... Reservoir Characterization Artificial Intelligence correlation rock strength machine learning well logging strength log analysis Upstream Oil & Gas prediction porosity reservoir geomechanics neural network input parameter drilling data compressive strength sonic travel time...

...) and igneous rocks (granitoids and dolerite). A back propagation artificial neural network was developed and trained in order to predict UCS. The input parameters were unit weight γ, (Is ( 50 )), (σ t ), and lithology. The lithology was introduced in the neural network as a qualitative input parameter...

... or similar classification systems in providing an accurate estimation of TBM penetration rate has had limited success due to the nature of the weights assigned to the input parameters. The results of many investigations on this topic have shown a weak correlation between TBM penetration rate and RMR...

... elasticity machine learning fuzzy logic input parameter procedia engineering 191 dry density correlation uniaxial compressive strength genetic algorithm expression programming Gene Expression Programming equation gep method Reservoir Characterization Upstream Oil & Gas behnia algorithm...

... Upstream Oil & Gas residual strength procedure sensitivity analysis strength input parameter procedia engineering 191 tensile strength reservoir geomechanics contact cohesion contact tensile strength Reservoir Characterization flat-jointed material particle flow code effective...

..., roof lithology and the cutting direction of the continuous miner. Laboratory strength testing on shale in the immediate roof was done and the test results were used as input parameters into the Examine2D modelling. The numerical modelling results were analysed in an attempt to correlate the modelling...

... to stability problems. To solve these problems empirical, numerical and analytical methods are frequently used. All of the mentioned methods require input parameters such as strength and deformability properties of rock mass. Conventionally, insitu large-scale tests are required to determine the rock mass...

... of the stress dependent input parameters for pea gravel used as backfill. The input parameters have been calibrated using results from large oedometer tests and direct shear tests on a numerical oedometer test. To verify the obtained input parameters, a numerical shear test has been performed. It can be shown...

... Oil & Gas input parameter neural network physical property mean value compressive strength best performance porosity intelligent tool granite Artificial Intelligence resistance different combination prediction ANN model compressive behavior compression cross-validation scheme...

... Abstract The uniaxial compressive strength of rock material (UCS) is one of the fundamental input parameters for engineering applications to be constructed on/in rock masses such as deep slopes, tunnels and dams. However, preparation of the high quality cores for laboratory studies...

... analysis the influence of individual input parameters on the model output is investigated. Based on the sensitivity analysis insignificant parameters can be excluded from further design and interaction of different parameters can be revealed. Uncertainty analysis Uncertainty analysis investigates...

... the rock mass. More accurate knowledge of the inputs permits reliability of the analyses to be enhanced and excessive safety margins to be avoided. The task of this study was to assess the impact of the key input parameters on slope stability of the open pit mine inYakutia. A parametric analysis...

... into the calculation the stochastic character of the input parameters. In the first example the stochastic character of shear strength characteristics of soils is considered. The second example takes into account the stochastic character of the basic parameters of dynamic load (frequency and amplitude of the dynamic...

... important to describe relations between input parameters in an empirical model if one wants to obtain accurate results during stochastic analysis. In this paper, we run two different Monte Carlo simulations for the comparison of results generated from empirical Hoek-Brown failure criterion. First simulation...

... at Ulusay Hacettepe University, Department of Geological Engineering, Ankara, Turkey Abstract: Rock Mass Rating (RMR) and Q systems are the rock mass classification schemes most widely used in mining and tunneling. However, the authors experienced some difficulties in determining some input parameters...

... Abstract: Traditionally, tunnel lining design has been performed using deterministic analyses and conservative partial factors. Reliability methods offer a more rational approach to this process by incorporating the uncertainty in the input parameters and evaluating the probability of failure...

... intact rock strength ( ci) and the intact rock material constant mi. These intact properties are required to determine numerical mod- el input parameters when using the Hoek-Brown failure criteria (Hoek et al. 2002). Coupled with the Geological Strength Index, GSI, it is possible to determine rockmass...

..., and/or predictive indices such as fracture toughness, Schmidt hammer, Shore hardness, Punch penetration and DRI tests have been widely used as input parameters for predicting TBM performance (Graham 1976, Farmer & Glossop 1980, Blinheim 1979, Bamfrod 1984, Dollinger et al. 1998), a variety of theoretical models...

... simulation strength input parameter model parameter slope stability analysis arrangement assembly deformation circular element Rock Mechanics in Civil and Environmental Engineering Zhao, Labiouse, Dudt & Mathier (eds) © 2010 Taylor & Francis Group, London, ISBN 978-0-415-58654-2 Effects of model...

... more vulnerable and resulted into local and regional failure. A detail field study has been carried out to collect the representative rock samples for determination of geotechnical properties of rock. These properties have been used as input parameters for the three dimensional finite difference slope...