A Review of Brittleness Index Correlations for Unconventional Tight and Ultra-Tight Reservoirs

Abstract

Rock brittleness is pivotal in the development of the unconventional reservoirs. However, the existence of various methods of calculating the brittleness index (BI) such as the mineral-based brittleness index (MBI), the log-based brittleness index (LBI), and the elastic-based brittleness index (EBI) lead to inconclusive estimations of the brittleness index. Hence, in this work, the existing correlations are applied on prolific unconventional plays in the U.S. such as the Marcellus, Bakken, Niobrara, and Chattanooga Formation to examine the various BI methods. A detailed comparison between the MBI, LBI, and EBI has also been conducted. The results show that a universal correlation cannot be derived in order to define brittleness since it is a function of lithology. Correlation parameters vary significantly from one shale play to another. Nevertheless, an overall trend shows that abundant quartz and carbonates content yield high brittleness values, while the high clay content and porosity lower the rock brittleness.

Problems of Multiscale Brittleness Estimation for Hydrocarbon Reservoir Exploration and Development

“…Brittleness of the medium is a very complicated topic in its nature [2][3][4][5]. Many mechanical concepts have clear physical and mathematical meaning: elastic moduli are defined with respect to stress and strain changes; strength properties are defined as conditions standing for failure, and so on.…”

Data-driven optimization of brittleness index for hydraulic fracturing

“…Dozens of BI calculations are proposed for various formations with unique lithological and elastic properties, which could be roughly classified into (i) the mineral-based brittleness index (MBI) (Buller et al, 2010;Jarvie, Hill, Ruble, & Pollastro, 2007), (ii) the logging-based brittleness index (LBI) (Xiaochun Jin, Shah, Truax, & Roegiers, 2014), and (iii) the elastic-based brittleness index (EBI) (Rickman, Mullen, Petre, Grieser, & Kundert, 2008;Sharma & Chopra, 2015). Current efforts evaluate the BI calculations mainly based on numerical simulation (Xia et al, 2019), logging data (Mews, Alhubail, & Barati, 2019), and static tests (Feng, Zhang, Rezagholilou, Roshan, & Sarmadivaleh, 2019). Limited test at field-practical scales is presented, thus restricting the reliability and persuasion of these BI calculations.…”

Investigating the Relationship between the Brittleness Index and Crack Initiation Stress for the Granite under Triaxial Compression

“…), extensive researches have been carried out to propose new parameters or indexes to quantitatively evaluate rock fracture properties and predict rock mechanical behaviors (Tarasov and Potvin, 2013;Wawersik and Fairhurst, 1970;Batougina et al, 1983;Ashby and Sammis, 1990;Stavrogin and Protossenia, 1985;Baron, 1992;Aubertin et al, 1994;Xia, et al, 2016;Bishop,1967;Hajiabdolmajid, et al, 2002;Hajiabdolmajid and Kaiser, 2003;Li et al, 2012). Amongst the parameters, brittleness index (BI) has been widely employed to predict rock burst potential and develop unconventional reservoirs (Tarasov and Potvin, 2013;Mews et al, 2019). The brittle failure of rock is closely related to the crack initiation, propagation, coalescences and nucleation from micro to macro scales (Damjanac and Fairhurst, 2010;Xi et al, 2018aXi et al, , 2018bGuo et al, 2020;Xi et al, 2020).…”

“…To date, many different expressions for the brittleness index have been proposed based on the mineral composites, microstructures, penetration, energy balance analysis, unconfined compressive strength and/or Brazilian tensile strength, etc (Mews et al, 2019). Coates and Parsons (1966) proposed to use the ratio of the reversible strain to the total strain at the point of failure for the brittleness index.…”