Publications by authors named "Ali Samer Muhsan"

Dye-Sensitized Solar Cells (DSSCs) have attracted great attention due to environmentally friendly low-cost processing, excellent working ability in diffuse light, and potential to meet the power demands of future buildings due the true class of building integrated photovoltaics (BIPV). Nevertheless, DSSCs have relatively low photoconversion efficiency (PCE) due to multiple issues. Several strategies have been employed to enhance its PCE.

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  • The study explores an improved method to predict bubble point pressure (Pb) using adaptive neuro-fuzzy inference system (ANFIS) and trend analysis, overcoming challenges faced by traditional pressure-volume-temperature (PVT) measurements.
  • The ANFIS model was developed with data from over 700 datasets and has shown superior performance compared to 21 existing models, achieving an impressive correlation coefficient (R) of 0.994.
  • Results indicate that the ANFIS model accurately represents the relationships between input and output parameters, demonstrating greater reliability and precision in predicting Pb than previously established models.
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Article Synopsis
  • - Bubble point pressure ( ) is crucial for petroleum production and reservoir characterization, but traditional measurement methods are expensive and time-consuming, leading researchers to explore alternatives like empirical correlations and machine learning.
  • - Previous methods have limitations in accuracy and often lack a clear understanding of the relationships between input features and target predictions, prompting the development of a new model using the Group Method of Data Handling (GMDH).
  • - The GMDH model, built from 760 datasets, demonstrates superior accuracy with low errors and a high correlation coefficient, indicating it effectively captures the physical behavior of the relationships among key parameters like gas solubility and reservoir temperature.
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The bubble point pressure ( ) is a crucial pressure-volume-temperature (PVT) property and a primary input needed for performing many petroleum engineering calculations, such as reservoir simulation. The industrial practice of determining is by direct measurement from PVT tests or prediction using empirical correlations. The main problems encountered with the published empirical correlations are their lack of accuracy and the noncomprehensive data set used to develop the model.

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A novel resin-based nanocomposite-coated sand proppant is introduced to address the issue of proppant flowback in post-fracturing fluid flowback treatments and hydrocarbon production. Self-aggregation in the water environment is the most attractive aspect of these developed proppants. In this work, sand was sieve-coated with 0.

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Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature.

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The chemical sand consolidation methods involve pumping of chemical materials, like furan resin and silicate non-polymer materials into unconsolidated sandstone formations, in order to minimize sand production with the fluids produced from the hydrocarbon reservoirs. The injected chemical material, predominantly polymer, bonds sand grains together, lead to higher compressive strength of the rock. Hence, less amounts of sand particles are entrained in the produced fluids.

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