Reservoir characterization is an important component of hydrocarbon exploration and production, which requires the integration of different disciplines for accurate subsurface modeling. This comprehensive research paper delves into the complex interplay of rock materials, rock formation techniques, and geological modeling techniques for improving reservoir quality. The research plays an important role dominated by petrophysical factors such as porosity, shale volume, water content, and permeability—as important indicators of reservoir properties, fluid behavior, and hydrocarbon potential. It examines various rock cataloging techniques, focusing on rock aggregation techniques and self-organizing maps (SOMs) to identify specific and

Modern statistical techniques offer a range of methodologies for modelling time series data, with conditional and unconditional approaches providing complementary insights that enhance overall model accuracy. This article introduced a modified ARIMA model employing conditional and unconditional parameter estimates. The methodology for the new model based on novel methods is provided. The prediction process, one and two steps ahead, is covered in detail, and a novel algorithm is presented. The best model is picked based on various measurement criteria, such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute scaled error (MASE). The suggested model is applied to a monthly petrol sales dataset (Jan

In this research, Artificial Neural Networks (ANNs) technique was applied in an attempt to predict the water levels and some of the water quality parameters at Tigris River in Wasit Government for five different sites. These predictions are useful in the planning, management, evaluation of the water resources in the area. Spatial data along a river system or area at different locations in a catchment area usually have missing measurements, hence an accurate prediction. model to fill these missing values is essential.
The selected sites for water quality data prediction were Sewera, Numania , Kut u/s, Kut d/s, Garaf observation sites. In these five sites models were built for prediction of the water level and water quality parameters.

Blend solid polymer electrolytes (BSPEs) comprising PVA/HPMC/CuSO4 were prepared using a solution casting approach. BSPEs were synthesized with varying weight percentages of CuSO4 (0, 10, 20, and 30 wt.%). A variety of experimental methods, including X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and linear sweep voltammetry (LSV), were used to characterize these BSPE systems. The polymer blend matrix underwent structural alterations, according to the XRD data. The standard data from JCPDS card numbers for copper sulfate matches well with the observed strong peaks of PVA/HPMC+20 wt.% of CuSO4 BSPE. The complex

الخلاصة

     يتضمن البحث تعيين عنصر الزئبق السام بتراكيزنزرة عالية الدقة (نانوغرام) باستخدام منظومة يخار الزئبق البارد لنماذج غذائية (لحوم حمراء ، لحوم بيضاء ) مختلفة ونماذج مائية (ماء النهر، مياه صناعية ، ماء الشرب) وربط المنظومة بتقنية الامتصاص الذري اللهبي.

     ان عنصر الزئبق من اشد العناصر سمية وان التراكيز المسموح بها عالميا لايتعدى جزء واحد