The addition of nanoparticles to the drilling fluids for geological projects is a promising application for enhancing the stability, rheological properties, and overall performance of the mud. This study aims to thoroughly analyze and compare the effects of silica and alumina nanoparticles on the properties and effectiveness of polymer drilling mud. Silica and alpha-alumina nanoparticles were synthesized using the sol-gel process and characterized through several techniques, including X-ray diffraction, Fourier-transform infrared spectroscopy, field-emission scanning electron microscopy, and atomic force microscopy. The effects of silica and alumina nanoparticles on various properties of drilling mud were then measured using a perme

   This study demonstrates a sustainable, "trash-to-treasure" approach by synthesizing silica (SiO2) and alumina (Al2O3) nanoparticles (30–80 nm) from local waste materials—specifically bentonite clay and aluminum wire waste—and evaluating their performance as eco-friendly additives in 350 mL water-based drilling fluids at concentrations ranging from 0 to 1 g. Tested under harsh subsurface conditions, the incorporated nanoparticles significantly enhanced the fluids' rheological properties, lubricity, filtration control, and swelling inhibition, with performance scaling alongside nanoparticle concentration. Notably, at a 1 g dosage, the fluid's yield point spiked from a baseline of 9 to 42 for SiO2 and 32 for (Al2O3), while h

The objective of the conventional well testing technique is to evaluate well- reservoir interaction through determining the flow capacity and well potential on a short-term basis by relying on the transient pressure response methodology. The well testing analysis is a major input to the reservoir simulation model to validate the near wellbore characteristics and update the variables that are normally function of time such as skin, permeability and productivity multipliers.

Well test analysis models are normally built on analytical approaches with fundamental physical of homogenous media with line source solution. Many developments in the last decade were made to increase the resolution of transient response derivation to meet the

Liquefied petroleum gases (LPG) consist of hydrocarbons obtained by refining crude oil, either from propane or butane or a mixture of the two. There are often other components such as propylene, butylene or other hydrocarbons, but they are not the main component. The study aims to review previous studies dealing with designing an LPG system to deliver gas to residential campuses and buildings. LPG is extracted from natural gas NG by several processes, passing through fractionation towers and then pressuring into CNG storage tanks. Gas contains several problems, including gas leakage through the pipes and leads to fires or explosions in LPG storage and distribution tanks, so safety conditions were taken in the design and implementation. T

Gum Arabic is a natural gummy exudate gained from the trees of Acacia species (Acacia senegal and Acacia seyal), Family: Fabaceae. Gum Arabic considers as a dietary fiber with a high percentage of carbohydrates and low protein content. Sugars arabinose and ribose were originally discovered and isolated from gum Arabic and it is representing the original source of these sugars. A gum emanation from trees occurs under stress conditions such as heat, poor soil fertility, drought, and injury. Mainly gum is produced in belt region of Africa, mainly Sudan, Chad, and Nigeria. In the food industry, it is used in confectionery; in the pharmaceutical industry, it is used as emulsifier, film coating and others. Traditionally the g

This paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar

This paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar