... range of values is of paramount importance. In this paper, a probabilistic approach incorporating distributions for input parameters and ten empirical relationships was used to determine a range of anticipated foundation rock mass modulus values for a proposed spillway, thereby allowing for a range...

... canada government pore pressure realization probabilistic approach monte carlo simulation input parameter unconventional resource rock planar discontinuity injection horizontal stress magnitude geological subdiscipline application prediction workflow ARMA 23 0720 Probabilistic Stress...

... data predict shear wave velocity data-driven intelligent approach gradient ann model output variable input parameter ARMA 23 860 A Data-driven Intelligent Approach to Predict Shear Wave Velocity in Shale Formations Ayyaz Mustafa Civil and Environmental Engineering Department, University...

... network neural network model prediction hydraulic fracturing upstream oil & gas relative importance non-spherical particle ann model petroleum science fracture wall effect engineering particle input variable neuron machine learning proppant sphericity input parameter diameter...

... stochastic hydro-thermal simulations 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...

... 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...

.... The benefits of this approach include: rapid calculation, easy to understand results, scalable resolution for the analysis and the ability to handle uncertainty in the ranges and distributions of the input parameters. Results will be presented for the Cooper Basin in South Australia as analyzed in the GBA...

... Characterization principal stress direction confidence interval uncertainty space gradient input parameter formation stress test configuration bérard ARMA 19 66 Leveraging return of experience to improve the success rate of formation stress tests: A case study in the Gulf of Mexico Bérard, T...

... was negligible. For laboratory conditions, size effects can be very significant. 1 Introduction Reservoir Characterization size effect Lecampion hydraulic fracturing pore pressure hoop stress reservoir geomechanics approximation input parameter borehole diameter Upstream Oil & Gas...

... basin surface and the next predefined horizon (Fig. 6, inset). We model the salt as a solid visco-plastic material using a reduced form of the Munson and Dawson formulation (Munson and Dawson, 1979). The formulation has a number of input parameters (Table 1), that are calibrated according to Fredrich et...

... 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...

.... 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...

... solution layered formation fracture aperture input parameter correction factor nonuniform height ARMA 18 1079 Modeling Fracture in Layered Formations Using a Simplified 3D Displacement Discontinuity Method Tianyu Li, Omid Razavi, Jon E. Olson Hildebrand Department of Petroleum and Geosystems...

..., 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...

... ground transportation back analysis input parameter rail transportation accuracy Efficiency iteration rock mass parameter identification application exact solution algorithm different location measurement Point Response Surface Methodology back analysis method parameter estimation ARMA...

... machine learning Artificial Intelligence log analysis Upstream Oil & Gas well logging log data empirical correlation static poisson compressional wave travel time shear wave travel time input parameter coefficient correlation estimation bulk density wave travel time prediction ANN...

... 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 reservoir simulation Frictional Resistance numerical simulation procedure slippage input parameter coefficient...

... Intelligence strength log analysis Upstream Oil & Gas empirical correlation core sample carbonate reservoir prediction unconfined compressive strength s-wave velocity input parameter log data p-wave travel ANN model new correlation bulk density society of petroleum engineers ARMA 17-428...

... 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...