Three-dimensional seismic interpretation and synthetic modeling of the Atoka and Morrow formations, in the Buffalo Valley Field (Delaware Basin, New Mexico, Chaves County) for reservoir characterization using neural networks

While the advantages of Artificial Neural Networks (ANN) for reservoir characterization are widely known, this project expands such benefits by providing a method by which an ANN can be trained prior to its application to real data. First, a geological and 3D seismic interpretation of the lower Pennsylvanian Atoka-Morrow sequence in the Buffalo Valley Field, New Mexico was executed. Then, to design the ANN and to test its predictions, I generated well-logs and synthetic seismic models for seismic attributes extraction. In order to bridge the vertical resolution gap between well-logs (high resolution) and seismic data (low resolution), VSP data was added as an intermediate step to train the ANN. This results in more effective predictions of density and sonic velocity values of the interval of interest.; A synthetic model based on well-log correlation, seismic interpretation, and regional information provided the data set for the ANN training. The synthetic modeling also guided the 3D seismic interpretation of the Atoka and Morrow Formations. (Abstract shortened by UMI.)...