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Exploring the Impact of Petrophysical Uncertainties on Recoverable Reserves: A Case Study
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Reliable estimation of critical parameters such as hydrocarbon pore volume, water saturation, and recovery factor are essential for accurate reserve assessment. The inherent uncertainties associated with these parameters encompass a reasonable range of estimated recoverable volumes for single accumulations or projects. Incorporating this uncertainty range allows for a comprehensive understanding of potential outcomes and associated risks. In this study, we focus on the oil field located in the northern part of Iraq and employ a Monte Carlo based petrophysical uncertainty modeling approach. This method systematically considers various sources of error and utilizes effective interpretation techniques. Leveraging the current state of available data, our approach generates a wide range of theoretically possible results. Furthermore, establishing a set of probabilities to indicate the likelihood of each possible outcome is of utmost importance. By implementing this approach, we aim to enhance reserve assessments by accounting for petrophysical uncertainties, thereby providing decision makers with valuable insights into the range of possible outcomes and associated risks. This study contributes to a more robust understanding of recoverable reserves and supports informed decision making in the oil and gas industry.

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Publication Date
Mon Jul 01 2024
Journal Name
Journal Of Food Process Engineering
Artificial intelligence‐based modeling of novel non‐thermal milk pasteurization to achieve desirable color and predict quality parameters during storage
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Abstract<sec><label></label><p>This study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro‐fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field‐based non‐thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (<italic>p</italic> > 0.05) for lightness (<italic>L</italic>*), redness‐greenness (<italic>a</italic>*), yellowness‐blueness (<italic>b</italic>*), total color differences (∆<italic>E</italic>), hue angle (<italic>h</italic></p></sec> ... Show More
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Publication Date
Sun Mar 06 2022
Journal Name
Nature Environment And Pollution Technology
Green Synthesis Of Bimetallic Iron/Copper Nanoparticles Using Ficus Leaves Extract For Removing Orange G(OG) Dye From Aqueous Medium
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This study shows that it is possible to fabricate and characterize green bimetallic nanoparticles using eco-friendly reduction and a capping agent, which is then used for removing the orange G dye (OG) from an aqueous solution. Characterization techniques such as scanning electron microscopy (SEM), Energy Dispersive Spectroscopy (EDAX), X-Ray diffraction (XRD), and Brunauer-Emmett-Teller (BET) were applied on the resultant bimetallic nanoparticles to ensure the size, and surface area of particles nanoparticles. The results found that the removal efficiency of OG depends on the G‑Fe/Cu‑NPs concentration (0.5-2.0 g.L-1), initial pH (2‑9), OG concentration (10-50 mg.L-1), and temperature (30-50 °C). The batch experiments showed

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Publication Date
Fri Nov 01 2024
Journal Name
Heliyon
Strength and Deformation of Encased Concrete Columns by I- Section Steel and I- Section GFRP Subjected to Different Load Conditions
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Publication Date
Sun Mar 01 2026
Journal Name
Journal Of Molecular Structure
Synthesis, characterization, DFT calculations, molecular docking, ADMET analysis and biological activity of Schiff base metal complexes against colon cancer cells
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Publication Date
Sun Mar 31 2024
Journal Name
Iraqi Geological Journal
Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach
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Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

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Publication Date
Sat Mar 01 2014
Journal Name
International Journal For Sciences And Technology
Preparation of Carboxy Methylated mPEG-Block-(4-Dodecyl Anilide) Copolymers and Their Visco Metric and Surface Tension Properties in THF
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Preparation of Carboxy Methylated mPEG-Block-(4-Dodecyl Anilide) Copolymers and Their Visco Metric and Surface Tension Properties in THF

Publication Date
Sat Jan 03 2026
Journal Name
Journal Of Molecular Structure
Synthesis, characterization, DFT calculations, molecular docking, ADMET analysis and biological activity of Schiff base metal complexes against colon cancer cells
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Publication Date
Sun Mar 01 2026
Journal Name
Journal Of Molecular Structure
Synthesis, characterization, DFT calculations, molecular docking, ADMET analysis and biological activity of Schiff base metal complexes against colon cancer cells
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Publication Date
Wed Jun 16 2021
Journal Name
Biochemical And Cellular Archives
Synthesis, characterization and pharmaceutical studies of schiff base from 2-pyrrolidinone derivative and imidazole-2-carboxaldehyde and corresponding complexes with Metal (||)
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Synthesis, characterization and pharmaceutical studies of schiff base from 2-pyrrolidinone derivative and imidazole-2-carboxaldehyde and corresponding complexes with Metal (||)

Publication Date
Sun Sep 06 2015
Journal Name
Baghdad Science Journal
Measurement of radon gas concentration in water and soil samples in AL-Najaf governorate by using nuclear track detector (CR-39)
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The radon gas concentration in environmental samples soil and water of selected regions in Al-Najaf governorate was measured by using alpha-emitters registrations which are emitted form radon gas in (CR-39) nuclear track detector. The first part is concerned with the determination of radon gas concentration in soil samples, results of measurements indicate that the highest average radon concentration in soil samples was found in (Al-Moalmen) region which was (100.0±7.0 Bq/m3), while the lowest average radon concentration was found in (Al-Askary) region which was (38.5±4.7 Bq/m3), with an average value of (64.23±14.9 Bq/m3) ,the results show that the radon gas concentrations in soil is below the allowed limit from (ICRP) agency which is (

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