Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/98272
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Type: Conference paper
Title: New models of brittleness index for shale gas reservoirs : weights of brittle minerals and rock mechanics parameters
Author: Hu, Y.
Gonzalez Perdomo, M.
Wu, K.
Chen, Z.
Zhang, K.
Yi, K.
Ren, G.
Yu, Y.
Citation: Proceedings Asia Pacific Unconventional Resources Conference and Exhibition, 2015, pp.SPE-177010-MS-1-SPE-177010-MS-12
Publisher: Society of Petroleum Engineers
Issue Date: 2015
ISBN: 9781613993880
Conference Name: Asia Pacific Unconventional Resources Conference and Exhibition (15URCE) (9 Nov 2015 - 11 Nov 2015 : Brisbane, QLD)
Statement of
Responsibility: 
Yuan Hu, M. E. Gonzalez Perdomo, Keliu Wu, Zhangxin Chen, Kai Zhang, Jie Yi, Guoxian Ren, Yanguo Yu
Abstract: Brittleness indices (BI) commonly used in the petroleum industry are based on elastic modulus or mineralogy that can be calculated from well logs. However, they ignore the weights of these two factors. Also, it is imprecise to calculate BI by considering quartz (or dolomite) as the only brittle mineral in mineralogy-based BI prediction. Shale gas reservoirs like Eagle Ford are rich in carbonate minerals. If the carbonate minerals are ignored in those reservoirs, the value of BI will be greatly underestimated. On the other hand, brittle minerals like quartz, dolomite and calcite play different roles in BI calculation. If we equally treat them without weighting in BI prediction, the BI being calculated will be inaccurate as well. This paper analyzes the influence of calcite on rock mechanics parameters and BI comparing with quartz and clay. Then new models of BI prediction are built to characterize the weight of each brittle mineral and rock mechanics parameter. Based on the least squares method, optimal values of weight coefficients will be obtained by iteration. The results show that calcite improves rock brittleness and should be considered as a brittle mineral in BI prediction. However, the weight of calcite is less than quartz. From the statistics results, quartz > dolomite > calcite > clay occurs in improving BI. The results also show that Young's modulus plays a more important role in BI prediction than Poisson's ratio.
Rights: © 2015 Society of Petroleum Engineers
DOI: 10.2118/177010-MS
Published version: https://www.onepetro.org/conference-paper/SPE-177010-MS
Appears in Collections:Aurora harvest 3
Australian School of Petroleum publications

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