The downhole flow profiles of the wells with single production tubes and mixed flow from more than one layer can be complicated, making it challenging to obtain the average pressure of each layer independently.  Production log data can be used to monitor the impacts of pressure depletion over time and to determine average pressure with the use of Selective Inflow Performance (SIP). The SIP technique provides a method of determining the steady state of inflow relationship for each individual layer. The well flows at different stabilized surface rates, and for each rate, a production log is run throughout the producing interval to record both downhole flow rates and flowing pressure. PVT data can be used to convert measured in-situ r

The downhole flow profiles of the wells with single production tubes and mixed flow from more than one layer can be complicated, making it challenging to obtain the average pressure of each layer independently.  Production log data can be used to monitor the impacts of pressure depletion over time and to determine average pressure with the use of Selective Inflow Performance (SIP). The SIP technique provides a method of determining the steady state of inflow relationship for each individual layer. The well flows at different stabilized surface rates, and for each rate, a production log is run throughout the producing interval to record both downhole flow rates and flowing pressure. PVT data can be used to convert measured in-situ rates

The accurate identification of internal and external pressures in thick-walled hyperelastic vessels is a challenging inverse problem with significant implications for structural health monitoring, biomedical devices, and soft robotics. Conventional analytical and numerical approaches address the forward problem effectively but offer limited means for recovering unknown load conditions from observable deformations. In this study, we introduce a Graph-FEM/ML framework that couples high-fidelity finite element simulations with machine learning models to infer normalized internal and external pressures from measurable boundary deformations. A dataset of 1386 valid samples was generated through Latin Hypercube Sampling of geometric and l

In this work, two different laser dye solutions were used to host highly-pure silicon nitride nanoparticles as scattering centers to fabricate random gain media. The laser dye was dissolved in three different solvents (ethanol, methanol and acetone) and the final results were obtained for methanol only. The silicon nitride nanoparticles were synthesized by dc reactive magnetron sputtering technique with average particle size of 35 nm. The random gain medium was made as a solid rod with high spectral efficiency and low production cost. Optical emission with narrow linewidth was detected at 532-534 nm as 9 mg of silicon nitride nanoparticles were added to the 10 -5 M dye solution. The FWHM of 0.3 and 3.52 nm was determined for Rhodamine B and

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New membrane electrodes for determination of ciprofloxacin hydrochloride were prepared depending on ciprofloxacin hydrochloride - phosphotungstic acid (CFH-PT) as an active material and these electrodes were made with three plasticizers: Di-octylphenylphosphonate(DOPH), Di-butyl phosphate (DBP)Tri-n-butyl phosphate(TBP), in PVC matrix. One of the ciprofloxacin electrodes was gave Nernstian slope equal to 57.21 mV/ decade for DOPH membrane with concentration range from 1.5×10-5 to1.0×10-1 M, and detection limit equal to 1.5×10-6 M .Lifetime was 93 days. Non- Nernstian responses equal to 39.40 and 30.70 mV/ decade for membranes DBP, TBP, respectively. These electrodes were gave concentration range from 1.0× 10-5 to 1.0×10-2 and from 4.0

Development of a precise and delicate reaction has been acquired for the determination of vancomycin hydrochloride using batch and cloud point extraction (CPE) methods. The first method is based on the formation of azo dye as a result of diazotized dapsone coupled with vancomycin HCl (VAN) in a basic medium. The sensitivity of this reaction was enhanced by utilizing a nonionic surfactant (Triton X-114) and the cloud point extraction technique (second method). The azo dye formed was extracted into the surfactant-rich phase, dissolved in ethanol and detected at λ_max 440 nm spectrophotometrically. The reaction was investigated using both batch and CPE methods (with and without extraction), and a simple comparison between the two

The current research aims to find out the extent to which students of the Faculty of Education for Pure Sciences\/Ibn al-Haitham have owned laboratory academic skills, the researcher adopted a descriptive research approach to conform to the goal of the research, the research sample the consisted of 140 students from the Department of Chemistry Phase II, The research tool, which consisted of a measure of laboratory academic skills, which consisted of seven skills and consisted of 28 paragraphs (four paragraphs per field), was prepared and the pent-up scale was chosen because the selected sample were university students, and the results showed the ownership of students' skills of laboratory academic skills other than skill The use of the libr

Genistein (GEN) is The major isoflavone found in soybeans, has a number of cardiovascular health benefits, Postmenopausal syndrome and osteoporosis. A direct flow injection analysis method for estimation of (GEN) in pure and supplements formulation . This system is based on diazotization coupling reactions between procaine penciline (PR) and genistein in basic medium, they formed yellow dyes have maximum absorption at 416 nm. Calibration curve were constructed over different GEN concentrations, linearity for GEN was 10-100 µg.mL-1 and detection limits of 1.51 ?g/mL. In the FIA technique, all analytical factors were analyzed and optimized. The established method was successfully used to determine GEN in the formulations of its supplement

In this research, the water quality of the potable water network in
Al-Shuala Baghdad city were evaluated and compare them with the
Iraqi standards (IQS) for drinking water and World Health
Organization standards (WHO), then water quality index (WQI) were
calculator: pH, heavy metals (lead, cadmium and iron), chlorides,
total hardness, turbidity, dissolved oxygen, total dissolved solid and
electrical conductivity. Water samples are collected weekly during
the period from February 2015 to April 2015 from ten sites. Results
show that the chlorides, total dissolved solid and electrical
conductivity less than acceptable limit of standards, but total
hardness and heavy metals in some samples higher than acceptabl