... geologist rock type sedimentary rock neural network deep learning artificial intelligence classification mechanism geological subdiscipline pixel attention computer vision seismic facies classification machine learning se-resnet block proceedings channel attention image super-resolution...

... exploration of prospecting where borehole information is limited. rock type classification geologist reservoir characterization unsupervised machine geology machine learning grid approach seismic facies classification seismic data seismic facies silhouette score dan ferdinand fernandez...

... the best subset of features that differentiate the desired classes. reservoir characterization seismic facies classification algorithm upstream oil & gas classification spectral decomposition exploration geophysicist applied geoscience artificial intelligence decomposition feature...

... 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...

... his/her selection. To improve the efficiency of training section selection (TSS) and avoid introducing bias from manual screening, this work proposes an automated active learning (AutoAL) workflow for iterative seismic facies classification, which is capable of quantitatively evaluating the machine...

... a nested attention-aware U-Net for liver CT image seg- mentation: IEEE International Conference on Image Processing, 345 349, doi: httpsdoi.org/10.1109/ICIP40778.2020.9190761. Liu, M., M. Jervis, W. Li, and P. Nivlet, 2020, Seismic facies classification using supervised convolutional neural networks...

...Using relative geologic time to constrain seismic facies classification using neural networks Haibin Di*, Zhun Li, and Aria Abubakar, Schlumberger Summary the success of convolutional neural networks (CNNs) in pattern recognition and image processing, it has also been Robust facies classification...

... ABSTRACT Selecting appropriate attributes is a crucial part of the seismic facies classification workflow for reducing computational cost and reasonable model building. For supervised learning, the best attribute subset can be built by selecting input attributes that identify...

...) have been applied to assist interpretation such as salt identification, significant challenges still remain for 3D multi-class seismic facies classification: complex data representation, limited labeled data for training, imbalanced facies class distribution and lack of rigorous performance evaluation...

.... In this study, we present a novel technique called Exhaustive PNN which uses Probabilistic Neural Networks to determine the best suite of seismic attributes to perform a supervised seismic facies classification to differentiate salt from the background seismic response in a Eugene Island seismic survey...

... ABSTRACT We utilized coherence, spectral decomposition, and seismic facies classification to study channel systems and depositional environments during the Pliocene epoch in the offshore area of southwestern Taiwan. The characteristics of channel systems, such as distribution, thickness...

... semantic classes. In this work, we propose an encoder-decoder based Bayesian SegNet architecture for seismic facies classification and introduce the concept of predictive entropy to obtain uncertainty maps. By applying the method to real seismic data with salt and sediment structures, we observe high...

... seismic facies classification generative topographic mapping principal component facies covariance matrix Roy , A. , 2013 , Latent space classification of seismic facies : Ph.D. Dissertation , The University of Oklahoma , 212 . Hampson , D. , J. S. Schuelke , and J...

... 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...

..., I introduce an encoder-decoder CNN model for seismic facies classification, which classifies all samples in a seismic line simultaneously and provides superior seismic facies quality comparing to the traditional patch-based CNN methods. I compare the encoder-decoder model with a traditional patch...

... Start Time: 9:20:00 AM Location: Poster Station 1 Presentation Type: Poster narrow channel black arrow feature representation neural network seismic facies classification annual meeting deep encoder spectral variance feature parameter amplitude spectra upstream oil & gas...

... proposed and successfully applied to computer-assisted seismic facies classification, including k-means, self-organizing mapping, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks. Although well documented in the literature, the terminology...

... Evaluation of uncertainty propagation in seismic facies classification Cheng Yuan *, Jingye Li , Xiaohong Chen, Fangfang Wang State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing) National Engineering Laboratory for Offshore Oil Exploration, China...

... Summary Automated seismic facies classification is an interpretation workflow that provides an efficient means to integrate and analyze large amount of seismic data. The resulting output from the classification process is a single map (or volume) capturing the spatial distribution of seismic...

... artificial intelligence seismic facies resolution reservoir characterization machine learning complete linkage algorithm seismic facies classification procedure information upstream oil & gas Seismic Facies Classification Using 2-D and 3-D Multi-attribute Hierarchical Clustering Algorithms...