Seismic multi-attribute analysis for petrophysics reservoir prediction with probabilistic neural network in “FA” field

Abstract

Oil and gas reserves are increasingly difficult to find due to more complex geological conditions. This complex condition causes difficulties in determining reservoir distribution. Therefore, a better method is needed to overcome these complex geological conditions. In this study, the petrophysics analysis by using the multi-attribute and the Probabilistic Neural Network (PNN) used to make reservoir distribution model on seismic horizontal slice. This multi-attribute method and Probabilistic Neural Network (PNN) that can search for correlation between seismic attributes and the data sought, for the prediction of property values from surrounding rocks. From this method, the distribution of porosity data with a correlation value of 0.52 was generated, water saturation with a correlation value of 0.73, and shale content with a correlation value of 0.58. Where the combination of porosity data, water saturation, shale content, and acoustic impedance (AI) data of inversion results can be a clue to identify reservoir distribution. From the porosity and saturation values, hydrocarbon dispersion can be made, wherein this study values were obtained between 0.01 0.03. This “FA” field has a reservoir between wells F-06, FA-05, FA-15, and FA-18 and spreads westward from wells FA-05, FA-15 & FA-18. The distribution of petrophysical parameters generated from the validation of well data using the multi-attribute method. This thing prove that Multi-attribute and neural network analysis can be used to determine predictions of porosity, water saturation, and shale content well and can be used for reservoir characterization.