... in Triassic Chang 8 Formation, in Ansai Region, eastern Ordos Basin, a method of constructing pseudo capillary pressure from conventional well logging data based on machine learning method was proposed. Based on the analysis of the morphological feature of mercury injection capillary pressure (MICP) from core...

... of TRI. We examined the correlation between HSV color spaces and the ruggedness index G TRI . Third, we examined the use of a machine learning model to predict the ruggedness of the orthoimage in the Oga Coast, with the Itoshima Coast data, HSV color space data, and G TRI data as the supervisory data...

...-2 to draw the multiple images of facies distribution constrained by well data using Generative Adversarial Network (GAN). reservoir characterization flow in porous media machine learning fluid dynamics deep learning 26th formation evaluation symposium estimation result sedimentary...

... The 26th Formation Evaluation Symposium of Japan 30th September, 1st October, 7th October 2021 MACHINE LEARNING TO PREDICT LARGE PORES AND PERMEABILITY IN CARBONATE RESERVOIRS USING STANDARD LOGS Ibrahim B. Milad1 and Russell G. Farmer 2 1. BP Senior Petrophysicist 2. BP Petrophysics Discipline...

... machine learning classification pretest interpretation reservoir permeability evaluation intrinsic permeability permeability estimation mobility porosity lithofacies Classification lithofacies Japan Fluid Dynamics evaluation effective permeability 25th formation evaluation symposium...

... locations. The comparison was attained with respect to the variance estimation through the cross-validation procedure. It was concluded that Bayesian Kriging is more accurate prediction of formation permeability than the universal Kriging. geologic modeling geological modeling machine learning...

... Intelligence Upstream Oil & Gas machine learning optimization problem oil phase evolutionary algorithm reservoir simulation ilh objective function three-phase relative permeability relative permeability water saturation flow in porous media Fluid Dynamics 24th formation evaluation symposium...

... and summarized characteristics of logging curves and response values could provide a basis for lithology identification. The machine learning approach (SVM) can improve the accuracy of lithological identification. well logging Mineral Content Reservoir Characterization structural geology basement rock...

...; EDX Plate E.37B) where fracture are also found within clay matrix. machine learning log analysis complex reservoir Formation Evaluation Symposium porosity lower baong formation Artificial Intelligence belumai formation organic-rich shale baseline well logging Upstream Oil & Gas...

... and petrophysics for both conventional and unconventional oil/gas resources. geological modeling Bayesian Inference geologic modeling Artificial Intelligence Petrel machine learning Upstream Oil & Gas Formation Evaluation Symposium estimation result estimation multiple soft data weighting factor...

..., October 11-12, 2017 -10- Figure 19: Cumulative oil production (RBF) Figure 20: Cumulative oil production (RBF) Figure 21: Cumulative oil production (CMGDECE) Figure 22: Sampling distributions (MC simulation) Figure 23: Forecast with uncertainty for cumulative oil (MC Simulation) machine learning risk...

... in Japan. Ryoichi Matsui is a geologist at INPEX Technical Research Center. He received his B.Sc. and M.Sc. in geology from Kyoto University in Japan. Artificial Intelligence Reservoir Characterization feasibility grain size Formation Evaluation Symposium quartz cement machine learning ichthy...

... for the target formations, which are Chikubetsu formation and Sankebetsu formation. These formations are commonly observed onshore outcrop in alternating beds of mudstone, siltstone, tuff and sandstone in Eocene to Miocene age. log analysis Formation Evaluation Symposium machine learning Upstream Oil...

... for lithofacies classification and permeability modeling through advanced machine learning algorithms, Journal of Petroleum Exploration and Production Technology, doi:10.1007/s13202-017-0360-0 Al-Mudhafar, W. J., 2016a, Integrating Probabilistic Neural Networks and Generalized Boosted Regression Modeling...

... by the relative hypocenter T hydraulic fracturing hypocenter location machine learning Upstream Oil & Gas microseismic event Artificial Intelligence september 29 22nd formation evaluation symposium determination receiver multiplet event Reservoir Characterization microseismic monitoring...

... by the original core, as well as on neighbouring wells, where no core data was available. machine learning Reservoir Characterization Artificial Intelligence textural class well logging Fluid Dynamics flow in porous media upper section prediction log analysis Upstream Oil & Gas Rabiller...

... to determine TOC by conventional log measurements using statistical and artificial intelligence algorithms. well logging Engineering coefficient Artificial Intelligence shale shale formation TOC content organic-deficient shale machine learning Upstream Oil & Gas China University Passey...

... of realizations for the selection of superior realizations, which contributed to the dramatic reduction of CPU-time by Program-2. machine learning Engineering unsampled location evolutionary algorithm objective function template Upstream Oil & Gas optimization Artificial Intelligence selection...

...% or not. The predicted permeability from each algorithm in each case have been outlined, depicted, and discussed for its compatible with the measured values. Reservoir Characterization Fluid Dynamics core permeability flow in porous media 20th formation evaluation symposium machine learning Upstream Oil...

.... machine learning Artificial Intelligence 20th formation evaluation symposium october 1 Engineering porosity test data petrographic data University image analysis intelligent system Upstream Oil & Gas prediction algorithm neural network correlation coefficient fuzzy logic Reservoir...