{"title":"一种利用钻井测井参数实时检测地压的新方法","authors":"Gongquan Li, Zhizhan Wang","doi":"10.1109/MEC.2011.6026001","DOIUrl":null,"url":null,"abstract":"Real-Time detecting abnormal formation pressure can not only prevent the happening of drilling hazard, but also effective protect the pollution of reservoir. A detective model can be made from some drilling-logging parameters because these parameters collected by comprehensive logging instrument can indicate the abnormal pressure information existing in the formation. First, a PCA method is used to process six wells from Dongying Depression, China in order to reduce the cross-correlation among parameters and the count. Then a neural net model is trained by the result in the first step. Finally, thirty wells are detected by the model. The correspondence between real data and predicted results is about 84.6%. So this method can be used in the real case.","PeriodicalId":386083,"journal":{"name":"2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A new method for detecting real-time geopressure from drilling-logging parameters\",\"authors\":\"Gongquan Li, Zhizhan Wang\",\"doi\":\"10.1109/MEC.2011.6026001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Real-Time detecting abnormal formation pressure can not only prevent the happening of drilling hazard, but also effective protect the pollution of reservoir. A detective model can be made from some drilling-logging parameters because these parameters collected by comprehensive logging instrument can indicate the abnormal pressure information existing in the formation. First, a PCA method is used to process six wells from Dongying Depression, China in order to reduce the cross-correlation among parameters and the count. Then a neural net model is trained by the result in the first step. Finally, thirty wells are detected by the model. The correspondence between real data and predicted results is about 84.6%. So this method can be used in the real case.\",\"PeriodicalId\":386083,\"journal\":{\"name\":\"2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MEC.2011.6026001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MEC.2011.6026001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new method for detecting real-time geopressure from drilling-logging parameters
Real-Time detecting abnormal formation pressure can not only prevent the happening of drilling hazard, but also effective protect the pollution of reservoir. A detective model can be made from some drilling-logging parameters because these parameters collected by comprehensive logging instrument can indicate the abnormal pressure information existing in the formation. First, a PCA method is used to process six wells from Dongying Depression, China in order to reduce the cross-correlation among parameters and the count. Then a neural net model is trained by the result in the first step. Finally, thirty wells are detected by the model. The correspondence between real data and predicted results is about 84.6%. So this method can be used in the real case.