{"title":"Practical Application of Tensor Model in Laminated Sand Shale Analysis","authors":"Aditya Ariewijaya","doi":"10.2118/197208-ms","DOIUrl":null,"url":null,"abstract":"\n Correctly evaluating reservoirs with thin laminations can be challenging. From a conventional perspective, this type of reservoir is often considered to be nonpay because of its low resistivity. Tensor models help improve resistivity using horizontal (RH) and vertical (RV) resistivity measurements from triaxial induction logging tools. In the absence of triaxial advanced measurements of RH and RV, tensor model equations using a conventional openhole (triple combo data) can be used.\n This approach is based on rearranging the tensor model with the Moran-Gianzero equation and using several assumptions for unique cases. This method explains the workflow to calculate sand resistivity correctly using only openhole data as well as calculating the anisotropic shale resistivity that is often estimated from nearby shales. A mathematical method is preferred to obtain consistent results for anisotropic shale resistivity parameters to reduce calculation uncertainty. Sensitivity analyses are created to provide a sense of how these parameters affect the results on sand resistivity.\n For a vertical well where relative dip is close to zero, RSd can be calculated without knowing the RshV. The same equation provides a 10% error on RSd at VLam<10% and relative dip <10°. At a higher relative dip and anisotropic shale resistivity, a cubic equation with a new coefficient is proposed. Sensitivity analyses are made to compare a true RSd and calculated RSd with changing RshH and RshV variables. The model demonstrates that a 10% change on RshH could cause a 30% error on RSd at VLam of 10%, while changes in RshV only begins to affect RSd up to 30% at VLam 70%. Graphical and mathematical methods are proposed to help prevent misestimating the RshH and RshV. The graphical method is preferred when a complete data set for all relative dip is available, while the mathematical method is preferred when the data set is limited.\n Unique cases where the RSd can be calculated as well as demonstrations on how anisotropic shale resistivity parameters can be determined using only conventional openhole (triple combo) data are highlighted. The additional set of constraints on the iteration of the cubic equation represents an improvement of the previous study, whereas the proposed method to determine the RshH and RshV helps prevent estimation errors of these parameters and helps improve RSd calculation accuracy.","PeriodicalId":11328,"journal":{"name":"Day 4 Thu, November 14, 2019","volume":"112 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 4 Thu, November 14, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/197208-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Correctly evaluating reservoirs with thin laminations can be challenging. From a conventional perspective, this type of reservoir is often considered to be nonpay because of its low resistivity. Tensor models help improve resistivity using horizontal (RH) and vertical (RV) resistivity measurements from triaxial induction logging tools. In the absence of triaxial advanced measurements of RH and RV, tensor model equations using a conventional openhole (triple combo data) can be used.
This approach is based on rearranging the tensor model with the Moran-Gianzero equation and using several assumptions for unique cases. This method explains the workflow to calculate sand resistivity correctly using only openhole data as well as calculating the anisotropic shale resistivity that is often estimated from nearby shales. A mathematical method is preferred to obtain consistent results for anisotropic shale resistivity parameters to reduce calculation uncertainty. Sensitivity analyses are created to provide a sense of how these parameters affect the results on sand resistivity.
For a vertical well where relative dip is close to zero, RSd can be calculated without knowing the RshV. The same equation provides a 10% error on RSd at VLam<10% and relative dip <10°. At a higher relative dip and anisotropic shale resistivity, a cubic equation with a new coefficient is proposed. Sensitivity analyses are made to compare a true RSd and calculated RSd with changing RshH and RshV variables. The model demonstrates that a 10% change on RshH could cause a 30% error on RSd at VLam of 10%, while changes in RshV only begins to affect RSd up to 30% at VLam 70%. Graphical and mathematical methods are proposed to help prevent misestimating the RshH and RshV. The graphical method is preferred when a complete data set for all relative dip is available, while the mathematical method is preferred when the data set is limited.
Unique cases where the RSd can be calculated as well as demonstrations on how anisotropic shale resistivity parameters can be determined using only conventional openhole (triple combo) data are highlighted. The additional set of constraints on the iteration of the cubic equation represents an improvement of the previous study, whereas the proposed method to determine the RshH and RshV helps prevent estimation errors of these parameters and helps improve RSd calculation accuracy.