.... sedimentary rock upstream oil & gas reservoir geology rock type log analysis complex reservoir artificial intelligence geologist asia government china government porosity tight carbonate reservoir chloride concentration machine learning water saturation fluid identification chlorine...

... government geologist reservoir sedimentary rock structural geology china government artificial intelligence effectiveness dolomite evaluation machine learning carbonate rock rock type reservoir characterization calibration test section vug resistivity reservoir effectiveness fracture core...

...’ distribution trend was consistent with cuttings and core lithologies which composed of the three major lithology zones: Dunite, Gabbro, and Harzburgite. Another two automatic facies analysis methods were also attempted which are Class-Based Machine Learning (CBML)and Heterogeneous Rock Analysis (HRA...

.... upstream oil & gas solver core analysis geology artificial intelligence machine learning japan government mineral log analysis 28th formation evaluation symposium numerical solver multi-salinity analysis synthetic data equation ffri measurement geologist asia government well logging...

... will discuss few case studies utilizing artificial intelligence (AI) and machine learning (ML) workflow in petrophysical evaluation based on the existing legacy data set and the newly acquired cased hole pulse neutron spectroscopy data. Some best practices in data training, model prediction and validation...

... Introduction This paper presents the methodology used to deliver a consistent set of rock types, with petrophysical outputs of porosity, water saturation, and permeability, using a class-based machine learning (CbML) method. This novel tool is designed for summarizing the unique...

... 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...