POROSITY PREDICTION AND MODELING OF RESERVOIR PROPERTIES OF ‘OSA’ FIELD NIGER DELTA USING ARTIFICIAL NEURAL NETWORK AND GEOSTATISTICS

Abstract

This study was aimed at predicting reservoir porosity with the aid of Artificial Neural Network (ANN) and modeling of reservoir properties vis-à-vis permeability, porosity and water saturation of 'OSA' field in Niger Delta. In mitigating the challenge caused by inaccurate estimation of reservoir porosity from well log measurement due to heterogeneity nature of reservoirs, ANN was employed for its accurate prediction as this technique is independent of inherent uncertainties present in borehole. The data set used comprises base map, 3-D seismic data, suite of well logs for five wells, core porosity data of three wells (Wells 1, 2 and 4) and check-shot survey information of ‘OSA’ field. Well logs data (sonic, resistivity and density) within the reservoir of interest were selected as input variables of the supervised network while core porosity data of Well 1 was set as its target and trained with MATLAB software based Artificial Neural Network (ANN) using Levernberg-Marquadt algorithm at epoch 10. The input data were randomly divided into 75% of training, 15% of validation and 15% of testing while the mean squared error and validation regression obtained after the training were 0.000268816 and 0.999996 respectively. The produced artificial intelligence script was run to predict porosity (true porosity) of wells 2, 3, 4 and 5. Detailed petrophysical analysis was also carried out on the well logs using empirical equations to estimate the reservoir properties for the purpose of modeling. Structural and property modeling i.e. effective porosity, permeability, water saturation and ANN predicted porosity of reservoir (RES 1) were distributed stochastically within the constructed 3D framework using Sequential Gaussian Simulation (SGS). For reservoir (RES 1), effective porosity ranges between 4% and 37% while ANN predicted porosity is from 14% to 38%, permeability (8900mD - 33000mD) and water saturation (14% - 34%). At validation, a very good match was obtained between the ANN predicted porosities and core porosity data i.e. average deviation error for well 2 and 4 are 0.019 and 0.095 respectively. This suggests that the ANN predicted porosity model represent the true porosity distribution of reservoir (RES 1). The result of this study has been able to locate two new hydrocarbon prospect areas in ‘OSA’ field.