... dimensional (2D) influences on the inversion results are identified via data misfit and azimuthal signal differences from a range of frequencies. Where this difference is significant, a hybrid 2D inversion is applied leveraging artificial intelligence methods like Artificial Neural Network (ANN) and a hybrid...

.... Geophysical Prospecting, 68 (9): 2697 2711. 9 SPWLA-2024-0029 structural geology borehole th annual logging symposium artificial intelligence reservoir characterization sitv-fwi log analysis application experiment inversion bri reconstruct artifact noise geologist well logging geophysics...

... logging data analysis and mode decomposition. Hao Chen received his bachelor's degree in geophysics. He is currently a senior engineer at China National Logging Corporation. Mainly engaged in comprehensive research on petrophysics, well logging and geology. SPWLA-2024-0059 artificial intelligence...

... heterogeneity. It also enables real-time decisions on the optimum locations of core samples for laboratory measurements to enhance reliable petrophysical evaluation and reservoir characterization at optimum cost. structural geology geologist artificial intelligence clastic rock well logging log...

... be logically extended to conventional reservoir evaluation and Carbon Capture and Storage (CCS) applications, broadening their applicability beyond unconventional hydrocarbon reservoirs. sedimentary rock complex reservoir inversion geologist mineral artificial intelligence clastic rock mudrock...

... 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...

.... (2024), referred to as real-time Fluid Monitoring and Classification (FMC). This method combines machine learning techniques with artificial intelligence, utilizing a spectral database and conducting data analysis in the eigen-space of spectral data. It enables discrimination between hydrocarbon and non...

... the noise model in the final real-world scenario tests. spwla-2024-0024 geology neural network log analysis well logging drilling data acquisition geologist artificial intelligence drilling measurement machine learning frequency algorithm attenuation prediction resistivity inversion...

... 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...

... artificial intelligence th annual logging symposium pore spwla 65 dispersion graph texture characteristic correlation variation matrix spwla-2024-0030 interna electrofacies SPWLA 65th Annual Logging Symposium, May 18-22, 2024 DOI: 10.30632/SPWLA-2024-0030 EVALUATION OF GENETIC AND GEOMETRIC...

... 65 geologist artificial intelligence saturation neutron decay spectrum salinity between-class variance detector pnn SPWLA 65th Annual Logging Symposium, May 18-22, 2024 DOI: 10.30632/SPWLA-2024-0103 BOREHOLE EFFECT CORRECTION IN PULSED NEUTRON-NEUTRON LOGGING FOR FORMATION CAPTURE CROSS...

.... A., De Almeida Waldmann, A. T., and Fioriti, L. D. S., 2018. Artificial intelligence use to predict severe fluid losses in pre-salt carbonates. In SPWLA 59th Annual Logging Symposium. Society of Petrophysicists and Well-Log Analysts. Symbols avg h P hi So Average Height Porosity Oil saturation...

.... drilling operation geologist geology lwd artificial intelligence drilling measurement logging while drilling drilling data acquisition real time system wellbore th annual logging symposium trajectory dimension sensitivity reconstruction accuracy well logging reservoir characterization...

... string result in less signals received by detectors installed on the density tool. This creates a unique challenge for accurate density measurement. geology modeling & simulation mineral artificial intelligence geologist drilling fluids and materials well logging drilling fluid...

... environments. Given the high vertical resolution of the DDL tool, it is one of the few methods suitable for thin-layered reservoirs. permeability geologist flow in porous media sedimentary rock geology well logging clastic rock artificial intelligence fluid dynamics rock type log analysis...

... 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...

... in the fields of artificial intelligence and machine learning, defines machine learning as the field of study that gives computers the ability to learn without being explicitly programmed (Mahesh, 2020). Pore space in sedimentary rocks is a crucial factor in reservoir characterization, hence, methods...