In this study, the upgrading of Iraqi heavy crude oil was achieved utilizing the solvent deasphalting approach (SDA) and enhanced solvent deasphalting (e-SDA) by adding Nanosilica (NS). The NS was synthesized from local sand. The XRD result, referred to as the amorphous phase, has a wide peak at 2Θ= (22 - 23º) The inclusion of hydrogen-bonded silanol groups (Si–O–H) and siloxane groups (Si–O–Si) in the FTIR spectra. The SDA process was handled using n-pentane solvent at various solvent to oil ratios (SOR) (4-16/1ml/g), room and reflux temperature, and 0.5 h mixing time. In the e-SDA process, various fractions of the NS (1–7 wt.%) have been utilized with 61 nm particle size and 560.86 m²/g surface area in the presence of

The influence of SDBS, SLS, SLES, and SS as drag reducing agents on flow of Iraqi crude oil in pipelines was investigated in the present work. The effect of additive type, additive concentration, pipe diameter, solution flow rate, and the presence of elbows on the percentage of drag reduction (%Dr) and the amount of flow increases (%FI) was addressed. The maximum drag reduction was 55% obtained at 250 ppm SDBS surfactant flowing in straight pipes of 0.0508 m I.D. The dimensional analysis was used for grouping the significant quantities into dimensionless groups to reduce the number of variables. The results showed good agreement between the observed drag reduction percent values and the predicted ones with high value of the co

The applications of herbal medicine have recently acquired growing interest in range of the prophylaxis and treatment of diseases. Olibanum has been used since ancient eras and several reports studied the pharmacological characteristics of boswellic acid, particularly their effect on the inflammatory response, analgesic properties, and anti-arthritic activity mostly in cell lines, but new approaches include animal models to assess these natural derivatives effects taking into consideration of being safer than synthetic preparations.  The impact of olibanum oil on several parameters was studied in rats during this study. These included white blood cell (WBC) count, lactate dehydrogenase (LDH), and C reactive protein (CRP), as well a

The injection of Low Salinity Water (LSWI) as an Enhanced Oil Recovery (EOR) method has recently attracted a lot of attention. Extensive research has been conducted to investigate and identify the positive effects of LSWI on oil recovery. In order to demonstrate the impact of introducing low salinity water into a reservoir, simulations on the ECLIPSE 100 simulator are being done in this work. To simulate an actual reservoir, an easy static model was made. In order to replicate the effects of injecting low salinity water and normal salinity, or seawater, the reservoir is three-phase with oil, gas, and water. It has one injector and one producer. Five cases were suggested to investigate the effect of low salinity water injection with differen

Oilwell cementing operations are crucial for drilling and completion, preserving the well's productive life. However, weak and permeable formations pose a high risk of cement slurry loss, leading to failure. Lightweight cement, like foamed cement, is used to avoid these difficulties. This study is focused on creating a range of foamed slurry densities and examining the effect of gas concentration on their rheological properties. The foaming agent and foam stabilizer are tested, and the optimal concentration is determined to be 2% and 0.12%, respectively, by the weight of the cement.

Furthermore, the construction of samples of foam cement with different densities (0.8, 1.0, 1.2, 1.4, and 1.6) g/cc is performed to f

The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals