3-D static modelling of lateral heterogeneity using geostatistics and artificial neural network in reservoir characterisation of "P" field, Niger Delta. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Fajana, Akindeji Opeyemi
• Is Part Of: NRIAG journal of astronomy and geophysics. Volume 9:Issue 1(2020)
• Page Start: 129
• Page End: 154
• Publication Date: 2020-01-01
• Subjects: Hydrocarbon -- inversion -- uncertainties -- porosity -- reserves; Astronomy -- Periodicals; Geophysics -- Periodicals; Astronomy; Geophysics; Electronic journals; Electronic journals; Periodicals; 520.5
• DOI: 10.1080/20909977.2020.1727674
• Languages: English
• ISSNs: 2090-9977

Abstract

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 Multi-layer Perceptron neural network and conditional sequential Gaussian simulation were applied to a suite of well logs data set and three dimensional (3-D) seismic dataset acquired from P-field, Niger Delta to predict lateral continuity of hydrocarbon reservoir properties. Multilayer perceptron neural network was used to model effective porosity (Eϕ) at the root-mean-square error of 0.00531 with an estimated average effective porosity of 0.2952. Furthermore, MLPNN modelling of hydrocarbon saturation (Sh ) at root mean square error of 0.02821 yielded an estimated average of 69.73%. The volume of shale (Vsh ) was also modelled at the root mean square error of 0.0282, with an estimated Vsh average of 9% for the study area. It was discovered that a comparatively higher net-to-gross (N-T-G), relatively higher effective porosity, high hydrocarbon saturation, very low volume of shale, and high permeability were observed in the areas of interest in the study area. A geostatistical approach was also used to model petrophysical properties. The parametric semivariogram model shows the range of 94–5230 m, nugget effect of 0.062, and sills of 0.075, 0.093, and 0.0121. Realisations were generated and ranked using the SGS algorithm suggest that any one of the realisations can independently represent the real picture of the subsurface geology within the study area. The integration of these different prediction tools and analyses of the outcomes from the research has improved our understanding of delineated reservoirs and improved lateral variation prediction of its properties. These prediction tools served better as a complementary tool to each other rather than as a comparison tool.