The study is an attempt to predict reservoir characterization by improving the estimation of petro-physical properties (porosity), through integration of wells information and 3D seismic data in early cretaceous carbonate reservoir Yamama Formation of (Abu-Amoud) field in southern part of Iraq. Seismic inversion (MBI) was used on post- stack 3 dimensions seismic data to estimate the values of P-acoustic impedance of which the distribution of porosity values was estimated through Yamama Formation in the study area. EMERGE module on the Hampson Russel software was applied to create a relationship between inverted seismic data and well data at well location to construct a perception about the distribution of porosity on the level of all uni

RPT is a method used for classifying various lithologies and fluids from data of well logging or seismic inversion. Three Formations (Nahr Umr, Shuaiba, and Zubair Formations) were selected in the East Baghdad Oil field within well EB-4 to test the possibility of using this method. First, the interpretations of the well log and Density – Neutron cross plot were used for lithology identification, which showed that Nahr Umr and Zubair formations consist mainly of sandstone and shale, while the Shuaiba Formation consists of carbonate (dolomite and limestone). The study was also able to distinguish between the locations of hydrocarbon reservoirs using RPT. Finally, a polynomial equation was generated from the cross plot domain (AI versus V

Hydrocarbon production might cause changes in dynamic reservoir properties. Thus the consideration of the mechanical stability of a formation under different conditions of drilling or production is a very important issue, and basic mechanical properties of the formation should be determined. There is considerable evidence, gathered from laboratory measurements in the field of Rock Mechanics, showing a good correlation between intrinsic rock strength and the dynamic elastic constant determined from sonic-velocity and density measurements. The values of the mechanical properties determined from log data, such as the dynamic elastic constants derived from the measurement of the elastic wave velocities in the material, should be more accurate t

Hydrocarbon production might cause changes in dynamic reservoir properties. Thus the consideration of the mechanical stability of a formation under different conditions of drilling or production is a very important issue, and basic mechanical properties of the formation should be determined.
There is considerable evidence, gathered from laboratory measurements in the field of Rock Mechanics, showing a good correlation between intrinsic rock strength and the dynamic elastic constant determined from sonic-velocity and density measurements.
The values of the mechanical properties determined from log data, such as the dynamic elastic constants derived from the measurement of the elastic wave velocities in the material, should be more a

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

     Three seismic instantaneous attributes (phase, frequency, and variance) were utilized on 3D-seismic poststack migrated data, covering 617.31 km², integrated with data of two wells (Du-1 and Du-2) in Dujaila oil field, southeast of Iraq. They gave good results in detecting reef buildups and confirmed the existence of the stratigraphic hydrocarbon trap that was not obvious in the conventional seismic amplitude sections. They display several seismic criteria in attribute sections for recognizing reef buildups and hydrocarbon accumulation, such as phase reversal, low frequency, and high amplitude variance. The seismic attributes emphasized that the stratigraphic trap of reef rudist buildups with hydrocarbon content is con

Shiranish formation has been divided into two microfacies units: 1 - Many biowackestone facies and 2 - maly packstone using planktonic foraminifera and other carbonate components in the rock cutting and core slides. Microfacies reflect marin deep shelf margin in the lower part of the formation, the upper part was deeper. The thickness of the formation is determined, depending on addition to the presence of echinoderm framents debris and spines. This is in disagreement with the 195 ft thickness reported by the Oil Exploration Company The age of the formation is estimated depending on the recognized biostratigraphic zone using the index fossils to be Upper - Middle Mastrichtion.

In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty.