{"title":"矩阵增产- ipr方法优选候选井","authors":"E. M. Amarfio, P. T. Adusu","doi":"10.2118/198707-MS","DOIUrl":null,"url":null,"abstract":"\n The selection of appropriate candidate wells for a stimulation operation is the most vital step for the economic success of the process. The selection criteria include assessing the well damage and choosing the appropriate approach to stimulate it. Most selection approaches consider the effects of damage and their corresponding treatment methods neglecting the economic influence of the process. This research, therefore, presents a detailed approach to candidate well selection for matrix stimulation using Vogel’s Inflow Performance Relationship (IPR) curve analysis. A non-linear mathematical optimisation model was developed in Microsoft Excel using this analysis. This model requires certain input parameters for each well in order to generate results which could be analysed for the right decision. To validate the model, data from four wells on the Nero Field were used as input parameters. The results show that Well N3 has the highest total post-stimulation production of 12 833 886 barrels of oil and therefore should be considered for the stimulation operation. Sensitivity analysis was also conducted on Well N3 to see the performance of the well when certain independent variables such as price of oil, discount rate, and stimulation time are varied. The results show that the post-stimulation well performance is positively influenced by oil price, increasing as the oil price increase. The post-stimulation well performance, however, show a negative influence from both the discount rate and stimulation time, decreasing as those two parameters increase","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"39 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimising Candidate Well Selection for Matrix Stimulation-IPR Approach\",\"authors\":\"E. M. Amarfio, P. T. Adusu\",\"doi\":\"10.2118/198707-MS\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The selection of appropriate candidate wells for a stimulation operation is the most vital step for the economic success of the process. The selection criteria include assessing the well damage and choosing the appropriate approach to stimulate it. Most selection approaches consider the effects of damage and their corresponding treatment methods neglecting the economic influence of the process. This research, therefore, presents a detailed approach to candidate well selection for matrix stimulation using Vogel’s Inflow Performance Relationship (IPR) curve analysis. A non-linear mathematical optimisation model was developed in Microsoft Excel using this analysis. This model requires certain input parameters for each well in order to generate results which could be analysed for the right decision. To validate the model, data from four wells on the Nero Field were used as input parameters. The results show that Well N3 has the highest total post-stimulation production of 12 833 886 barrels of oil and therefore should be considered for the stimulation operation. Sensitivity analysis was also conducted on Well N3 to see the performance of the well when certain independent variables such as price of oil, discount rate, and stimulation time are varied. The results show that the post-stimulation well performance is positively influenced by oil price, increasing as the oil price increase. The post-stimulation well performance, however, show a negative influence from both the discount rate and stimulation time, decreasing as those two parameters increase\",\"PeriodicalId\":11110,\"journal\":{\"name\":\"Day 2 Tue, August 06, 2019\",\"volume\":\"39 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 2 Tue, August 06, 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/198707-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 2 Tue, August 06, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/198707-MS","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimising Candidate Well Selection for Matrix Stimulation-IPR Approach
The selection of appropriate candidate wells for a stimulation operation is the most vital step for the economic success of the process. The selection criteria include assessing the well damage and choosing the appropriate approach to stimulate it. Most selection approaches consider the effects of damage and their corresponding treatment methods neglecting the economic influence of the process. This research, therefore, presents a detailed approach to candidate well selection for matrix stimulation using Vogel’s Inflow Performance Relationship (IPR) curve analysis. A non-linear mathematical optimisation model was developed in Microsoft Excel using this analysis. This model requires certain input parameters for each well in order to generate results which could be analysed for the right decision. To validate the model, data from four wells on the Nero Field were used as input parameters. The results show that Well N3 has the highest total post-stimulation production of 12 833 886 barrels of oil and therefore should be considered for the stimulation operation. Sensitivity analysis was also conducted on Well N3 to see the performance of the well when certain independent variables such as price of oil, discount rate, and stimulation time are varied. The results show that the post-stimulation well performance is positively influenced by oil price, increasing as the oil price increase. The post-stimulation well performance, however, show a negative influence from both the discount rate and stimulation time, decreasing as those two parameters increase