Exploration and production activities have moved into more challenging deep-water and subsea environments. Many of the clastic reservoirs in these environments are characterized by thick overburden, HP-HT and largely unconsolidated formations with challenging sand management issues. For effective overall field/reservoir management, it is crucial to know if and when sand would fail and be ultimately produced. Field-life sanding potential evaluation and analysis, which seeks to evaluate the sanding potential of reservoir formations during the appraisal stage and all through the development to the abandonment stage, is therefore necessary so that important reservoir/field management decisions regarding sand control deployment can be made. Recent work has identified Unconfined Compressive Strength (UCS) as a key parameter required for the evaluation and analysis of sanding potential of any reservoir formation. There is therefore the need to be able to predict this important sanding potential parameter accurately and in real time to reduce the level of uncertainties usually associated with sanding potential evaluation and analysis. In this work, neural network coded in C++ was trained with log-derived petrophysical, geomechanical and textural data to develop a stand-alone model for predicting UCS. Real-time functionality of this model is guaranteed by real time data gathering via logging while drilling (LWD) and other measurement while drilling (MWD) tools. The choice of neural network over and above other methods and techniques which have been widely used in the industry was informed by its ability to better resolve the widely known complex relationship between petrophysical, textural and geomechanical strength parameters.