Geomechanical data are never sufficient in quantity or adequately precise and accurate for design purposes in mining and civil engineering. The objective of this paper is to show the variability of rock properties at the sampled point in the roadway’s roof, and then, how the statistical processing of the available geomechanical data can affect the results of numerical modelling of the roadway’s stability. Four cases were applied in the numerical analysis, using average values (the most common in geomechanical data analysis), average minus standard deviation, median, and average value minus statistical error. The study show that different approach to the same geomechanical data set can change the modelling results considerably. The case shows that average minus standard deviation is the most conservative and least risky. It gives the displacements and yielded elements zone in four times broader range comparing to the average values scenario, which is the least conservative option. The two other cases need to be studied further. The results obtained from them are placed between most favorable and most adverse values. Taking the average values corrected by statistical error for the numerical analysis seems to be the best solution. Moreover, the confidence level can be adjusted depending on the object importance and the assumed risk level.

Sand Production is one of the major challenges in oil and gas wells. It damages well head equipment and flowlines leading to lost production time and unnecessary workover expenses. Meanwhile, utilizing sand control measures when not needed can reduce production rates while reducing profits. Several methods have been developed to accurately predict sanding potentials. However, most are rather complex and time consuming. This study was justified to develop a real-time sanding model with minimal input parameters using well log data. A geomechanical model was developed to estimate critical pressure below which sand production is expected. Effective stresses at a stable perforation cavity and far field were established using stress-strain relationship. Hoek Brown's failure criterion was applied to investigate the failure mechanics once stability was lost. The parameters in the model included poisson ratio v, uniaxial compressive strength C_o, biot's poroelastic coefficient α, overburden pressure P_ob, and Hoek Brown's material constant s and a. Five different hypothetical case studies were used to validate the model and the trends are very encouraging. A FORTRAN program was written for the model in order to facilitate sanding predictions. The results observed by the model gives curves that increase at various points of depth, indicating potentially weak sandstone where sanding should be expected. A bottomhole flowing pressure, Pwf, 2500 psi was specified. It was observed that case A will not experience sand production. Cases B and C will experience sanding in the intervals 3000 - 3675ft and 3000 - 3625ft respectively. Cases D and E will experience sand production. Upon comparison with other published analytical models such as Almisned 1995 and Oseghe 2015, it gives very optimistic results. This model serves as a useful tool for making well informed cost effective decisions regarding sanding and sand control requirements.

In the original publication the resolution