The Mauddud Formation was one of the important and widespread Lower Cretaceous period formations in Iraq. It has been studied in three wells (EB. 55, EB. 58, and EB. 59) within the East Baghdad Oil Field, Baghdad, central Iraq. 280 thin sections were studied by microscope to determine fauna, the formation composed of limestone and dolomitized limestone in some parts which tends to be marl in some parts, forty species and genus of benthic foraminifera have been identified beside algae and other fossils, three biozones have been identified in the range which is: Orbitolina qatarica range zone (Late Albian), Orbitolina sefini range zone (Late Albian – Early Cenomanian) and Orbitolina concava range zone (Early Cenomanian), The age of

    Shiranish has been studied at Hijran section near Erbil city, NE Iraq. Fifty two thin-sections were prepared to study them under polarized microscope, to determine the petrographic component, organic content and digenetic processes. Rock units subdivided into four rock beds, as follows: dolostone, foraminiferal biomicrite, poorly washed biomicrite and micrite. Vertical succession of Shiranish Formation refers to off-shore quite marine environment.

   Petrophysical characterization is the most important stage in reservoir management. The main purpose of this study is to evaluate reservoir properties and lithological identification of Nahr Umar Formation in Nasiriya oil field. The available well logs are (sonic, density, neutron, gamma-ray, SP, and resistivity logs). The petrophysical parameters such as the volume of clay, porosity, permeability, water saturation, were computed and interpreted using IP4.4 software. The lithology prediction of Nahr Umar formation was carried out by sonic -density cross plot technique. Nahr Umar Formation was divided into five units based on well logs interpretation and petrophysical Analysis: Nu-1 to Nu-5. The formation lithology is mainly

Some new mono isoimides of asymmetrical pyromillitdiimide derived from pyromellitic dianhydride were synthesized and studied by their melting points, FTIR, and 1HNMR spectroscopy and CHN analysis (for some of them) and it was proved that the mechanism of the formation of these isoimides followed, the mechanism suggested by Cotter et al. by using N, N─-dicyclohexylcarbodiimide as dehydrating agent, in spite of the groups attached to the phenyl moiety as mentioned in literatures.

One of the costliest problems facing the production of hydrocarbons in unconsolidated sandstone reservoirs is the production of sand once hydrocarbon production starts. The sanding start prediction model is very important to decide on sand control in the future, including whether or when sand control should be used. This research developed an easy-to-use Computer program to determine the beginning of sanding sites in the driven area. The model is based on estimating the critical pressure drop that occurs when sand is onset to produced. The outcomes have been drawn as a function of the free sand production with the critical flow rates for reservoir pressure decline. The results show that the pressure drawdown required to

As an important resource, entanglement light source has been used in developing quantum information technologies, such as quantum key distribution(QKD). There are few experiments implementing entanglement-based deterministic QKD protocols since the security of existing protocols may be compromised in lossy channels. In this work, we report on a loss-tolerant deterministic QKD experiment which follows a modified “Ping-Pong”(PP) protocol. The experiment results demonstrate for the first time that a secure deterministic QKD session can be fulfilled in a channel with an optical loss of 9 dB, based on a telecom-band entangled photon source. This exhibits a conceivable prospect of ultilizing entanglement light source in real-life fiber-based

In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa