Shiranish formation has been divided into two microfacies units: 1-Marly biowacke stone facies 2-Biogenic pack stone facies These microfacies reflected marine deep shelf margin in the upper part of the formation, the lower part was deeper. 238 slides were investigated depending on Mineralogical, compositional and Biological processes, which reflect deep shelf margin at upper part of the formation, but at the lower part open sea environment. The age of the formation is estimated depending on the recognized biostratigraphic zone using the index fossils to be Upper- Middle Maestrichtian.

Accurate pore and fracture pressure detection is a major step in successful drilling operations design. The overestimation of these parameters absolutely leads to serious problems throughout and after well drilling. This study is concerned with the characterization and analysis of the most significant diagenetic processes that degrade or improve the reservoir characteristics of the Mauddud Formation in the Badra oil field. The primary goal of this research is to estimate the pore pressure and fracture pressure using well logging data by Techlog 2015 software in order to assess the impact on the estimation of the mud weight window (MWW). The estimated values of formation pressures are then analyzed according to different diagenetic p

Oil recovery from carbonate reservoirs is one of the critical challenges in the oil industry due to the strongly oil-wet nature, natural fractures, and the heterogeneity of carbonate rocks. Subsequently, waterflooding can only displace oil from large fractures, leaving the majority of oil trapped in the rock matrix. This work suggests that nanofluid flooding, as a predesigned flooding method, is an alternative to conventional waterflooding. Various concentrations of silica nanofluid at different nanoparticle concentrations were formulated and systematically investigated for their characteristics, stability at reservoir conditions, and their influence on wettability and oil recovery. Silica nanoparticles were sustainably synthesized

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

Reservoir rock typing integrates geological, petrophysical, seismic, and reservoir data to identify zones with similar storage and flow capacities. Therefore, three different methods to determine the type of reservoir rocks in the Mushrif Formation of the Amara oil field. The first method represents cluster analysis, a statistical method that classifies data points based on effective porosity, clay volume, and sonic transient time from well logs or core samples. The second method is the electrical rock type, which classifies reservoir rocks based on electrical resistivity. The permeability of rock types varies due to differences in pore geometry, mineral composition, and fluid saturation. Resistivity data are usually obtained from w