Prediction of Well Logs Data and Estimation of Petrophysical Parameters of Mishrif Formation, Nasiriya Field, South of Iraq Using Artificial Neural Network (ANN)

Petrophysical properties including volume of shale, porosity and water saturation are significance parameters for petroleum companies in evaluating the reservoirs determining hydrocarbon zones. These can be achieved through conventional petrophysical calculations from well logs data such as gamma ray, sonic, neutron, density deep resistivity. The logging operations targeted limestone Mishrif Ns-X Well, Nasiriya Oilfield, south Iraq could not done due to some problems related condition. ray log was only recorded cased borehole. Therefore, estimating perforation zones has performed drilled abandoned. This paper presents a solution estimate missing open-hole Formation resistivity using supervised Artificial Neural Network (ANN) Petrel software (2016.2). Furthermore, original gamma-ray along with predicted ANN models were processed, effective calculated determine coefficient determination (R2) 0.65, 0.77, 0.82, 0.04 between tested total correlations 0.67, 0.91, 0.84 0.57 density, respectively. best possible hydrocarbon-bearing zone ranges depth about 1980-2030 m mB1unit. provides good accuracy matching clean non-heterogeneous formations compared those higher heterogeneity that contain more than one type lithology. Well can, therefore, linked development plans Field instead neglect it.