Rock Strength from Core and Logs, Where We Stand and Ways to Go

Abstract Knowledge of accurate rock strength is essential for in situ stress estimation, wellbore stability analysis, sand production prediction and other geomechanical applications. Reliable 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 measured values. 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 and field. Their general applications, therefore, need to be critically assessed on a case by case basis. This paper briefly: (i) outlines the best practice for obtaining quality rock strength data from core tests; (ii) presents common empirical rock strength equations for sedimentary rocks and (iii) 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 within individual rock properties 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 properties, the variation in rock strength in a given sedimentary rock is controlled by mineralogy, sedimentology and micro-structure of the rock and simple log-derived rock strength models need further modification and classification incorporating these geological characteristics. This paper has shown that when sufficient core rock strength data exists, applications 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 tools can be correlated with petrophysical logs to generate mechanical facies and improved sampling for conventional rock testing.