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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
Mon Jan 01 2024
Journal Name
Open Engineering
Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
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Abstract<p>The current study focuses on utilizing artificial intelligence (AI) techniques to identify the optimal locations of production wells and types for achieving the production company’s primary objective, which is to increase oil production from the Sa’di carbonate reservoir of the Halfaya oil field in southeast Iraq, with the determination of the optimal scenario of various designs for production wells, which include vertical, horizontal, multi-horizontal, and fishbone lateral wells, for all reservoir production layers. Artificial neural network tool was used to identify the optimal locations for obtaining the highest production from the reservoir layers and the optimal well type. Fo</p> ... Show More
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Publication Date
Mon Jan 30 2023
Journal Name
Iraqi Journal Of Science
Prediction of Well Logs Data and Estimation of Petrophysical Parameters of Mishrif Formation, Nasiriya Field, South of Iraq Using Artificial Neural Network (ANN)
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    Petrophysical properties including volume of shale, porosity and water saturation are significance parameters for petroleum companies in evaluating the reservoirs and determining the hydrocarbon zones. These can be achieved through conventional petrophysical calculations from the well logs data such as gamma ray, sonic, neutron, density and deep resistivity. The well logging operations of the targeted limestone Mishrif reservoirs in Ns-X Well, Nasiriya Oilfield, south of Iraq could not be done due to some problems related to the well condition. The gamma ray log was the only recorded log through the cased borehole. Therefore, evaluating the reservoirs and estimating the perforation zones has not performed and the drilled well was

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Publication Date
Wed Dec 25 2024
Journal Name
University Of Kirkuk Journal For Administrative And Economic Science
Anova For Fuzzy Data With Practical in The Medical Field
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This research study Blur groups (Fuzzy Sets) which is the perception of the most modern in the application in various practical and theoretical areas and in various fields of life, was addressed to the fuzzy random variable whose value is not real, but the numbers Millbh because it expresses the mysterious phenomena or uncertain with measurements are not assertive. Fuzzy data were presented for binocular test and analysis of variance method of random Fuzzy variables , where this method depends on a number of assumptions, which is a problem that prevents the use of this method in the case of non-realized.

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Publication Date
Sun Dec 30 2018
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of penetration Rate and cost with Artificial Neural Network for Alhafaya Oil Field
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Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

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Publication Date
Thu Feb 01 2024
Journal Name
Journal Of Engineering
The Economic Evaluation of Various Production Scenarios for Zubair Reservoir in the Kifl Field
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This work evaluates the economic feasibility of various production scenarios for the Zubair reservoir in the Kifl oil field using cash flow and net present value (NPV) calculations. The Kifl field is an exploratory field that has not yet been developed or assessed economically. The first well was drilled in 1960, and three other wells were later drilled to assess the oil accumulation, so in this research, Different production scenarios were evaluated economically. These scenarios were proposed based on the reservoir model of the Zubair formation in the field. The research methodology used QUE$TOR software to estimate capital expenditures (CapEx) and operating expenditures (OpEx) based on field-level data, production prof

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Publication Date
Thu Nov 21 2019
Journal Name
Journal Of Engineering
Comparative Permeability Estimation Method and Identification of Rock Types using Cluster Analysis from Well Logs and Core Analysis Data in Tertiary Carbonate Reservoir-Khabaz Oil Field
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Characterization of the heterogonous reservoir is complex representation and evaluation of petrophysical properties and application of the relationships between porosity-permeability within the framework of hydraulic flow units is used to estimate permeability in un-cored wells. Techniques of flow unit or hydraulic flow unit (HFU) divided the reservoir into zones laterally and vertically which can be managed and control fluid flow within flow unit and considerably is entirely different with other flow units through reservoir. Each flow unit can be distinguished by applying the relationships of flow zone indicator (FZI) method. Supporting the relationship between porosity and permeability by using flow zone indictor is ca

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Publication Date
Thu Aug 30 2018
Journal Name
Iraqi Journal Of Science
Seismic Facies Analysis for Lithofacies Prediction, Okam Field of Niger Delta, Nigeria
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Seismic facies analysis constrained with well log information have been used to predict lithofacies distribution across the Okam Field of Niger Delta. Density and gamma ray logs were cross-plotted and the seismic section was subdivided vertically into different seismic facies. The delineated lithologies, from well logs were correlated with seismic facies signatures using lines of intersection across the wells. Gamma ray and resistivity logs were used to identify the interfaces between the lithofacies and correlated across the field. Structural interpretation was carried out. Time slices were generated and examined at different intervals within the identified reservoirs. Stratigraphic related attribute and envelope were extracted on these

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Publication Date
Thu May 28 2020
Journal Name
Iraqi Journal Of Science
Palynofacies and Source Rocks Evaluation for Selected Samples of Subba Oil Field, Southern Iraq
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This study includes a palynological and organic geochemical evaluation of Nahr Umr, Zubair, and Yamama Formations in the Subba field, Southern Iraq, represented by three wells (Su-14, Su-9 and Su-8). The determination of quantity organic matter showed that the rocks of Nahr Umr Formation had a total organic carbon of 4.76%, indicating very good production of hydrocarbons where type (II) kerogen was dominant, but the rocks were thermally immature. Zubair Formation had a total organic carbon of 1.91% to 2.26%, indicating good to very good production of hydrocarbons where kerogen of types (I) and (II/III) were dominant, with low thermal maturity. Yamama Formation showed a total organic carbon of 1.68%, revealing good production of hydrocarb

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Publication Date
Wed Dec 25 2024
Journal Name
Journal Of Baghdad College Of Dentistry
Effect of surface treatments and thermocycling on shear bond strength of various artificial teeth with different denture base materials
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Background: Separation and deboning of artificial teeth from denture bases present a major clinical and labortory problem which affect both the patient and the dentist. The optimal bond strength of artificial teeth with denture base reinforced with nanofillers and flexible denture bases and the effect of thermo cycling should be evaluated. This study was conducted to evaluate and compare the shear bond strength of artificial teeth (acrylic and porcelain) with denture bases reinforced by 5% Zirconium oxide nanofillers and flexible bases under the effect of different surface treatments and thermo cycling and comparing the results with conventional water bath cured denture bases. Material and methods: Two types of artificial teeth; acrylic and

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Artificial Neural Network (ANN) for Prediction of Viscosity Reduction of Heavy Crude Oil using Different Organic Solvents
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The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests  and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a  heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage  (5, 10 and  20 wt.% )  of  (n-heptane, toluene, and a mixture of  different ratio

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