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Geomechanical Modeling and Artificial Neural Network Technique for Predicting Breakout Failure in Nasiriyah Oilfield
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Wellbore instability is one of the major issues observed throughout the drilling operation. Various wellbore instability issues may occur during drilling operations, including tight holes, borehole collapse, stuck pipe, and shale caving. Rock failure criteria are important in geomechanical analysis since they predict shear and tensile failures. A suitable failure criterion must match the rock failure, which a caliper log can detect to estimate the optimal mud weight. Lack of data makes certain wells' caliper logs unavailable. This makes it difficult to validate the performance of each failure criterion. This paper proposes an approach for predicting the breakout zones in the Nasiriyah oil field using an artificial neural network. It also presents the optimal mud weight window for this field, which can be used to optimise the mud weights to minimise the wellbore instability issues. The results showed that an artificial neural network is a powerful tool for determining the breakout zones using the input data. The obtaining root mean square error and the determination coefficient were respectively 0.0082 and 0.959, by which the 1D MEM gave a high match between the predicted wellbore instabilities using the Mogi-failure criterion and the predicted breakout using the ANN model. Most borehole enlargements occur due to formation shear failures because of using low mud weights during drilling. The conclusion clarify the1.35 g/cc is the optimal mud weights for drilling new wells in this field of interest with fewer drilling issues.

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Publication Date
Tue Dec 01 2020
Journal Name
Journal Of Engineering
Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index
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Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of eac

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Publication Date
Mon May 26 2025
Journal Name
Middle East Journal Of Digestive Diseases (mejdd)
Investigating the Role of sHLA-G in the Immunopathogenesis and predicting Gastroesophageal Reflux Disease
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Background : Gastroesophageal reflux disease (GERD) is one of chronic gastrointestinal diseases in which patient may be asymptomatic or was complained from heartburn and regurgitation or pulmonary symptoms. Aim of the study : Examine the serum level of sHLA-G in GERD patients and can be used as a biomarker for early detection of GERD disease. Materials and methods : The design of the study was a case- control prospective enrolled forty patients consulted Gastroenterology Unit- Al-Kindy Teaching Hospital, were diagnosed as GERD by their physician, and compared to second forty control healthy group form January-2023 to May-2024. Serum used for quantitative assessment of soluble HLA-G (sHLA-G) using a sandwich enzyme-linked immunosorbent a

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Geological Journal
Wellbore Instability Analysis to Determine the Safe Mud Weight Window for Deep Well, Halfaya Oilfield
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Wellbore instability is one of the most common issues encountered during drilling operations. This problem becomes enormous when drilling deep wells that are passing through many different formations. The purpose of this study is to evaluate wellbore failure criteria by constructing a one-dimensional mechanical earth model (1D-MEM) that will help to predict a safe mud-weight window for deep wells. An integrated log measurement has been used to compute MEM components for nine formations along the studied well. Repeated formation pressure and laboratory core testing are used to validate the calculated results. The prediction of mud weight along the nine studied formations shows that for Ahmadi, Nahr Umr, Shuaiba, and Zubair formations

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Publication Date
Sun Dec 31 2023
Journal Name
Iraqi Geological Journal
A Comparative Reservoir Study of Zubair and Nahr Umr Formations in Subba Oilfield, Southern Iraq
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The lower Cretaceous sandstones of Zubair and Nahr Umr formations are the main producing reservoirs in Subba oilfield in southern Iraq. Key differences in their petrophysical and depositional attributes exist affecting their reservoir characteristics. The evaluation of well logs and core porosity-permeability data show better reservoir properties in Nahr Formation. The Litho-saturation logs indicate greater thickness of oil-saturated reservoir units for Nahr Unr Formation associated with lower values of shale volume, and higher values of effective porosity. In addition, higher values of permeability for Nahr Umr Formation is suggested by applying porosity-irreducible water saturation cross plot. The reducing reservoir quality of Zub

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Publication Date
Fri Dec 01 2023
Journal Name
Bahrain Medical Bulletin
Effectiveness of Instructional Program on Nurses’ Knowledge Concerning Palliative and Supportive Care for old Adults with Heart Failure
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Abstract Background: The prevalence of heart failure (HF) continues to increase with an increase in the aging population. Palliative care should be integrated into routine disease management for all patients with serious illness, regardless of settings or prognosis. Objectives: The purposes of this study were to determine the level of knowledge of nurses concerning palliative care for patients with heart failure after implementation of instructional program. Design: The study was a quasi-experimental study and consists of 60 nurses. Setting: The study was conducted between17th November 2021, to 10th February 2022, at three teaching hospitals in Baghdad city, Iraq. Method: A non-probability (purposive) sample was utilized, nurses who agreed

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Publication Date
Sat Oct 01 2011
Journal Name
Journal Of Engineering
MODIFIED TRAINING METHOD FOR FEEDFORWARD NEURAL NETWORKS AND ITS APPLICATION in 4-LINK SCARA ROBOT IDENTIFICATION
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In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

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Publication Date
Mon Oct 17 2011
Journal Name
Journal Of Engineering
MODIFIED TRAINING METHOD FOR FEEDFORWARD NEURAL NETWORKS AND ITS APPLICATION in 4-LINK SCARA ROBOT IDENTIFICATION
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In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha

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Publication Date
Fri Nov 29 2024
Journal Name
The Iraqi Geological Journal
Data Driven Approach for Predicting Pore Pressure of Oil and Gas Wells, Case Study of Iraq Southern Oilfields
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Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

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Publication Date
Sun Feb 10 2019
Journal Name
Iraqi National Journal Of Nursing Specialties
Impact Televised Media Violence upon Children's relationship at Primary Schools with their Mates in Al-Nasiriyah City
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Abstract:
Objective: To know the effect of televised media violence on the relationship of Children's primary school with mates and identify association between the demographic characteristics and socioeconomic status for primary school children and their relation with exposure to televised violence media.
Methodology: simple probability sample of 221 children were selected from 1425 children are learning in schools in Al-Nasiriyah City.
Results: Results of the current study showed that (53.8%) of children exposed to Televised media violence be relationship with their mates weak, (46.2%) of them have their relationship with their mates good, as well as the study showed that the presence significant correlation with males genders

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Publication Date
Thu Feb 01 2024
Journal Name
Journal Of Engineering
Identifying Failure Factors in the Implementation of Residential Complex Projects in Iraq
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Residential complexes have witnessed a great demand in most countries worldwide, as they are one of the main infrastructure elements, in addition to achieving a developed urban landscape. However, complex residential projects in developing countries face various factors that could be improved in their implementation, especially in Iraq. Sixty-two experts in residential complex projects were interviewed and surveyed to verify these projects' failure factors,. Fifty-one factors were the main failure factors, divided into four main components (leadership, management system, external forces, and project resources). The Relatively Important Index (RII) is used to determine the relative importance factors and obtain the top tw

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