Accurate knowledge of rock strength is essential for in situ stress, wellbore stability and sanding evaluations. Quantitative data on rock strength can only be obtained from cores. However, cores are limited, discontinuous and often biased. Consequently, rock strength evaluation is primarily based on log strength indicators, calibrated where possible against limited core measurements. There are a number of published log-core strength correlations that can be used for rock strength modelling. These empirical relationships are developed for specific rock type, age, depth range, field and their application to other rock needs to be assessed before they are used for any geomechanical application. This paper first briefly presents common empirical rock strength equations for sedimentary rocks and discusses ways of improving rock strength estimates. While some equations such as porosity-based or sonic log-based rock strength models work reasonably well, rock strength variations with individual rock property show considerable scatter, indicating that most of the empirical models are not sufficiently generic to fit all rocks in the database. Like any other physical rock property, the variation in rock strength in a given sedimentary rock is controlled by mineralogical, sedimentological, and micro-structure of the rock and simple log-derived rock strength models need further modification and classification incorporating these geological features. It is shown that when sufficient core rock strength data exists, application of computing techniques, such as fuzzy logic and cluster pattern recognition, coupled with sedimentary facies analysis and diagenetic classification can improve strength estimation. Semi-continuous impact energy logs using portable non- destructive testing tool in the core store can be correlated with petrophysical logs to generate mechanical facies and improved sampling for conventional rock testing. Field examples and application of such techniques are presented and results are compared with commonly used simple empirical rock strength models.
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