Innovative laboratory research and fluid breakthroughs have improved carbonate matrix stimulation technology in the recent decade. Since oil and gas wells are stimulated often to increase output and maximum recovery, this has resulted in matrix acidizing is a less costly alternative to hydraulic fracturing; therefore, it is widely employed because of its low cost and the fact that it may restore damaged wells to their previous productivity and give extra production capacity. Limestone acidizing in the Mishrif reservoir has never been investigated; hence research revealed fresh insights into this process. Many reports have stated that the Ahdeb oil field's Mishrif reservoir has been unable to be stimulated due to high inj

Terrestrial Laser Scanning (TLS) can be used to detect damage and analyze volumetric changes in large structural test specimens. Compared to traditional imaging and surface analysis methods, the primary reason for using 3D scanning is the difficulty of attaching targets to towers of varying heights, as well as the fact that some towers are covered with insulating materials that obscure key landmarks. In addition, the large number of incoming and outgoing pipes and stairs attached to the towers further complicates traditional assessment methods. This research focuses on assessing the damage to the three refining towers at the Baiji Refinery facility. Scanning was performed using a 3D laser scanner with HDR color imaging, capturing da

The study focused on the treatment of real oilfield produced water from the East Baghdad field affiliated to the Midland Oil Company (Iraq) using an oil skimming process followed by a coagulation/flocculation process for zero liquid discharge system applications. Belt type oil skimmer was utilized for evaluating the process efficiency with various operating conditions such as temperature (17-40 °C) and time (0.5-2.5 hr.). Polyaluminum chloride (PAC) coagulant and polyacrylamide (PAM) flocculant was used to investigate the performance of the coagulation/flocculation process with PAC dosage (5-90 ppm) and pH (5-10) as operating conditions. In the skimming process, the oil content, COD, turbidity, and TSS decreased with an increase in tempera

SAPO-11 is synthesized from silicoaluminophosphate in the presence of di-n-propylamine as a template. The results show that the sample obtained has good crystallinity, 396m2/g BET surface area, and 0.35 cm3/g pore volume. The hydroisomerization activity of (0.25)Pt (1)Zr (0.5)W/SAPO-11 catalyst was determined using n-decane and base oil. All hydroisomerization experiments of n-decane were achieved at a fixed bed plug flow reactor at a temperature range of 200-275°C and  LHSV 0.5-2h-1.  The results show that the n-decane conversion increases with increasing temperature and decreasing LHSV, the maximum conversion of 66.7 % was achieved at temperature 275°C and LHSV of 0.5 h-1. Meanwhile, the same catalyst was used to improve base oil spec

   SAPO-11 is synthesized from silicoaluminophosphate in the presence of di-n-propylamine as a template. The results show that the sample obtained has good crystallinity, 396m²/g BET surface area, and 0.35 cm³/g pore volume. The hydroisomerization activity of (0.25)Pt (1)Zr (0.5)W/SAPO-11 catalyst was determined using n-decane and base oil. All hydroisomerization experiments of n-decane were achieved at a fixed bed plug flow reactor at a temperature range of 200-275°C and  LHSV 0.5-2h^-1.  The results show that the n-decane conversion increases with increasing temperature and decreasing LHSV, the maximum conversion of 66.7 % was achieved at temperature 275°C and LHSV of 0.5 h^-1

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

New nanotechnology-based approaches are increasingly being investigated for enhanced oil recovery (EOR), with a particular focus on heavy oil reservoirs. Typically, the addition of a polymer to an injection fluid advances the sweep efficiency and mobility ratio of the fluid and leads to a higher crude oil recovery rate. However, harsh reservoir conditions, including high formation salinity and temperature, can limit the performance of such polymer fluids. Recently, nanofluids, that is, dispersions of nanoparticles (NPs) in a base fluid, have been recommended as EOR fluids; however, such nanofluids are unstable, even under ambient conditions. In this work, a combination of ZrO2 NPs and the polyacrylamide (PAM) polymer (ZrO2 NPs–PAM) was us