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Petrophysical Analysis Based on Well Logging Data for Tight Carbonate Reservoir: The SADI Formation Case in Halfaya Oil Field
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Carbonate reservoirs are an essential source of hydrocarbons worldwide, and their petrophysical properties play a crucial role in hydrocarbon production. Carbonate reservoirs' most critical petrophysical properties are porosity, permeability, and water saturation. A tight reservoir refers to a reservoir with low porosity and permeability, which means it is difficult for fluids to move from one side to another. This study's primary goal is to evaluate reservoir properties and lithological identification of the SADI Formation in the Halfaya oil field. It is considered one of Iraq's most significant oilfields, 35 km south of Amarah. The Sadi formation consists of four units: A, B1, B2, and B3. Sadi A was excluded as it was not filled with hydrocarbons. The structural and petrophysical models were built based on data gathered from five oil wells. The data from the available well logs, including RHOB, NPHI, SONIC, Gamma-ray, Caliper, and resistivity logs, was used to calculate the petrophysical properties. These logs were analyzed and corrected for environmental factors using IP V3.5 software. where the average formation water resistivity (Rw = 0.04), average mud filtrate resistivity (Rmf = 0.06), and Archie's parameters (m = 2, n = 1.9, and a = 1) were determined. The well-log data values calculated the porosity, permeability, water saturation, and net-to-gross thickness ratio (N/G).

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Publication Date
Tue May 01 2012
Journal Name
Journal Of Engineering
Study on wind loads coefficients and flow field characteristics around the parabolic trough with stiffeners
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wind load coefficient

Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
Estimate the Parallel System Reliability in Stress-Strength Model Based on Exponentiated Inverted Weibull Distribution
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Abstract<p>In this paper, we employ the maximum likelihood estimator in addition to the shrinkage estimation procedure to estimate the system reliability (<italic>R<sub>k</sub> </italic>) contain <italic>K<sup>th</sup> </italic> parallel components in the stress-strength model, when the stress and strength are independent and non-identically random variables and they follow two parameters Exponentiated Inverted Weibull Distribution (EIWD). Comparisons among the proposed estimators were presented depend on simulation established on mean squared error (MSE) criteria.</p>
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Publication Date
Mon Sep 23 2019
Journal Name
Baghdad Science Journal
Hazard Rate Estimation Using Varying Kernel Function for Censored Data Type I
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     In this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used:  local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction
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Publication Date
Tue Aug 31 2021
Journal Name
International Journal Of Intelligent Engineering And Systems
FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management
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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

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Publication Date
Thu Nov 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Requirements of investment in the marsh : a case study in the Marshlands of Maysan
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The search aims to find out the most important requirements for the success of marshes investment and maximize the expected returns of investment was marked by a problem find aquestion for availability requirements necessary for the success of marshes investment has a research community owners decision in the province Maysan the most important conclusions to the search is that the investment great importance in support of the economy and raising the cultural and social level, there is no investment successful but only with configure the investment environment appropriate economic, social, cultural, political, and security ways. the most important recommendations are working on the provision of the investment environment appropria

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Publication Date
Fri Mar 28 2008
Journal Name
Ama, Agricultural Mechanization In Asia, Africa & Latin America
The Effect of Two Type of Plows With Four Speeds on the Field Capacity and Bulk Density
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Publication Date
Tue Nov 01 2016
Journal Name
Materials Science Forum
Study the Effect of Liquid Layer Level on the Formation of Zinc Oxide Nanoparticles Synthesized by Liquid-Phase Pulsed Laser Ablation
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This work is focused on studying the effect of liquid layer level (height above a target material) on zinc oxide nanoparticles (ZnO and ZnO2) production using liquid-phase pulsed laser ablation (LP-PLA) technique. A plate of Zn metal inside different heights of an aqueous environment of cetyl trimethyl ammonium bromide (CTAB) with molarity (10-3 M) was irradiated with femtosecond pulses. The effect of liquid layer height on the optical properties and structure of ZnO was studied and characterized through UV-visible absorption test at three peaks at 213 nm, 216 nm and 218 nm for three liquid heights 4, 6 and 8 mm respectively. The obtained results of UV–visible spectra test show a blue shift accomp

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Publication Date
Sun Sep 03 2017
Journal Name
Baghdad Science Journal
Gravity Field Interpretation for Major Fault Depth Detection in a Region Located SW- Qa’im / Iraq
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This research deals with the qualitative and quantitative interpretation of Bouguer gravity anomaly data for a region located to the SW of Qa’im City within Anbar province by using 2D- mapping methods. The gravity residual field obtained graphically by subtracting the Regional Gravity values from the values of the total Bouguer anomaly. The residual gravity field processed in order to reduce noise by applying the gradient operator and 1st directional derivatives filtering. This was helpful in assigning the locations of sudden variation in Gravity values. Such variations may be produced by subsurface faults, fractures, cavities or subsurface facies lateral variations limits. A major fault was predicted to extend with the direction NE-

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Publication Date
Thu Apr 01 2021
Journal Name
Complexity
Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem
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Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

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