...; Aladasani and Bai, 2010). In this study, the authors collected data of worldwide projects successfully applying EOR methods based on the screening criteria of J. J. Taber et al. (Taber et al., 1996). then applied artificial intelligence (based on Python programming language), specifically machine learning...

... 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. In addition, some considerations using...

... than that of traditional models. These results have shown the competitiveness of the ANN models and its high applicability to drilling operations. isrm sponsored regional symposium 2022 vietnam government rop drilling operation machine learning upstream oil & gas neural network th...

... characterization geologist coseismic landslide artificial intelligence earthquake structural geology machine learning gulang earthquake landslide probability society iop publishing iop conf gansu province environmental science 861 conference intensity regression coefficient landslide probability...

... development in the future [3] . Over the past decades, various methods have been proposed to carry out experiments for generating LSM. Among them, the machine learning models achieved excellent performance in landslides predicting and susceptibility mapping and became the more popular ones, such as decision...

... stratum between the existing boreholes is often not investigated, which brings the possibility of vital errors in underground design due to inaccurate interpretation of the ground conditions. It is desirable to utilize advanced machine learning methods to predict and update the 3D geo-models. Machine...

...]. Besides the aforementioned methods, the machine learning method was an emerging approach to increase computational efficiency [25-26]. However, these methods, which combine Bayesian and machine learning, did not account for the effect of uncertain factors (i.e. rainfall, water table fluctuation...

... and mechanical parameter selection accuracy. geologist mineral distribution histogram structural geology artificial intelligence wellbore design wellbore integrity society iop publishing iop conf conference variation phyllosilicate machine learning correlation statistics characteristic...

... Abstract It's essential to identify the soil distribution for shield tunneling construction, especially in mixed ground conditions. Machine learning models have been conducted to infer the stratigraphic heterogeneity, but mostly relying on the geological survey report. The sparse borehole data...

... characterization engineering tunnel evolutionary algorithm reservoir geomechanics journal chinese journal artificial intelligence machine learning society iop publishing iop conf criterion simulation geological subdiscipline rock mechanic environmental science 861 prediction rockburst conference...

... in China [4,5] . clastic rock geologist fracturing fluid oil shale mudrock rock type sedimentary rock fracture proppant geology complex reservoir artificial intelligence machine learning environmental science 861 viscosity fracture propagation morphology shale gas reservoir...

... at the same site and the universality of the two methods has not been well discussed yet. olkiluoto artificial intelligence geological disposal information management hlw geologist machine learning society iop publishing iop conf posiva oy groundwater characteristic requirement conductivity...

... geologist machine learning society iop publishing iop conf geology geological subdiscipline environmental science 861 correlation algorithm swcc prediction correlation coefficient artificial intelligence coefficient conference university criterion suction equation saturation...

... of rock samples was established based on discrete element software MatDEM with high-performance numerical analysis, and the actual mechanical properties of the model samples were predicted using a machine learning method, XGBoost algorithm. A certain amount of sample data sets was generated, and through...

... geologist artificial intelligence geological subdiscipline geology machine learning mathematics of computing reservoir geomechanics borehole conference characteristic highway tunnel environmental science 861 engineering terrain topography subregion tunnel axis tunnel reservoir...

... structural geology mineral geologist artificial intelligence silicate conference deep learning reservoir characterization society iop publishing iop conf feldspar characteristic microscope imaging machine learning reaction equation environmental science 861 specimen correlation...

... not represent the actual situation. With the development of computer technology, the parameter identification method based on machine learning provides a new way to solve the above problems. Aiming at the rheological model based on thermodynamics with internal state variables, a back analysis method...

... at first. The Colorado School of Mines model [4-6] and the Norwegian University of Science and Technology model [7] were the most famous ones to estimate the PR. Statistical models based on mathematical rules, and computational models based on machine learning were developed as well [8]. Generalized linear...

... landslide reservoir characterization geology artificial intelligence knowledge management equation prediction geologist risk management geological subdiscipline conference criteria sedimentary rock rock type machine learning environmental science 861 displacement china society...

... geology geological subdiscipline hongmei south road tunnel artificial intelligence intelligent disaster prevention platform conference tunnel machine learning information environmental science 861 geologist reservoir geomechanics firefighter accident society iop publishing iop conf...