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Selection of an Optimum Drilling Fluid Model to Enhance Mud Hydraulic System Using Neural Networks in Iraqi Oil Field
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In drilling processes, the rheological properties pointed to the nature of the run-off and the composition of the drilling mud. Drilling mud performance can be assessed for solving the problems of the hole cleaning, fluid management, and hydraulics controls. The rheology factors are typically termed through the following parameters: Yield Point (Yp) and Plastic Viscosity (μp). The relation of (YP/ μp) is used for measuring of levelling for flow. High YP/ μp percentages are responsible for well cuttings transportation through laminar flow. The adequate values of (YP/ μp) are between 0 to 1 for the rheological models which used in drilling. This is what appeared in most of the models that were used in this study. The pressure loss is a gathering of numerous issues for example rheology of mud), flow regime and the well geometry. An artificial neural network (ANN) that used in this effort is an accurate or computational model stimulated by using JMP software. The aim of this study is to find out the effect of rheological models on the hydraulic system and to use the artificial neural network to simulate the parameters that were used as emotional parameters and then find an equation containing the parameters μp, Yp and P Yp/ μp to calculate the pressure losses in a hydraulic system. Data for 7 intermediate casing wells with 12.25" hole size and 95/8" intermediate casing size are taken from the southern Iraq field used for the above purpose. Then compare the result with common equations used to calculate pressure losses in a hydraulic system. Also, we calculate the optimum flow by the maximum impact force method and then offset in Equation obtained by (Joint Marketing Program) JMP software. Finally, the equation that was found to calculate pressure losses instead of using common hydraulic equations with long calculations gave very close results with less calculation.                                                                                 

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Publication Date
Tue Dec 31 2019
Journal Name
Journal Of Engineering
A High Resolution 3D Geomodel for Giant Carbonate Reservoir- A Field Case Study from an Iraqi Oil Field
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Constructing a fine 3D geomodel for complex giant reservoir is a crucial task for hydrocarbon volume assessment and guiding for optimal development. The case under study is Mishrif reservoir of Halfaya oil field, which is an Iraqi giant carbonate reservoir. Mishrif mainly consists of limestone rocks which belong to Late Cenomanian age. The average gross thickness of formation is about 400m. In this paper, a high-resolution 3D geological model has been built using Petrel software that can be utilized as input for dynamic simulation. The model is constructed based on geological, geophysical, pertophysical and engineering data from about 60 available wells to characterize the structural, stratigraphic, and properties distribution along

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Publication Date
Tue Dec 31 2019
Journal Name
Journal Of Engineering
A High Resolution 3D Geomodel for Giant Carbonate Reservoir- A Field Case Study from an Iraqi Oil Field
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Constructing a fine 3D geomodel for complex giant reservoir is a crucial task for hydrocarbon volume assessment and guiding for optimal development. The case under study is Mishrif reservoir of Halfaya oil field, which is an Iraqi giant carbonate reservoir. Mishrif mainly consists of limestone rocks which belong to Late Cenomanian age. The average gross thickness of formation is about 400m. In this paper, a high-resolution 3D geological model has been built using Petrel software that can be utilized as input for dynamic simulation. The model is constructed based on geological, geophysical, pertophysical and engineering data from about 60 available wells to characterize the structural, stratigraphic, and properties distri

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Publication Date
Mon Jul 01 2019
Journal Name
Iop Conference Series: Materials Science And Engineering
3D geological model of Nahr Umr/Ratawi oil field in Basra
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Abstract<p>The study includes building a 3-D geological model, which involves get the Petrophysical properties as (porosity, permeability and water saturation). Effective Porosity, water saturation results from log interpretation process and permeability from special correlation using core data and log data. Clay volume can be calculated by six ways using IP software v3.5 the best way was by using gamma Ray. Also, Water Resistivity, flushed zone saturation and bulk volume analysis determined through geological study. Lithology determined in several ways using M-N matrix Identification, Density-Neutron and Sonic-Neutron cross plots. The cut off values are determined by Using EHC (Equivalent Hydra</p> ... Show More
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Publication Date
Fri Apr 30 2021
Journal Name
Iraqi Geological Journal
Geological Model for Mauddud Reservoir Khabaz Oil Field
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The Mauddud reservoir, Khabaz oil field which is considered one of the main carbonate reservoirs in the north of Iraq. Recognizing carbonate reservoirs represents challenges to engineers because reservoirs almost tend to be tight and overall heterogeneous. The current study concerns with geological modeling of the reservoir is an oil-bearing with the original gas cap. The geological model is establishing for the reservoir by identifying the facies and evaluating the petrophysical properties of this complex reservoir, and calculate the amount of hydrocarbon. When completed the processing of data by IP interactive petrophysics software, and the permeability of a reservoir was calculated using the concept of hydraulic units then, there

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Publication Date
Fri Sep 30 2016
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Using Different Surfactants to Increase Oil Recovery of Rumaila Field (Experimental Work)
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Enhanced oil recovery is used in many mature oil reservoirs to increase the oil recovery factor. Surfactant flooding has recently gained interest again. To create micro emulsions at the interface between crude oil and water, surfactant flooding is the injection of surfactants (and co-surfactants) into the reservoir, thus achieving very low interfacial tension, which consequently assists mobilize the trapped oil.

In this study a flooding system, which has been  manufactured and described at high pressure. The flooding processes included oil, water and surfactants. 15 core holders has been prepared at first stage of the experiment and  filled with washed sand grains 80-500 mm and then packing the sand to obtain sand packs

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Calibrating the Reservoir Model of the Garraf Oil Field
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   History matching is a significant stage in reservoir modeling for evaluating past reservoir performance and predicting future behavior. This paper is primarily focused on the calibration of the dynamic reservoir model for the Meshrif formation, which is the main reservoir in the Garraf oilfield. A full-field reservoir model with 110 producing wells is constructed using a comprehensive dataset that includes geological, pressure-volume-temperature (PVT), and rock property information. The resulting 3D geologic model provides detailed information on water saturation, permeability, porosity, and net thickness to gross thickness for each grid cell, and forms the basis for constructing the dynamic reservoir model. The dynamic reservoir mo

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Publication Date
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Multiphase Flow Behavior Prediction and Optimal Correlation Selection for Vertical Lift Performance in Faihaa Oil Field, Iraq
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In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H

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Publication Date
Tue Jan 01 2013
Journal Name
Thesis
User Authentication Based on Keystroke Dynamics Using Artificial Neural Networks
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Computer systems and networks are being used in almost every aspect of our daily life, the security threats to computers and networks have increased significantly. Usually, password-based user authentication is used to authenticate the legitimate user. However, this method has many gaps such as password sharing, brute force attack, dictionary attack and guessing. Keystroke dynamics is one of the famous and inexpensive behavioral biometric technologies, which authenticate a user based on the analysis of his/her typing rhythm. In this way, intrusion becomes more difficult because the password as well as the typing speed must match with the correct keystroke patterns. This thesis considers static keystroke dynamics as a transparent layer of t

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Machine Learning And Computing
Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks
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Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

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