Physical properties modeling of reservoirs in Mansuri oil field, Zagros region, Iran

The porosity and permeability distribution in four layers of the Cretaceous Ilam Formation was simulated using optimized artificial intelligent algorithms based on conventional logging data of 50 wells in Mansuri oil field in Iran. First, the neutron porosity, interval transit time and density wireline logs in five key wells with core data were used as input parameters to calculate porosity and permeability of the reservoirs using backpropagation artificial neural network (BP neural network) and Support Vector Regression methods, and based on the correlation between the calculated results and the core tested results, BP neural network method was taken to do the physical property calculation. Then, the porosity and permeability distribution of the four layers were modeled using kriging geostatistical algorithms. The results show that Layers 2.1 and 2.2 are high in porosity, Layers 1, 2.1 and 2.2 are high in permeability, while Layer 3 is not reservoir; and the porosity and permeability are higher in the north and lower in the south on the whole.