The scaling-up of laboratory rock mechanical measurements from sample-scale to reservoir-scale is fundamental to evaluation of wellbore stability, sanding potential, reservoir compaction or casing failure. Understanding rock heterogeneity is fundamental for adequate scaling-up laboratory measurements to core- and reservoir-scales and thus, to predictions of mechanical failure. Historically, scaling-up from core scale to reservoir scale has been dependent on calibration of log-based models to a sparsely sampled data set of rock mechanical property measurements made on core plugs. Such a sparsely sampled data set of core plug measurements alone may inadequately characterize the range heterogeneities in the reservoir, resulting in less than optimum log-based predictive models. With the introduction of continuous, high resolution, rock strength (UCS) measurements on core via scratch testing, an excellent calibration reference for producing robust log-based predictions of rock strength now exist.
In this study, high-resolution measurements of strength heterogeneity were obtained as a function of core length and were correlated with fundamental textural and compositional parameters from petrographic analysis. Using adaptive learning neural networks, fundamental relationships between log measurements and rock strength were obtained. This methodology was adequate for characterizing the intrinsic rock heterogeneity at appropriate scales for mechanical analysis of completion design and sanding (0.25 ft). The methodology is also potentially applicable to the scaling-up of other fundamental mechanical properties such as in-situ strength, compressibility and thick-walled cylinder strength.
Results show that intrinsic textural heterogeneity and strength heterogeneity are strongly related in sedimentary rocks. Recognizing the importance of rock heterogeneity and being able to scale-up this property to core and reservoir scales via log measurements results in significant improvements in the predictive capacity for sanding potential and wellbore stability. For example, thin layers of considerably weaker-strength than the surrounding rock, undetectable from conventional log-based rock strength predictions, were detected and included in the mechanical model. In addition to possessing high sanding potential, these weaker sections are also regions of fluid loss during drilling. Results can be used for selection of competent rock across the field (based on LWD measurements) for multilateral junction placement, and for selection of optimum completion strategies.
Rock mechanics evaluations of wellbore stability, sanding potential, reservoir compaction or casing failure require the scaling-up of fundamental laboratory measurements of rock mechanical properties from sample-scale to reservoir-scale. This scaling-up of mechanical properties is often conducted via correlation with log measurements and thus, log-based correlations of rock properties (primarily strength and elastic moduli) are of fundamental importance for drilling, completion, and long-term production calculations. Historically, scaling-up from core scale to reservoir scale has been performed using sparsely sampled data sets of rock mechanical property measurements made on core plugs. As such sparsely sampled data sets of core plug measurements are not likely to adequately characterize the range heterogeneities in the reservoir, less than optimum log-based predictive models have typically been the result.