Body Wave Velocities Estimation From Wireline Log Data Utilizing an Artificial Neural Network for a Carbonate Reservoir, South Iran

Abstract

Reservoir characterization is a prerequisite study for oil and gas field development. Body wave velocities are important parameters for reservoir characterization studies. In this research, a back-propagation artificial neural network (BP-ANN) including the Levenberg-Marquardt training algorithm was used as an intelligent tool to estimate compressional and shear wave velocities. The efficiency of utilizing density log and photoelectric effect (PEF) in improving estimation accuracy have been evaluated as well. The petrophysical data from three wells were used for constructing intelligent models in the South Pars field, Southern Iran. The fourth and fifth wells from the field were used to evaluate the reliability of the model. The results showed that a BP-ANN was successful in estimating body wave velocities and so when just gamma ray, neutron, deep resistivity (lateral log deep) were used as net work inputs, the net exactness ware comparatively low but using PEF effects increased this exactness. By using density log the net exactness noticeably grew and in this manner using both PEF and density log beside other mentioned logs as inputs approached to more real results.

Keywords:

• artificial neural network
• compressional and shear wave velocities
• density log
• petrophysical logs
• photo electric effect