Permeability is one of the most important property for reservoir characterization, and its prediction has been one of the fundamental challenges specially for a complex formation such as carbonate, due to this complexity, log analysis cannot be accurate enough if it’s not supported by core data, which is critically important for formation evaluation. In this paper, permeability is estimated by making both core and log analysis for five exploration wells of Yammama formation, Nasiriyah oil field. The available well logging recorders were interpreted using Interactive Petrophysics software (IP) which used to determine lithology, and the petrophysical properties. Nuclear Magnetic Resonance (NMR) Measurements is used for laboratory tests, which provide an accurate, porosity and permeability measurements. The results show that the main lithology in the reservoir is limestone, in which average permeability of the potential reservoir units’ values tend to range from 0.064275 in zone YA to 20.74 in zone YB3, and averaged porosity values tend to range from 0.059 in zone YA to 0.155 in zoneYB3. Zone YB3 is found to be the best zone in the Yammama formation according to its good petrophysical properties. The correlation of core-log for permeability and porosity produce an acceptable R^2 equal to 0.618, 0.585 respectively
The present study develops an artificial neural network (ANN) to model an analysis and a simulation of the correlation between the average corrosion rate carbon steel and the effective parameter Reynolds number (Re), water concentration (Wc) % temperature (T o) with constant of PH 7 . The water, produced fom oil in Kirkuk oil field in Iraq from well no. k184-Depth2200ft., has been used as a corrosive media and specimen area (400 mm2) for the materials that were used as low carbon steel pipe. The pipes are supplied by Doura Refinery . The used flow system is all made of Q.V.F glass, and the circulation of the two –phase (liquid – liquid ) is affected using a Q.V.F pump .The input parameters of the model consists of Reynolds number , water concentration and temperature. The output is average corrosion rate .The performance of the two training algorithms, gradient descent with momentum and Levenberg-Marquardt, are compared to select the most suitable training algorithm for corrosion rate model. The model can be used to calculate the average corrosion rate properties of carbon steel alloy as functions of Reynolds number, water concentration and temperature. Accordingly, the combined influence of these effective parameters and the average corrosion rate is simulated. The results show that the corrosion rate increases with the increase of temperature, Reynolds number and the increase of water concentration.
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