{"title":"模糊逻辑在不同排水面积水平井伪表皮估计中的应用","authors":"","doi":"10.33140/pcii.02.02.05","DOIUrl":null,"url":null,"abstract":"Horizontal wells are becoming widely used for primary and enhanced oil recovery operations compared to vertical\nwells as a result of their higher productivity due to large wellbore section exposed to the reservoir which is necessary\nfor both fluids production and injection operations.\nIn this work a new application related with using of fuzzy logic in the field of petroleum engineering was introduced to\ndevelop pseudo skin shape related factor to calculate and estimate the productivity of pseudo steady state horizontal\nwells. We can use fuzzy logic in order to determine the pattern and relationship between data set where this pattern\nmay not be clearly known or there is no mathematical relationship among them. Prediction of introduced model has\nbeen tested against known model (Bahadori 2012 model) that developed a simple model of pseudo skin factor for\nhorizontal well located within rectangular and square drainage areas [1].\nAfter training the model by using 2000 data set, it was successfully to estimate the pseudo skin shape related factor by\ntesting the model by using 1000 data set. Results indicate that the introduced model has an excellent agreement with\nvalues that have been obtained by using Bahadori 2012 model with average absolute deviation being less than 2.05%.\nSensitivity study was used by investigate different cluster values ranging from (0.3 – 0.7) to determine which is yield\nthe lowest average calculation error. Furthermore, trend analysis showed that there is excellent agreement between\nthis model and Bahadori 2012 model in the general trend.","PeriodicalId":355186,"journal":{"name":"Petroleum and Chemical Industry International","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Fuzzy Logic for Pseudo Skin Estimation for Horizontal wells within\\nVarious Drainage Areas\",\"authors\":\"\",\"doi\":\"10.33140/pcii.02.02.05\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Horizontal wells are becoming widely used for primary and enhanced oil recovery operations compared to vertical\\nwells as a result of their higher productivity due to large wellbore section exposed to the reservoir which is necessary\\nfor both fluids production and injection operations.\\nIn this work a new application related with using of fuzzy logic in the field of petroleum engineering was introduced to\\ndevelop pseudo skin shape related factor to calculate and estimate the productivity of pseudo steady state horizontal\\nwells. We can use fuzzy logic in order to determine the pattern and relationship between data set where this pattern\\nmay not be clearly known or there is no mathematical relationship among them. Prediction of introduced model has\\nbeen tested against known model (Bahadori 2012 model) that developed a simple model of pseudo skin factor for\\nhorizontal well located within rectangular and square drainage areas [1].\\nAfter training the model by using 2000 data set, it was successfully to estimate the pseudo skin shape related factor by\\ntesting the model by using 1000 data set. Results indicate that the introduced model has an excellent agreement with\\nvalues that have been obtained by using Bahadori 2012 model with average absolute deviation being less than 2.05%.\\nSensitivity study was used by investigate different cluster values ranging from (0.3 – 0.7) to determine which is yield\\nthe lowest average calculation error. Furthermore, trend analysis showed that there is excellent agreement between\\nthis model and Bahadori 2012 model in the general trend.\",\"PeriodicalId\":355186,\"journal\":{\"name\":\"Petroleum and Chemical Industry International\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Petroleum and Chemical Industry International\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33140/pcii.02.02.05\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Petroleum and Chemical Industry International","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33140/pcii.02.02.05","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Fuzzy Logic for Pseudo Skin Estimation for Horizontal wells within
Various Drainage Areas
Horizontal wells are becoming widely used for primary and enhanced oil recovery operations compared to vertical
wells as a result of their higher productivity due to large wellbore section exposed to the reservoir which is necessary
for both fluids production and injection operations.
In this work a new application related with using of fuzzy logic in the field of petroleum engineering was introduced to
develop pseudo skin shape related factor to calculate and estimate the productivity of pseudo steady state horizontal
wells. We can use fuzzy logic in order to determine the pattern and relationship between data set where this pattern
may not be clearly known or there is no mathematical relationship among them. Prediction of introduced model has
been tested against known model (Bahadori 2012 model) that developed a simple model of pseudo skin factor for
horizontal well located within rectangular and square drainage areas [1].
After training the model by using 2000 data set, it was successfully to estimate the pseudo skin shape related factor by
testing the model by using 1000 data set. Results indicate that the introduced model has an excellent agreement with
values that have been obtained by using Bahadori 2012 model with average absolute deviation being less than 2.05%.
Sensitivity study was used by investigate different cluster values ranging from (0.3 – 0.7) to determine which is yield
the lowest average calculation error. Furthermore, trend analysis showed that there is excellent agreement between
this model and Bahadori 2012 model in the general trend.