Reservoir characterization and paleo-stratigraphic imaging over Okari Field, Niger Delta, using neural networks

Abstract

Reservoir geometry and internal architecture in the Niger Delta can vary over short distances with rapid lateral and vertical changes in lithology and porosity. Understanding such variations is critical to designing an optimum development strategy for prospects in this basin. It was in order to fully understand the variations in reservoir facies and internal architecture over Okari oil field in the Niger Delta that this study was undertaken. Conventional seismic interpretation, attribute analyses, and subsequent drilling had located a stack of reservoirs in a rollover anticline. To unravel the paleo-stratigraphy of the field and fully populate the field with log properties, we used a multilayered feed-forward neural network (MLFN) to predict shale volume and porosity from seismic and well-log data sets. Earlier, rock physics analyses had been undertaken to understand litho-fluid facies associations in the field and assist in further quantitative interpretation and calibration of neural-network predictions of res-ervoir property distribution.