... planning of project. classification comprehensive evaluation method machine learning tbm tbm performance comprehensive evaluation system membership function upstream oil & gas evaluation factor assessment eurock 2020 artificial intelligence disc cutter fuzzy comprehensive evaluation...

... of the algorithm is presented in [3]. Reservoir Characterization metals & mining production control seismic modeling variant procedure emergence Artificial Intelligence node connectivity component rasskazov machine learning production monitoring flow metering manifestation Rock Pressure...

... 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 uniaxial compressive strength travel time artificial...

... conditions, and thus the potential for dynamic loads. Reservoir Characterization Upstream Oil & Gas metals & mining strength standardized regression coefficient coefficient machine learning structural geology correlation pillar factor safety Maleki mechanics Artificial...

... creativecommon machine learning bayesian network structure procedia engineering 191 Bayesian Inference responsibility Artificial Intelligence open access article rock burst hazard prediction incomplete data elsevier ltd eurock 2017 University license by-nc-nd 4 Procedia Engineering 191 ( 2017...

... in this paper was inspired by the methods of assessing physico-mechanical characteristics published in scientific literature. Principally, the most widely used is regression, but recently more sophisticated techniques such as neural networks have frequently been applied as well [5]. machine learning...

... shape pores and their corresponding ellipsoids is presented in section 3. Then, we evaluated analytically the contribution tensors of the ellipsoids and we estimate the relative error due to this approximation. Artificial Intelligence reservoir geomechanics approximation pore machine...

..., and tillite. machine learning Artificial Intelligence Upstream Oil & Gas index test strength prediction input parameter artificial neural network Ferentinou neural network igneous rock kwazulu-natal uniaxial compressive strength application estimation lithology Brazilian tensile...

.... Artificial Intelligence correlation salimi text classification rock mass classification system classification system penetration rate tbm performance prediction natural language Upstream Oil & Gas FPI application machine learning tunnel estimation regression tree decision tree learning...

... 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...

... criterion is not reliable due to the low correlation coefficient values using Mohr-Coulomb parameters and Hoek-Brown criterion in slope stability tables proposed by [6, 9]. reservoir geomechanics displacement Reservoir Characterization Upstream Oil & Gas machine learning Artificial...

... neural network SDI soft computing approach machine learning fuzzy logic durability index Ahmad aggregate impact value equation regression analysis procedia engineering 191 ACV multiple regression Upstream Oil & Gas PLI prediction independent variable Singh Procedia Engineering...

.... bit selection id bit mechanism machine learning Engineering Rock mechanics Artificial Intelligence ae rms Blunt Pattern Recognition Upstream Oil & Gas acoustic emission impregnated diamond bit algorithm subset application input variable combination drilling test Drilling tool...

... types, mainly welded tuff, granite, sandstone, limestone, and argillite. The data have very wide strength range. The UCS values ranges from 1.3 to 468.5 MPa. But, the strength of most samples is less than 150 MPa. machine learning Artificial Intelligence reservoir geomechanics Rock mechanics...

.... Artificial Intelligence Reservoir Characterization variate normal distribution reservoir geomechanics tensor variate normal distribution tensor covariance matrix Gupta variability nagar 2000 machine learning Upstream Oil & Gas Rock mechanics stress tensor application statistics matrix...

... directional shear strength model Artificial Intelligence orientation bedding strength model cutback rock mass shear strength model machine learning Upstream Oil & Gas structural geological model shale band slope stability limit equilibrium analysis pilbara region anisotropic rock mass...

... stability rock mass slope stability Design Change recommendation friable zone machine learning Upstream Oil & Gas crest line Implementation subdrill crest loss heat map blast design northeast section minor slope failure final wall blast design Rock Mechanics and Rock Engineering: From...

... (2000) vividly calls it soft science methods, and lists it as one of achievements in rock mechanics. Soft science methods aiming at the predicting of rockbrust includes many kinds, this paper groups it into twelve categories. machine learning rockburst prediction Rock mechanics Upstream Oil...

... this tunnel. Field and laboratory studies were carried out to measure the instantaneous breaking rate ( IBR ) of the hammer as well as the properties of the rock mass and the intact rock that were associated with each sample. Reservoir Characterization machine learning Artificial Intelligence...

... made to determine the rock mass quality using remote sensing data or digital pictures. machine learning Artificial Intelligence Reservoir Characterization persistence metals & mining reservoir geomechanics calculation information geomechanical parameter discontinuity rock mass...