Brian P. West

Interactive seismic facies classification using textural attributes and neural networks

In this study, we present an application of textural analysis to 3D seismic volumes. Specifically, we combine image textural analysis with a neural network classification to quantitatively map seismic facies in three-dimensional data.

Interactive Seismic Facies Classification of Stack And AVO Data Using Textural Attributes And Neural Networks

We present an interactive method for volume-based seismic facies mapping using seismic textural attributes and probabilistic neural networks. Textural analysis can quantitatively describe many aspects of the classic seismic facies description preformed by the interpreter. Stratigraphically-steered seismic texture is a quantitative, multi-trace (imagebased) attribute that mimics the visual process of the seismic interpreter more effectively than traditional trace-based attribute analyses do. Probabilistic neural networks (PNNs) are parallel implementations of a standard Bayesian classifier that can efficiently perform pattern classification. A primary advantage of the PNN is that it does not require extensive training. In the case of seismic analysis, a reliable seismic facies classification can occur with as little as one example per facies class.

The role of alternative scenarios and a regional geologic framework for subsalt and deep exploration

Over 50 years ago Wallace Pratt, a preeminent geologist with the Humble Oil and Refining Company, had it right when he said “Where oil is first found is in the minds of men…” Today, many minds—of both men and women—are finding exploration opportunities by developing new, innovative exploration concepts based on regional understanding combined with breakthrough imaging technologies.