... ABSTRACT: Bonded Block Models (BBMs) have become increasingly used to simulate laboratory rock samples in Unconfined Compressive Strength (UCS) and Triaxial tests. BBMs require input parameters that describe the deformation of the blocks themselves (block properties), as well...

... seismicity, whereby the three principal mechanisms for induced-seismicity triggering are accounted for, and uncertainties in input parameters are addressed stochastically to provide a probabilistic assessment of induced-seismicity hazard. The models provide probabilities for the expected maximum magnitudes...

... reservoir characterization reservoir geomechanics risk and uncertainty assessment spatial reasoning risk management spatial analysis approach aquitard vertical extent mechanical properties prospective area aquifer upstream oil & gas input parameter cooper basin artificial...

... network machine learning proppant neural network model prediction petroleum science wall effect shape parameter sphericity input parameter hydraulic fracturing diameter test data settling velocity upstream oil & gas relative importance non-spherical particle ann model fracture...

... and neural network development. A generic reservoir with a doublet system is used for the numerical simulations with Monte Carlo sampling of input parameters from the uniform distributions of formation properties and operations. The simulation results are used to extract the performance metrics, including...

... data is available. In this study, we assessed the use of a clustering algorithm, namely self-organizing maps, for classification of different lithological classes of limestone. The input parameters are geo-mechanical and geological properties of limestones such as density, uniaxial compressive strength...

... on closed-form expressions that are fast to run and rely on few input parameters. All models depend on the maximum and mini- mum horizontal total stresses (respectively, H and h), the pore pressure (pp) and the material tensile strength ( T ), with the ad- dition of a poroelastic constant for the Haimson...

... effective stress Reservoir Characterization uniaxial stress ratio Nikolinakou application stress-level dependency stress level Upstream Oil & Gas formulation input parameter sediment mean effective stress friction angle plastic strain sediment stress-level dependency 1. INTRODUCTION...

... framework to deviated wells, including the risk of creating tortuous fractures. Upstream Oil & Gas deviation Reservoir Characterization uncertainty space input parameter Artificial Intelligence severity pressure measurement reservoir geomechanics pore pressure assessment station 3...

..., but numerical micromechanical input parameters can interchangeably be used in twodimensional (2D) and 3D models, given they are the same element size, to produce similar emergent macroscopic properties. In 3D simulations, the real-world loading conditions can be completely replicated, thus leading...

.... To account for the uncertainty in the model input parameters, they are modeled as random variables. To this end, input parameters are divided into two classes of random variables: tensor-valued and scalar random variables and their corresponding statistical description is constructed by employing Maximum...

...-shaped fracture (Sneddon, 1946 and Green and Sneddon, 1950; Segedin, 1951), which is given as: 24(1 )( ) 1 ( )netn P R r D r G R (10) where is Poisson s ratio and G is the shear modulus. Table 1. Input parameters for penny-shaped fracture case Fracture length or radius (R) 100 ft Net pressure...

... the contact model. This model applies to rockfall simulation to delimit the area where possible falling rock may exist. Upstream Oil & Gas restitution proceedings Nishimura slippage input parameter coefficient rockfall energy ratio three-dimensional numerical model incoming angle slope...

... Artificial Intelligence correlation strength empirical correlation prediction unconfined compressive strength core sample log analysis Upstream Oil & Gas input parameter p-wave travel new correlation society of petroleum engineers carbonate reservoir s-wave velocity bulk density 1...

... Characterization Artificial Intelligence input parameter prediction travel time well logging empirical model correlation wave travel time ANN model continuous profile rock mechanical parameter neural network log data empirical correlation static poisson compressional wave travel time log analysis...

..., bulk density, minimum horizontal stress, pore pressure, over burden stress, and compressional wave and shear wave velocities. They used 600 data points to build their model and used fuzzy ranking system to rank influential input parameters for their model. The main limitation of Al-Anazi and Gates...

... ABSTRACT: Probability theory has been increasingly applied to address the uncertainty of geomechanical parameters for prediction of allowable drilling mud weight. When conducting wellbore stability analysis, input parameters are estimated from well log data and empirical correlations...

... Intelligence parameter identification measurement Point Response Surface Methodology back analysis method parameter estimation Efficiency iteration rock mass international journal algorithm different location ground transportation back analysis input parameter application exact solution 1...

... nonlinear correlation among the input parameters via the interaction matrices while ANN is implemented to objectively assign weights to the input parameters of the GBI. Fall of ground (FoG) of rock mass surrounding the excavation comprising gravity induced structurally controlled, block movement and stress...

... Simulation boundary horizontal sample stiff host reservoir geomechanics material model input parameter constituent elastic property ellipsoidal inclusion onset plastic strain anisotropy stiffness 1. INTRODUCTION The strength and deformability of shales are fundamental properties relevant...