Estimation of porosity from well logs and seismic using artificial neural network

Abstract

The Artificial Neural Network (ANN) is widely used to map and estimate reservoir properties. Since ANN has the ability of non-linear computing and self-error correction, it serves as an alternative method to enhance reservoir characterization by improving reservoir properties' prediction. This study employed an integrated approach where seismic, and well log data are used alongside ANN to evaluate petrophysical properties in Field X, South Caspian Basin. The study field is situated in the South Caspian Basin, which developed during Tertiary-Quaternary. The South Caspian Basin covers the southern part of the Caspian Basin, coastal regions of eastern Azerbaijan, northern Iran, and western Turkmenistan. The field is geologically situated on an elongated and multi-crest, anticlinal feature, better known as Apsheron Sill. Available data set consists of log data from four wells: X1, X2, X3, X4, and 3D pre-stack seismic data. Firstly, the reservoir properties were calculated from the well log data. The simultaneous inversion was carried out to obtain elastic rock properties like the P-impedance, S-impedance, density. After that, elastic properties were used to obtain 3D dimensional SQp and SQs attributes. Furthermore, the Radial basis function Neural Network (RBFN) model was created with optional inputs: volume of P-impedance, S-impedance, density, SQp, SQs and property logs and outputs: porosity. Moreover, this research work demonstrates an application of SQp and SQs attributes for properties prediction using neural network. The neural network's primary purpose is to determine a relationship between obtained volumes and reservoir parameters, porosity at well locations. The RBFN model is successfully trained and validated on the field data. The results demonstrated an excellent correlation between actual property logs. They predicted properties, which gives confidence for spatial prediction in areas where well logs are not available.