WebABSTRACT Subsurface petrophysical properties usually differ between different reservoirs, which affects lithology identification, especially for unconventional reservoirs. Thus, the lithology identification of subsurface reservoirs is a challenging task. Machine learning can be regarded as an effective method for using existing data for lithology prediction. By … WebWe developed a new neural network-based methodology called democratic neural network association (DNNA). The DNNA method was trained using lithology logs from wells simultaneously with prestack seismic data. This technique, using a probabilistic approach, aims to find patterns in seismic that will predict lithology distribution and uncertainty.
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WebIn this paper, we aim to define the most effective machine learning techniques for well log-based determination of lithology on the example of oil field in western Siberia, Russia. … WebExtended elastic impedance process for fluid and lithology prediction. Using the EEI correlation plot you can perform an extensive scan of the Chi projection angle to find the best match with the target property, in this … retirement homes daytona beach florida
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WebPermeability Prediction Based on the Logging Data of Gas Hydrate Reservoir by Using Machine Learning Method *CHAO XU1, Hitoshi Tomaru1 1. ... distribution of hydrate saturation under lithology control, and the permeability predicted by artificial neural network not only reflects the actual formation lithology variation more precisely, ... Web17 mei 2024 · Lithology identification is a task of great significance in reservoir characterization for petroleum exploration and engineering [].It is the basis for reservoir … Web22 sep. 2024 · Abstract: Prediction of lithology/fluid (LF) characteristics is always the bottleneck problem and difficulty of reservoir characterization. Deep-learning-based data … retirement homes for sale in north tyneside