... Summary In this study, we show an application of estimating total organic carbon (TOC) in a Barnett Shale play from the widely available triple combo logs using support vector machine (SVM). Being a nonlinear supervised learning technique, SVM provides superior estimation than the traditional...

... al., 2005). permeability value annual international meeting log analysis svm kernel function upstream oil & gas machine learning feature space permeability training data matrix support vector machine relation artificial intelligence transformation dimensional space support...

...Seismic attributes selection based on SVM for hydrocarbon reservoir prediction Zhang Chang-kai*, Lu Wen-kai, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University. Summary...

.... Support Vector Machines (SVMs) are computer learning algorithms that can be used to emulate non-linear geophysical inversion. SVMs depend on making multiple comparisons between examples of input data. These comparisons can be made in parallel on the GPU, significantly speeding up the algorithm. GPUs only...

... trained an e-insensitive Support Vector Machine (SVM) to regress the water saturation from seismic data. Validations of SVM regression on the gas reservoir and a prospect of low saturation gas suggest that it is possible to distinguish between commercial gas and low saturation gas at deep water reservoirs...

... that, in order to use a computer learning algorithm such as a Support Vector Machine (SVM) to interpret data, it is necessary to train the SVM with a number of examples that is on the order of the number of model parameters. Actually, the SVM can to be trained with much smaller training set, the size of which...

... at the same model from different paradigms. Linear inversion finds a model by minimizing a least squares objective function to which there is a closed form solution. NNs and SVMs use training data to approximate a functional inverse. If the relationship between models and data is non-linear...

... ABSTRACT We present a new classification technique to recognize and predict reservoirs from seismic data using support vector machine (SVM) pattern recognition. As the method is data-driven it is especially suitable for use with non-linear multiattributes. The method has good generalization...

.... Inversion of the non-linear Zoeppritz equations is an ill-posed problem. The results of an inversion depend heavily on the a priori assumptions used to regularize it. If an SVM is trained using data that contains the same assumptions, it will get the same answer. It captures non-linear relationships...

... Summary A Support Vector Machine (SVM) is used to approximate an inverse to the Zoeppritz equations. It is tested on an AVO data set from the Gulf of Mexico which includes a gas hydrate layer. Physically realistic examples of velocity and density contrasts from the region are used to train...