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Prediction Unconfined Compressive Strength for Different Lithology Using Various Wireline Type and Core Data for Southern Iraqi Field
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Unconfined Compressive Strength is considered the most important parameter of rock strength properties affecting the rock failure criteria.  Various research have developed rock strength for specific lithology to estimate high-accuracy value without a core.  Previous analyses did not account for the formation's numerous lithologies and interbedded layers. The main aim of the present study is to select the suitable correlation to predict the UCS for hole depth of formation without separating the lithology. Furthermore, the second aim is to detect an adequate input parameter among set wireline to determine the UCS by using data of three wells along ten formations (Tanuma, Khasib, Mishrif, Rumaila, Ahmady, Maudud, Nahr Umr, Shuaiba and Zubair). After calibration with core test, the results revealed that Young’s Modulus correlations are the best to predict UCS with RMSE (53.23 psi).

Furthermore, the result showed that using the static Young Modulus as an input parameter in predicting UCS gives a closer result to the laboratory test than using a sonic log. This study found that many previous equations were developed for only one type of rock and tended to generalize poorly to the broader database. This study also provided more accurate rock strength estimation, leading to better prognosis in operational strategies and hydraulic fracturing location planning in oil well development when geomechanical analysis needs to be addressed where no core is available. Finally, the expected continuous rock mechanical profile indicates the formation's strength and stability around the wellbore.

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Publication Date
Sun Mar 01 2020
Journal Name
Journal Of Engineering
Study the Effect of Particle Sizes and Concentration on the Rheological Properties of Iraqi Bentonite for Using as Drilling Fluids
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The aim of this study is to identify the effect of particle size and to increase the concentration of Iraqi bentonite on rheological properties in order to evaluate its performance and to know if it can be used as drilling fluid without additives or not. In this study, Iraqi bentonite was carried out by mineral composition (XRD), chemical composition (XRF) and Particle size distribution (PSD), and its rheological properties were measured at different particle size and concentration. The results showed that when the particle size of Iraqi bentonite decreased, and the rheological properties were increased with increased concentration of Iraqi bentonite. Also, Iraqi bentonite was unable to use as drilling fluid without certain additives.

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
measure the relationship between imported inflation and foreign trade in the Iraqi economy for long 1990-2015 using model nardl
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The study aims to discuss the relation between imported inflation and international trade of Iraqi economy for the period (1990-2015) by using annual data. To achieve the study aim, statistical and Econometrics methods are used through NARDL model to explain non-linear relation because it’s a model assigned to measure non-linear relations and as we know most economic relations are non-linear, beside explaining positive and negative effects of imported inflation, and to reach the research aim deductive approach was adopted through using descriptive method to describe and determine phenomenon. Beside the inductive approach by g statistical and standard tools to get the standard model explains the

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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models
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ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

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Publication Date
Wed Nov 13 2019
Journal Name
International Journal Of Research In Pharmaceutical Sciences
Prediction of maternal diabetes and adverse neonatal outcome in normotensive pregnancy using serum uric acid
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Diabetes mellitus, with adverse neonatal events are challenging issues to all obstetricians and pediatricians, where uric acid could play a vital role. We aimed to assess the relationship and prognostic benefits of serum uric acid measured at about 20 weeks’ gestation in normotensive pregnancy, with subsequent maternal diabetes, and neonatal complications. All singleton normotensive pregnant women with normal blood glucose, serum creatinine, and weight before pregnancy, whom attended Medical City Hospital, Department of Obstetrics and Gynecology in Baghdad, were involved and regarded as the case group, on the condition that their serum uric acid measured at 20 weeks’ gestation > 3 mg/dl, but if ≤ 3 mg/dl, they would be regi

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology
Water quality assessment and sodium adsorption ratio prediction of Tigris River using artificial neural network
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Publication Date
Thu Jan 03 2019
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and Artificial Neural Network
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Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology (jestec)
Water Quality Assessment and Sodium Adsorption Ratio Prediction of Tigris River Using Artificial Neural Network
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Sodium adsorption ratio (SAR) is considered as a measure of the water suitability for irrigation usage. This study examines the effect of the physicochemical parameters on water quality and SAR, which included Calcium(Ca+2), Magnesium(Mg+2), Sodium (Na+), Potassium (K), Chloride (Cl-), Sulfate(SO4-2), Carbonate (CO3-2), Bicarbonate (HCO3-), Nitrate (NO3-), Total Hardness (TH), Total Dissolved Salts (TDS), Electrical Conductivity (EC), degree of reaction (DR), Boron (B) and the monthly and annually flow discharge (Q). The water samples were collected from three stations across the Tigris River in Iraq, which flows through Samarra city (upstream), Baghdad city (central) and the end of Kut city (downstream) for the periods of 2016-201

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Publication Date
Wed Sep 23 2020
Journal Name
Artificial Intelligence Research
Hybrid approaches to feature subset selection for data classification in high-dimensional feature space
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This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

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Publication Date
Sun Jun 05 2016
Journal Name
Baghdad Science Journal
Developing an Immune Negative Selection Algorithm for Intrusion Detection in NSL-KDD data Set
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With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica

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Publication Date
Fri Jan 01 2016
Journal Name
Statistics And Its Interface
Search for risk haplotype segments with GWAS data by use of finite mixture models
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The region-based association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease. Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls. To tackle the problem of the sparse distribution, a two-stage approach was proposed in literature: In the first stage, haplotypes are computationally inferred from genotypes, followed by a haplotype coclassification. In the second stage, the association analysis is performed on the inferred haplotype groups. If a haplotype is unevenly distributed between the case and control samples, this haplotype is labeled

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