...). then applied artificial intelligence (based on Python programming language), specifically machine learning, to come up with the most suitable model. Then the authors checked the accuracy of the model and applied it to selecting the appropriate EOR method for each field in the Lower Miocene, Y basin, Vietnam. 2...

... prediction models of cutting performance have been proposed. Especially, empirical models using index properties and observed field performance data have been widely used. Lately, big data collected from TBM tunnel sites have been used with the techniques of machine learning and artificial intelligence...

...., and Jafarnezhad A., (2012). Real-time prediction of rate of penetration during drilling operation in oil and gas wells, The 46th U.S. Rock Mechanics/Geomechanics Symposium, Chicago, Illinois. 6. Soares C. and Gray K., (2019). Real-time predictive capabilities of analytical and machine learning rate of penetration...

... Characterization structural geology machine learning Artificial Intelligence metals & mining strength Significance Maleki stress Gradient coal bump asian rock mechanics symposium 29 case study isrm international symposium violent failure correlation coefficient reservoir geomechanics Thickness...

... Artificial Intelligence isrm international symposium asian rock mechanics symposium 29 AE source Upstream Oil & Gas AE signal singapore arms10 AE sensor Reservoir Characterization traditional method refraction source location method new method machine learning triaxial...

... process has been widely used in rock mechanics tests and rock engineering projects throughout the world Upstream Oil & Gas experiment genetic algorithm reliability machine learning source location algorithm arrival time algorithm source simulation experiment Reservoir Characterization...

... try, many artificial intelligence and machine learning methods, such as back propagation (BP), radial basis function (RBF), support vector machine (SVM) were also utilized (Liu et al., 2017; Zhang, 2017). However, the evaluation results were barely satisfactory no matter which method was taken use...

... asian rock mechanics symposium 29 artificial neural network application machine learning subdivision rock cavern excavation method excavation excavation parameter rock mass US government cavern excavation design ANN model construction Optimization of the Excavation Design for Rock...

... and material (including ore and waste). Industry statistics indicate that a high proportion of fatal injuries occur within 5 m from the working face. This is as expected, since the bulk of the workforce is predominately concentrated at the face. machine learning risk management Artificial...

... for forecasting the onset of the dynamics events (Priestley, 1981; Lu et al., 2012). Upstream Oil & Gas AE signal kHz machine learning eigenvalue contribution rate eigenfrequency time series precursor periodicity Artificial Intelligence isrm international symposium component wave main...

... using various statistical analysis techniques that will be presented. Artificial Intelligence accuracy Stability Graph machine learning regression logistic regression stability number data mining principal component logistic regression multiple linear regression metals & mining...

... understanding of induced seismicity source mechanisms in underground mining can help to optimize mining operations, reduce delays and production losses. Understanding the main source mechanism in mines is essential to a better prediction of the rock mass response to mining. metals & mining machine...

... to predict the PPV value (Dehghani and Ataee-Pour, 2011; Monjezi et al., 2011; Amnieh et al., 2012; Faradonbeh et al., 2016). expert system machine learning fuzzy logic Reservoir Characterization large-span tunnel vibration velocity particle velocity PPV isrm international symposium asian...

... of the spatial variability of geotechnical engineering is essential to find a balance between the complexity of the survey data and the reliability of the simulation results. Conditional probability is the tool for stochastic analysis. risk management risk and uncertainty assessment machine learning...

... and consequently it influences on the strength parameters. machine learning Upstream Oil & Gas drilling operation igneous rock Artificial Intelligence isrm international symposium rock mass classification hoek-brown strength parameter sedimentary rock Reservoir Characterization RMR asian...

... a chisel tool at 5 mm depth of cut in unrelieved cutting mode. They indicated that there was a good relationship between TC and SE. reservoir geomechanics metals & mining Upstream Oil & Gas machine learning Artificial Intelligence specific energy structural geology isrm international...

... Artificial Intelligence deviation tunnel face digital image orientation value asian rock mechanics symposium 29 digital image processing machine learning orientation detection line segment characterization smx analyst image processing trace detection Joint Trace Detection in Digital Images...

... in the United States (NEA, 2001). machine learning structural geology isrm international symposium asian rock mechanics symposium 29 Reservoir Characterization reservoir geomechanics Upstream Oil & Gas borehole xinchang url site China Disposal repository Site selection url site beishan...

... abrasivity characteristic evaluation Artificial Intelligence regression model multiple regression model Cerchar abrasivity index CAI correlation coefficient machine learning Upstream Oil & Gas correlation asian rock mechanics symposium 29 quartz content 2018. International Society...

... DInSAR subsidence velocity asian rock mechanics symposium 29 machine learning gp survey ground subsidence Tuzla salt mining subsidence deformation SAR data 2018. International Society for Rock Mechanics and Rock Engineering / Society for Rock Mechanics and Engineering Geology ...