Seismic Classification -- A Case Study in the Niger Delta

By

 Eugene A. Luzietti¹, Ken Hauser², Goke Adeniyi²

(1) Chevron Nigeria Limited, N/A, Nigeria (2) Schlumberger GeoQuest, N/A,

 The effective utilization of available data to reduce risk, improve cycle time, and increase reserves is a major challenge in today’s E&P industry. The extraction and exploitation of seismic attribute information provides an innovative means of creating supplementary data sets that can be accessed and interpreted. Seismic classification using neural networks to segregate facies and identify potential hydrocarbon bearing zones based upon the analysis of extracted attributes is a modern approach to interpretation and data utilization. Seismic classifications can be unsupervised - made using extracted attributes only - or they can be supervised - using existing well information to train the neural network as part of the classification scheme. Using information from seismic attribute extractions in combination with ‘ground truth’ information from the drill bit, these new classification algorithms provide a unique opportunity for the interpreter to enhance their understanding of the subsurface and to reduce the risk associated with oil exploitation. In this study, information from ten wells was combined with 3D seismic interpretation data (extracted seismic attributes) over a shelf/slope environment to investigate and understand the uncertainty associated with amplitude anomalies in the prospect area. The resultant facies classification yielded valuable information, which was used to guide the placement of a future well. Although the location has not yet been tested, the approach affords us a unique opportunity to utilize available information in a new way to achieve a better understanding of potential reservoir targets