627-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Workflow of Petrophysical Analysis Performed at Mine in Karaganda Coal Basin
For accurate coal bed methane (CBM) reserves estimation, it is necessary to evaluate reservoir characteristics. We present a workflow for formation evaluation of coalbed-methane wells, by interpretation of a limited number of legacy logs, including data preprocessing, lithology identification, proximate analysis and estimation of gas content of coal beds.
This workflow allowed the estimation of ash content from the available logs, including selective log (analogue of photoelectric absorption), which was recorded only on the territory of the former Soviet Union and never used for such calculations before. Even though the logs were recorded by old tools with low vertical resolution, we were able to identify heterogeneity of coal seams, using the principle of core ash content distribution. Integrated analysis of old core data and recent laboratory measurements of samples from coal pillars allowed to calculate proximate properties of the coal, which showed good match with observed data and could be considered as input parameters for property distribution in the geological model.
Also, it is worth to mention that an advanced plug-in was deployed to perform calculation of proximate properties and gas content for all available options and to significantly reduce time for screening different algorithms and rapidly analyzing results.