Xin Chen, Guihai Wang, Zhaofeng Wang, Zundou Liu, Zhaowei Liu, Yi Cui, Wenyuan Tian, Xiaodong Wei, Liugen Hou, Ke Yang, Gang Chen, Yaliang Xia, Xiao Yan, Zeren Zhang, Jingluan Liu
{"title":"3D Permeability Characterization Based on Pore Structure Analysis and Multi-Parameters Seismic Inversion and Its Application in H Oilfield","authors":"Xin Chen, Guihai Wang, Zhaofeng Wang, Zundou Liu, Zhaowei Liu, Yi Cui, Wenyuan Tian, Xiaodong Wei, Liugen Hou, Ke Yang, Gang Chen, Yaliang Xia, Xiao Yan, Zeren Zhang, Jingluan Liu","doi":"10.2523/IPTC-19180-MS","DOIUrl":null,"url":null,"abstract":"\n To improve the accuracy of permeability prediction, seismic constraint and sedimentary facies has often been adopted in conventional methods. However, it is porosity that both of them constrain, rather than permeability, and different pore structure with different permeability, the accuracy of permeability prediction cannot be radically improved. To address the problem of permeability prediction in carbonate reservoir, new permeability prediction technique workflow were summarized based on pore structure analysis and multi-parameters seismic inversion: division reservoir types based on the pore structure, construction of the rock types identification curve, carry out a rock type inversion and a porosity inversion constrained by seismic impedance respectively, and then get a final permeability prediction volume according to the porosity-permeability relationship and pore structure of core samples. It breaks the bottleneck that is difficult for seismic impedance (continuous variable) to constrain rock type (discrete variable), then constrains pore structure (continuous variable) related to rock type instead, and converts it into rock type using multi-parameters seismic inversion. According to the certification of new wells, this workflow have been applied successfully in carbonate reservoir of H oilfield in Middle East, it not only improves the prediction of rock type in space, but also permeability prediction accuracy.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"26 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 3 Thu, March 28, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2523/IPTC-19180-MS","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
To improve the accuracy of permeability prediction, seismic constraint and sedimentary facies has often been adopted in conventional methods. However, it is porosity that both of them constrain, rather than permeability, and different pore structure with different permeability, the accuracy of permeability prediction cannot be radically improved. To address the problem of permeability prediction in carbonate reservoir, new permeability prediction technique workflow were summarized based on pore structure analysis and multi-parameters seismic inversion: division reservoir types based on the pore structure, construction of the rock types identification curve, carry out a rock type inversion and a porosity inversion constrained by seismic impedance respectively, and then get a final permeability prediction volume according to the porosity-permeability relationship and pore structure of core samples. It breaks the bottleneck that is difficult for seismic impedance (continuous variable) to constrain rock type (discrete variable), then constrains pore structure (continuous variable) related to rock type instead, and converts it into rock type using multi-parameters seismic inversion. According to the certification of new wells, this workflow have been applied successfully in carbonate reservoir of H oilfield in Middle East, it not only improves the prediction of rock type in space, but also permeability prediction accuracy.