The Nahr Umr Formation is considered one of the main reservoirs produced in southern Iraq. It is one of the important siliciclastic deposits of the Cretaceous sequence of Iraq oilfields. Zubair oil fields ZB-190 and ZB-047 were chosen to study areas. This study depends on the available core and cutting samples to determine the facies analysis, depositional environments, petrographic characteristics and diagenesis processes. Based on the description of the core and the borehole, six types of facies were distinguished in the Nahr Umr Formation, resulting in an intercalated sandstone and shale with a thin layer of siltstone. The petrographic study of the clastic part of the Nahr Umr Formation showed that the sandstone is composed m

Gas Chromatography GC, Gas Chromatography–Mass spectrometry GC/MS techniques used for analysis of the crude oils that taken from (10) producing wells in Nasiriyah oil field including (NS-1, NS-3, NS-4, NS-5, NS-6, NS-7, NS-8, NS-9, NS-10, and NS-12) from Mishrif reservoir . This reservoir is one of the important reservoirs in Al-Nasiriyah oil field, and it will be the main subject in the current study in order to provide information of crude oil analysis in this area, also to provide information on its characterizations. Mishrif Formation is one of the principle carbonate reservoir in central and southern Iraq. It is part of the wasia group and widespread throughout the Arabian gulf, It is deposited during Cenomanian-Early Turonian cyc

      The Mishrif Formation (Cenomanian – Early Turonian) is an important geologic formation in southern Iraq due to its petrophysical properties and geographic extensions, making it a good reservoir of hydrocarbons. Petrophysical properties of the Mishrif Formation in the current study at the Nasiriya oil field were determined from the interpretation of three open-hole logs data of (NS-1, NS-2, and NS-3) wells.

The results of the Mishrif petrophysical evaluation showed that the formation consists of five variable units (CRI, MA, CRII, MB1 and MB2), each one characterized by distinct petrophysical characteristics.

The upper (MA) and lower (MB) units were determined using electrical, porosity and gamma-ray logs. A sha

The Yamama Formation is a significant reservoir in the southern part of Iraq. This formation consists of limestone deposited throughout the Lower Cretaceous period within main retrogressive depositional series. This study aims to identify the impact of the diagenesis processes on the reservoir’s characteristics (porosity and permeability). Diagenesis processes’ analysis and the identification of Yamama Formation depended on the examination of more than 250 thin sections of the core samples from two wells that were used to determine different diagenetic environments and processes. The three identified diagenetic environments that affected Yamama reservoir were the marine, meteoric and burial environments. Eight diagenetic pr

Nasryia oil field is located about 38 Km to the north-west of Nasryia city. The field was discovered in 1975 after doing seismic by Iraqi national oil company. Mishrif formation is a carbonate rock (Limestone and Dolomite) and its thickness reach to 170m. The main reservoir is the lower Mishrif (MB) layer which has medium permeability (3.5-100) md and good porosity (10-25) %. Form well logging interpretation, it has been confirmed the rock type of Mishrif formation as carbonate rock. A ten meter shale layer is separating the MA from MB layer. Environmental corrections had been applied on well logs to use the corrected one in the analysis. The combination of Neutron-Density porosity has been chosen for interpretation as it is c

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

The method of solving volterra integral equation by using numerical solution is a simple operation but to require many memory space to compute and save the operation. The importance of this equation appeares new direction to solve the equation by using new methods to avoid obstacles. One of these methods employ neural network for obtaining the solution.

This paper presents a proposed method by using cascade-forward neural network to simulate volterra integral equations solutions. This method depends on training cascade-forward neural network by inputs which represent the mean of volterra integral equations solutions, the target of cascade-forward neural network is to get the desired output of this network. Cascade-forward neural

In this study, the upgrading of Iraqi heavy crude oil was achieved utilizing the solvent deasphalting approach (SDA) and enhanced solvent deasphalting (e-SDA) by adding Nanosilica (NS). The NS was synthesized from local sand. The XRD result, referred to as the amorphous phase, has a wide peak at 2Θ= (22 - 23º) The inclusion of hydrogen-bonded silanol groups (Si–O–H) and siloxane groups (Si–O–Si) in the FTIR spectra. The SDA process was handled using n-pentane solvent at various solvent to oil ratios (SOR) (4-16/1ml/g), room and reflux temperature, and 0.5 h mixing time. In the e-SDA process, various fractions of the NS (1–7 wt.%) have been utilized with 61 nm particle size and 560.86 m²/g surface area in the presence of 12 m