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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPWLA 28th Formation Evaluation Symposium of Japan, September 13–14, 2023
Paper Number: SPWLA-JFES-2023-T
... environment due to economic factors. This requires an innovative out-of-box solution to close the gap and narrow down the uncertainty in petrophysical evaluation. This paper will discuss few case studies utilizing artificial intelligence (AI) and machine learning (ML) workflow in petrophysical evaluation...
Proceedings Papers

Paper presented at the SPWLA 28th Formation Evaluation Symposium of Japan, September 13–14, 2023
Paper Number: SPWLA-JFES-2023-J
... 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) to compare the result with FaciesSpect. These methods have already used commercially...
Proceedings Papers

Paper presented at the SPWLA 28th Formation Evaluation Symposium of Japan, September 13–14, 2023
Paper Number: SPWLA-JFES-2023-U
... 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...
Proceedings Papers

Paper presented at the SPWLA 28th Formation Evaluation Symposium of Japan, September 13–14, 2023
Paper Number: SPWLA-JFES-2023-E
.... 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...
Proceedings Papers

Paper presented at the SPWLA 28th Formation Evaluation Symposium of Japan, September 13–14, 2023
Paper Number: SPWLA-JFES-2023-S
.... 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...
Proceedings Papers

Paper presented at the SPWLA 28th Formation Evaluation Symposium of Japan, September 13–14, 2023
Paper Number: SPWLA-JFES-2023-F
... artificial intelligence effectiveness dolomite evaluation machine learning carbonate rock rock type reservoir characterization calibration test section vug resistivity reservoir effectiveness fracture core calibration effectiveness evaluation 28th formation evaluation symposium dissolution...
Proceedings Papers

Paper presented at the SPWLA 27th Formation Evaluation Symposium of Japan, September 14–15, 2022
Paper Number: SPWLA-JFES-2022-K
... color spaces and the ruggedness index GTRI. 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 GTRI data as the supervisory data, explanatory variables, and the objective function...
Proceedings Papers

Paper presented at the SPWLA 26th Formation Evaluation Symposium of Japan, September 30–October 7, 2021
Paper Number: SPWLA-JFES-2021-E
... 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...
Proceedings Papers

Paper presented at the SPWLA 26th Formation Evaluation Symposium of Japan, September 30–October 7, 2021
Paper Number: SPWLA-JFES-2021-D
... years, an increasing number of studies have applied deep machine learning to reservoir modeling using INTRODUCTION a method called GAN. GAN is characterized by its ability to learn various features common to the training data and For reliable field development and reservoir management, to generate...
Proceedings Papers

Paper presented at the SPWLA 25th Formation Evaluation Symposium of Japan, September 25–26, 2019
Paper Number: SPWLA-JFES-2019-Q
... 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...
Proceedings Papers

Paper presented at the SPWLA 24th Formation Evaluation Symposium of Japan, October 11–12, 2018
Paper Number: SPWLA-JFES-2018-M
... 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...
Proceedings Papers

Paper presented at the SPWLA 24th Formation Evaluation Symposium of Japan, October 11–12, 2018
Paper Number: SPWLA-JFES-2018-E
... 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...
Proceedings Papers

Paper presented at the SPWLA 23rd Formation Evaluation Symposium of Japan, October 11–12, 2017
Paper Number: SPWLA-JFES-2017-T
...; 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...
Proceedings Papers

Paper presented at the SPWLA 23rd Formation Evaluation Symposium of Japan, October 11–12, 2017
Paper Number: SPWLA-JFES-2017-O
... 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...
Proceedings Papers

Paper presented at the SPWLA 23rd Formation Evaluation Symposium of Japan, October 11–12, 2017
Paper Number: SPWLA-JFES-2017-P
..., 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...
Proceedings Papers

Paper presented at the SPWLA 23rd Formation Evaluation Symposium of Japan, October 11–12, 2017
Paper Number: SPWLA-JFES-2017-X
... 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...
Proceedings Papers

Paper presented at the SPWLA 23rd Formation Evaluation Symposium of Japan, October 11–12, 2017
Paper Number: SPWLA-JFES-2017-AA
... 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...

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