Estimation of reservoir properties using pre-stack seismic inversion and neural network in mature oil field, Upper Assam basin, India

Abstract

The mature oil fields require comprehensive characterization for enhanced hydrocarbon production, and subsequently demands estimation of reservoir properties. The key properties viz. volume of clay, effective-porosity, hydrocarbon-saturation has been evaluated for an aging Oligocene reservoir of Upper Assam basin, located in northeastern India from seismic and well log data. Elastic properties (acoustic and shear impedance) and density are derived from pre-stack inversion of 3D seismic data. These elastic properties are analyzed for their sensitivity for discrimination of lithology and fluid-content, and many derived attributes are computed from elastic properties. These attributes are assessed for their predictability to predict the target reservoir properties using multi-attribute analysis. For each of the target property neural network is trained with the most predictable attributes, and multi-dimensional, non-linear neural network models are created using multilayered feed forward neural network (MLFN), followed by Probabilistic neural network (PNN). The specific neural network models for each target property are employed for quantitative estimate of volume of clay, effective-porosity, hydrocarbon-saturation in inter-well regions. The estimated properties leverage the identification of untapped oil reserves and provide promising opportunity for enhanced production through drilling of infill wells.