E3S Web Conf.
Volume 89, 2019The 2018 International Symposium of the Society of Core Analysts (SCA 2018)
|Number of page(s)||8|
|Section||Laboratory Core Analysis|
|Published online||29 March 2019|
Validation of Permeability and Relative Permeability Data Using Mercury Injection Capillary Pressure Data
University of Manituba
2 Equinor Canada Ltd.
3 Equinor ASA, Norway
* Corresponding author: Douglas.Ruth@umanitoba.ca
This paper reports on a study with the objective to validate a set of core analysis data using a combination of mercury injection capillary pressure (MICP) data and statistical correlation techniques. The data set is from an off-shore reservoir in Atlantic Canada. Analysis of this reservoir was complicated by the fact that the permeabilities of the samples were high, greater than 2400 mD. The analysis was done using an existing data set, not a data set specifically tailored for the techniques used in the analysis. The data analyzed included samples that represented seven zones in a single well. Porosities and permeabilities were available for the MICP samples. Electrical properties, along with porosities and permeabilities, were available on samples from each zone, but not from the same depths as the MICP samples. Steady-state relative permeabilities (SSRP) were available for stacked samples in each zone; one of the samples in the stack was a companion sample for one of the MICP samples from that zone. The MICP results were used to validate the permeability measurements using both the Swanson method (SM) and the Ruth-Lindsay-Allen (RLAM) method. The SM, using published correlation parameters, significantly under-predicted the permeabilities; the RLAM, which uses no correlation parameters, gave predictions within a maximum error of just over 33% and a mean error of -12%. The MICP data was used to validate the shapes of the SSRP curves using the Gates and Tempelaar-Lietz method (GT-LM), the Burdine method (BM), and a modified Burdine method (MBM). The GT-LM, which uses no correlation parameters, provided good predictions of the wetting phase SSRP curves but very poor predictions of the non-wetting phase SSRP curves. The BM, using published correlation parameters, provided poor predictions of the wetting phase SSRP curves but improved predictions of the non-wetting phase SSRP curves. The MBM provided good predictions of the wetting phase SSRP curves and acceptable predictions of the non-wetting phase SSRP curves. The MBM method does use a correlation parameter but a single value was used for all seven zones. This work provides a protocol for validating core analysis data that can be implemented in a straightforward manner to determine the “quality” of the data. The results emphasize the importance of MICP as an experimental technique. A proposed modified workflow is presented that would optimize the validation protocol.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.