... Utilizing multiple seismic amplitude attributes as features in various machine learning techniques has enhanced reservoir quality and lithofacies classification. However, the underlying limitations of resolution of thin lithofacies and the associated wavelet interference may adversely impact...

... The paper introduces an integrated machine learning-based fault classification workflow that creates fault component classification volumes that greatly reduces the burden on the human interpreter. We first compute a 3D fault probability volume from pre-conditioned seismic amplitude data using...

..., subjective and non-repeatable. Thus, we propose to train an end-to-end deep neural network (DNN) to identify the facies of the carbonate rocks from acoustic image logs and gamma ray logs. The well-trained DNN generalizes well to the test and uncertain sections. Compared with manual classification, the DNN...

... spectral coefficient applied geoscience energy 10 upstream oil & gas machine learning exploration geophysicist reflection coefficient classification artificial intelligence matrix international china university society correspond fanchang zhang east china nanying lan information...

... for stratigraphic classification. SOM utilizes attribute space to classify data into natural clusters which are ultimately represented by winning neurons in a 3D classification volume in survey space. Results yielded an excellent match with existing facies models and in some cases have revealed higher resolution...

... and stratigraphic attributes to identify the main reservoir seismic patterns, and a multiattribute seismic facies classification, based on K-means and hierarchical clustering algorithms, to map the distribution of these patterns and structural features within those reservoirs. As a result, we could evaluate...

... and have achieved state of the art classification accuracy of 94.5%. Automating the prediction along with achieving a higher accuracy opens up the possibility to create similar end-to-end solutions for problems in seismic, well-logging and interpretation domains. structural geology deep learning...

... mountainous seismic acquisition efficiency american association international operation infrastructure upstream oil & gas seismic acquisition obstacle classification dom exploration geophysicist applied geoscience society Application of LiDAR in mountainous seismic acquisition in the south...

... & gas seismic model inversion neural network reservoir characterization applied geoscience geophysics dnn exploration geophysicist energy 10 noise full-waveform inversion expanded abstract fwi society waveform inversion american association classification application Estimate near...

... government exploration geophysicist asia government co 2 das geophone upstream oil & gas reservoir characterization houston research center summary imaging american association public information sequestration weichang li aramco america classification applied geoscience international...

..., reduce skills required to execute the tasks and hence improved data quality and consistency to unlock new answer products. drilling data acquisition classification revolution upstream oil & gas reservoir characterization workflow artificial intelligence machine learning particle...

... in the full resolution image. Finally, neural networks trained on Dunham rock types show better interpretability than the pore type dataset. upstream oil & gas machine learning texture representation artificial intelligence baechle interpretation deep learning classification...

... Innovative interpretation methods based on efficient measurements are the key to establishing accurate “geological-geophysical” mappings. The multi-solvability of petrophysical responses leads to the complex non-linear relationship between multi-scale geophysical data and the classification...

... and development of four such NLP based applications which may interest the O&G community. We discuss attribute extraction, semantic information retrieval, natural language query translation and technical document classification. application machine learning exploration geophysicist upstream oil...

... photomicrograph american association classification Multiscale pore system characterization utilizing electron and light microscopy; Middle/Upper Jurassic Najmah Formation, West Kuwait Rasha Al-Morakhi, Naveen Verma, and Kim Long Nguyen, Kuwait Oil Company; Nick Nelson*, Estefania Endara, Dawn Hayes, Harry...

... of features are used to classify seismic facies of a fluvial reservoir in the Malay Basin, offshore Malaysia. We find that the subset of spectral components gives a better classification result than the whole set. The sequential forward selector and the embedded selector of random forest algorithms provide...

... oil & gas machine learning explainable machine learning logistic regression classifier classification prospect alregib datapoint interpretation american association applied geoscience neural network explanation dataset international exploration geophysicist alfarraj Explainable...

... applications: Computers and Geosciences, 85, 142 154, doi: httpsdoi.org/10.1016/j.cageo.2015.09.015. Giraud, J., M. Lindsay, M. Jessell, and V. Ogarko, 2020, Towards plausible lithological classification from geophysical inversion: Honouring geo- logical principles in subsurface imaging: Solid Earth, 11, 419...

... FORCE: Machine Predicted Lithology was a challenging facies classification contest using well logs from the North Sea’s Norwegian coast. As we built different machine learning workflows to predict the lithofacies best, we encountered a few interesting challenges. The first involved merging...

... proposed method. (c) Uncertainty in prediction using proposed method. © 2021 Society of Exploration Geophysicists 10.1190/segam2021-3583072.1 SEG International Exposition and 90th Annual Meeting Page 1423 REFERENCES Coléou, T., M. Poupon, and K. Azbel, 2003, Unsupervised seismic facies classification...