SEISMIC MULTIATTRIBUTE TRANSFORMS USING NEURAL NETWORK AND GEOSTATISTICAL INVERSION IN RESERVOIR CHARACTERIZATION OF ‘PENNAY’ FIELD, NIGER DELTA

Abstract

The anisotropic and heterogeneous nature of the subsurface units at offset wells gets more complex at locations outside well control. However, qualitative and quantitative predictions of reservoir properties and geometries beyond well control are vital to understanding the intrinsic characteristics of subsurface formations. Supervised Multilayer Perceptron neural network, conditional sequential Gaussian simulation, seismic multi-attribute transforms, and modeling via multivariate statistical analysis were applied to three-dimensional (3D) seismic and a suite of borehole log data set obtained from Pennay field, offshore Niger Delta with a view to predicting lateral continuity of hydrocarbon reservoir properties and quantify the uncertainty associated with the predictions with respect to optimal hydrocarbon extraction. Four horizons corresponding to near top of mapped hydrocarbon bearing sands were used to produce time maps and then depth structural maps using appropriate checkshot data. The post-stack seismic inversion was done and ten attributes were e x t r a c t e d from the seismic sections at 20- 40 ms time-windows covering all the mapped horizons. Petrophysical analysis of the mapped reservoirs revealed that the area is characterized with hydrocarbon saturation ranging from 56 -72 %, water saturation between 27 - 44 %, volume of shale between 7 – 20 % and porosity between 25-31 %. Five major structure building faults were identified. Structural closures identified as rollover anticlines and displayed on the time/depth structure maps suggest probable hydrocarbon accumulation at the upthrown side of the fault F4. Seismic attribute results reveal two main characteristic patterns of high and low amplitude and frequency areas. The Supervised MLPNN simulated volumes for petrophysical properties of interest have their uncertainties quantified and measure of accuracy in prediction in terms of root mean square error (RMSE). Permeability modelled at RMSE of 0.030, revealed some thief zones within and outside the areas with well concentration with average permeability of 635md. MLPNN modelled map of net-to-gross (NTG) at RMSE of 0.0290 revealed that 72% of the reservoirs have a very high NTG with viii average NTG of 0.7184. Effective porosity was modelled at RMS error of 0.0053 with resulting average effective porosity of 0.295. Moreover, hydrocarbon saturation was also modelled at RMSE of 0.0282 with an average of 69.7% and volume of shale was modelled at RMSE of 0.028 and average volume of shale of 9%. The study also revealed that the discovered petroleum reserve resources accumulations in the field for the four-mapped reservoir sand-bodies have a total Proven (1P) reserve resource estimate of 53.005MMBO at P90, 59.013MMBO at 2P/P50 and 65.898MMBO at 3P/P10. In conclusion, successful prediction has been done for petrophysical properties at inter-well points and locations beyond well control and had utilized a new framework that integrate methods of MLPNN and stochastic multivariate geostatistics with the classical seismic post-stack inversion operations to predicting and inferring reservoir properties for subsurface formation in the study area. This has improved our understanding of mapped reservoirs and enhanced lateral prediction of its properties. These prediction tools served better as a complementary tool to each other rather than as a comparism tool.