Optimum perforation location selection is an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magni

This work includs synthesis of several Schiff bases by condensation of 6- methoxy – 2- amino benzothiazole with some aldehydes and ketones (2- hydroxyl benzaldehyde, 4- hydroxyl benzaldehyde, 4- N,N –dimethy amino acetophenone, benzophenone) to abtain schiff bases (1-5). These schiff bases were found to react with phthalate anhydride to give oxazepine derivatives (6-10) that were reacted with primary aromatic amines to give Diazepine derivatives (11-15). Besides, we prepared new tetrazole derivatives (16-20) from the reaction of the prepared Schiff bases with sodium azide in the prepared compounds that were characterized by physical properties, FT-IR and some of the 1H-NMR and 13C –NMR spectroscopy.

A particulate polymer composite material was prepared by reinforcing with the Aluminum Oxide (Al₂O₃) or Aluminum (Al) metallic particles with a particle size of (30) µm to an unsaturated Polyester Resin with a weight fraction of (5%, 10%, 15%, 20%).

Tensile test results showed the maximum value of elastic modulus reached (2400MPa.)  in the case of reinforcing with (Al) particles with weight fraction (20%) and (1500 MPa.)  in the case of reinforcing with (Al₂O₃) particles of the same weight fraction.

  When the impact and the flexural strength tests were done, the results showed that flexural strength (F.S), maximum shear stress (τ_max), impact strength

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered