A new empirical model for enhancing well log permeability prediction, using nonlinear regression method: Case study from Hassi-Berkine oil field reservoir – Algeria

The reservoir permeability (K) factor is the key parameter for characterization. This considered as a determinant quality index. Depending on data required and procedure availability, can be defined from several methods such as; well test interpretation, wireline formation tester, core data. These approaches also in assumption with prediction targeting non-cored sections. According to similar status, logs records an interesting support tool use reach planned objectives. Thus, this investigation consists of finding out model able estimate log adjusting outcome results. In led research, applied approach data, start with, was aimed determine rock types (RRT) using flow zone indicator (FZI) method. obtained classification allows stating each type. order calculate logs, FZI has been founded out. A multi-regression technique used analyze relationship respect specific Gamma-ray (GR), Density Log (RHOB), Sonic (DT). An objective function designated minimize quadratic error between observed normalized coming calculated logs. process carried identify mathematical correlation allowing estimation porosity leading determination. As results, supporting relatively cores. final results accurate real associating exactitude performance logging boreholes overall characterization genuine quick method essentially deduction regarding sections, reference typing, probably further factors.