... this information directly through conventional methods. Unsupervised machine learning models (Chopra et al., 2019) can represent a new approach to providing accurate interpretation without any reference or labeling by discovering underlying patterns in the data, eliminating human bias. In this study, the Sel...

... mudstone well logging geochemical characterization log analysis reservoir characterization salinity spwla-2024-0042 geological subdiscipline porosity saturation structural geology rock type geochemistry machine learning multiphysic inversion interpretation permittivity sensitivity...

... structural geology geologist well logging evaporite artificial intelligence machine learning sedimentary rock rock type reservoir characterization lithology decision tree learning anhydrite accuracy signature spwla-2024-0028 sylvite polyhalite dataset log analysis th annual...

... efficiency and profitability by reducing time consumption.. drilling operation deep learning geology artificial intelligence orientation algorithm machine learning sinusoid bedding geologist neural network th annual logging symposium borehole enhanced ai-driven automatic dip picking...

... the quality of UDAR measurements by training a machine-learning (ML) algorithm to estimate channel noise levels directly from the raw measurements, thus, avoiding the need for an initial interpretation. To this end, a large dataset was created with raw measurements and noise levels from a wide range...

... structural geology artificial intelligence machine learning correlation algorithm standardization depth matching reservoir characterization spwla-2024-0046 procedure university brazil core analysis th annual logging symposium crossplot geological subdiscipline plug automatic approach spwla...

... and efficient tools to provide and analize these informations. carbonate rock sedimentology reservoir characterization facies sedimentary rock deep learning sedimentary geology geological subdiscipline structural geology rock type machine learning porosity geologist reservoir simulation...

... spwla-2024-0092 spwla 65 discontinuity injectite inversion log analysis machine learning resistivity regularization workflow reservoir mapping SPWLA 65th Annual Logging Symposium, May 18-22, 2024 DOI: 10.30632/SPWLA-2024-0092 HIGH-RESOLUTION 3D RESERVOIR MAPPING AND GEOSTEERING USING VOXEL...

... reservoir characterization estimation dispersion dielectric dispersion pirrone stratigraphy porosity spwla 65 salinity resolution reservoir geomechanics geological subdiscipline permeability estimation spwla-2024-0055 machine learning th annual logging symposium conductivity cec modeling...

... reveals the power of image data analysis in capturing the heterogeneity of the reservoir and the efficiency of machine learning algorithms in rapidly processing large datasets. INTRODUCTION The emergence of digital image analysis (DIA) technologies has revolutionized the way we analyze and interpret data...

... geologist artificial intelligence lwd geology well logging drilling data acquisition shear slowness log analysis machine learning spwla-2024-0078 drilling measurement th annual logging symposium logging while drilling annual logging symposium logging symposium algorithm mlaqi...

...SPWLA 65th Annual Logging Symposium, May 18-22, 2024 DOI: 10.30632/SPWLA-2024-0047 DESCRIBING THE POROSITY OF PRE-SALT CARBONATE ROCKS USING MACHINE LEARNING Gisella Roza Nunes, Gilberto Raitz Junior, Jeferson dos Santos and Leonardo Borghi, Federal University of Rio de Janeiro Copyright 2024, held...

... fracture characterization rock type log analysis geological subdiscipline plane geologist fracture artificial intelligence reservoir characterization extraction clastic rock complex reservoir well logging machine learning th annual logging symposium doi sedimentary rock...

...SPWLA 65rd Annual Logging Symposium, May 18-22, 2024 DOI: 10.30632/SPWLA-2024-0057 WEAK REFLECTION EXTRACTION IN BOREHOLE ACOUSTIC REFLECTION IMAGING USING AN UNSUPERVISED MACHINE LEARNING METHOD Qiang Wang, Hua Wang, Zhilong Fang, University of Electronic Science and Technology of China Danian Xu...

...SPWLA 65th Annual Logging Symposium, May 18-22, 2024 DOI: 10.30632/SPWLA-2024-0077 CASCADED MACHINE LEARNING IN NMR: UNVEILING A CONTINUOUS GRAIN SIZE DISTRIBUTION APPROACH FOR TACKLING SAND PRODUCTION CHALLENGES Muhamad Saiful Hakimi Daud, SLB; Seyed Mehdi Tabatabai, PETRONAS; Fui Kent Wong, SLB...

..., R., Dubourg, V., and Vanderplas, J., 2011, Scikitlearn: Machine Learning in Python, Journal of machine Learning research, 12, 2825-2830. Porwik, P., and Lisowska, A., 2004, The Haar-wavelet Transform in Digital Image Processing: its Status and Achievements, Machine Graphics and Vision, 13(1/2), 79...

... relationships. This permeability model is compared to machine learning (ML) permeability using wireline logs to provide additional data in wells where core is unavailable. Absolute permeabilities are converted to gas effective permeability using unsteady state relative permeability testing. Dynamic permeability...

... inversion methods. sedimentary rock clastic rock geologist structural geology reservoir simulation optimization problem artificial intelligence inversion drilling measurement resistivity log analysis spwla-2024-0071 machine learning spwla 65 university exploration engineering lwd...

... of the correlation between T2 distributions (upper plot) and the corresponding MICP PTS distributions (lower plot) of five carbonate core samples (Kwak et al., 2016). Note that it is often not possible to capture all these non-linear effects in an analytical form. As a universal approximator, machine learning (ML...

... reservoir rock type annual logging symposium saturation acquisition university nuclear magnetic resonance carbonate rock artificial intelligence machine learning spwla-2024-0091 petrofacies interpretation SPWLA 65th Annual Logging Symposium, May 18 22, 2024 DOI: 10.30632/SPWLA-2024-0091...