A Seismic Data Management System should meet certain requirements implied by the nature of seismic data. This kind of data is not solely characterized by alphanumeric attributes but also from a spatial and a temporal dimension (the epicenter and the time of earthquake realization, respectively). Moreover, visualizing areas of interest, monitoring seismicity, finding hidden regularities, and ass...

Gas-hydrates are crystalline form of water and methane that form at high pressure and low temperature. They have attracted the global attention due to their wide-spread occurrences in the permafrost and outer continental margins; potential as major energy resources; role in climate change etc. It is estimated that the carbon in gas-hydrates is two times the carbon content in fossil fuel reserve...

Seismic amplitudes contain important information which can be related to fluid saturation. The amplitude-versus-offset (AVO) analysis of seismic data based on Gassmann’s theory and approximation of the Zoeppritz equations, has played an central role in reservoir characterization. However, this standard technique faces a long standing problem, its inability to distinguish between partial gas and...

SUMMARY A combined space-Fourier representation focused on directional scale analysis is presented. The method leads to a space-log polar frequency distribution. Application to seismic data shows diierentiation in scale and orientation. Attributes are extracted to illustrate this diierentiation.

in data reduction processes through classifications applied to a wide spectrum of aspects—from traffic solutions and medicinal purposes to geophysical interpretations. Here we use an unsupervised approach where the neural network is free to search, to recognize, and to classify structural patterns in an n-dimensional vector field spanning the entire 3D input seismic attribute data set (Taner et...

Unsupervised neural networks such as the Kohonen Self-Organizing Maps (SOM) have been widely used for searching natural clusters in multidimensional and massive data. One example where the data available for analysis can be extremely large is seismic interpretation for hydrocarbon exploration. In order to assist the interpreter in identifying characteristics of interest confined in the seismic ...

Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general a...

Seismic data are used in petroleum exploration to define geologic features in the subsurface. Recent advancements in seismic exploration have examined the effect of fluid and rock properties on seismic attributes. These advancements may provide improved reservoir characterization using techniques examined here. This is accomplished two parts; first, a study of fluid properties and their effect ...

In this paper we present a novel approach to detect salt bodies based on seismic attributes and supervised learning. We report on the use of a machine learning algorithm, Extremely Randomized Trees, to automatically identify and classify salt regions. We have worked with a complex synthetic seismic dataset from phase I model of the SEG Advanced Modeling Corporation (SEAM) that corresponds to de...