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.

Seismic Paleo-Geomorphic System of the Extensional Province of the Niger Delta: An Example of the Okari Field

“…It was understood that the variability of subsurface facies was more intricate than initially presumed. This is a known situation in the Niger Delta, the fast rate of sedimentation along the shelf edge and strong wave action are believed to have ensured rapid spacial variation in subsurface facies distribution patterns (Aminu and Olorunniwo, 2011). There was therefore a need to fully image the distribution of reservoir facies within the field to assist with further development and production.…”

Application of spectral decomposition and neural networks to characterise deep turbidite systems in the outer fold and thrust belt of the Niger Delta

“…Spectral decomposition and artificial NNs are two technologies routinely applied in interpreting seismic data (Castagna, Sun and Siegfried 2003;Zhang 2008;Aminu and Olorunniwo 2011). The attraction of spectral decomposition lies in its ability to isolate the amplitude response of individual subsurface elements frequently beyond the resolution of conventional seismic data.…”

“…The attraction of spectral decomposition lies in its ability to isolate the amplitude response of individual subsurface elements frequently beyond the resolution of conventional seismic data. NNs, on the other hand, have the ability to exploit subtle correlations and process multiparameter multi-dimensional inputs in a complex non-linear fashion (Liu and Liu 1998;Aminu and Olorunniwo 2012) and allow the prediction of key petro-facies parameters for subsurface systems from seismic and well-log datasets (Banchs and Michelena 2002;Calderon and Castagna 2005;Aminu and Olorunniwo 2011;Ray, Sharma and Chopra 2014). NNs, on the other hand, have the ability to exploit subtle correlations and process multiparameter multi-dimensional inputs in a complex non-linear fashion (Liu and Liu 1998;Aminu and Olorunniwo 2012) and allow the prediction of key petro-facies parameters for subsurface systems from seismic and well-log datasets (Banchs and Michelena 2002;Calderon and Castagna 2005;Aminu and Olorunniwo 2011;Ray, Sharma and Chopra 2014).…”

“…Three-dimensional seismic data have found immense applications in subsurface imaging and characterisation (Aminu and Olorunniwo 2011;Manzi et al 2012;Dunlap, Wood and Moscardelli 2013). The most important application of 3D seismic data by expenditure size is hydrocarbon exploration.…”

“…It allows the localisation of individual events in frequency space and enables the delineation of both the geometry and internal architecture of sediment transport systems from seismic data (Portniaguine and Castagna 2005;Liu and Marfurt 2007;Blumentritt 2008;Harilal et al 2009;Davogustto et al 2013). NNs, on the other hand, have the ability to exploit subtle correlations and process multiparameter multi-dimensional inputs in a complex non-linear fashion (Liu and Liu 1998;Aminu and Olorunniwo 2012) and allow the prediction of key petro-facies parameters for subsurface systems from seismic and well-log datasets (Banchs and Michelena 2002;Calderon and Castagna 2005;Aminu and Olorunniwo 2011;Ray, Sharma and Chopra 2014). The application of these technologies frequently provides considerable insight into the properties and distribution of subsurface geologic features, as well as the structural and depositional mechanisms that have led thereto.…”