... in facies, texture, and in-situ stress gradient. The study utilizes traditional machine learning and emerging deep learning algorithms, such as Random Forest (RF) and Bi-directional Long Short-Term Memory (BiLSTM) for modeling. ML-based regressor models are optimized for each cluster and aggregated together...

... Seismic reservoir characterization workflows such as rock properties prediction are highly dependent on and limited by the quality and resolution of the input data. In this study, a multi-attribute Random Forest machine learning analysis applied to spectrally extrapolated seismic data...

...A Random Forest regressor based uncertainty quantification of porosity estimation from multiple seismic attributes Caifeng Zou*, Luanxiao Zhao, Minghui Xu, Yuanyuan Chen, and Jianhua Geng, School of Ocean and Earth Science, Tongji University Summary representatives of ensemble learning, Random...

... exposition seismic dataset experiment annual meeting selection algorithm stratigraphic segmentation segmentation classification anova random forest interpretation reservoir characterization marfurt machine learning feature agglomeration upstream oil & gas artificial intelligence...

... correlation and random forest is used to constrain the simulation based on secondary sources of data. An important output of this approach is the uncertainty quantification which aids the user in evaluating the simulation. In addition, the method also computes the feature importance of different secondary...

... The use of machine learning algorithms have become more commonplace throughout different industries in recent years. Geoscientists have had some success in implementing machine learning algorithms to automate seismic facies classification. In this study, we use a random forest learning...