Water flooding is one of the most important methods used in enhanced production; it was a pioneer method in use, but the development of technology within the oil industry, takes this subject toward another form in the oil production and application in oil fields with all types of oils and oil reservoirs. Now days most of the injection wells directed from the vertical to re-entry of full horizontal wells in order to get full of horizontal wells advantages.
This paper describes the potential benefits for using of re-entry horizontal injection wells as well as combination of re –entry horizontal injection and production wells. Al Qurainat productive sector was selected for study, which is one of the four main productive sectors of South Rumaila oil field. A simulation model – named as SRFQ was used in the present work to predict the re-entry horizontal wells performance.
Four scenarios were suggested to cover the full scope of the study; those scenarios are different in manner of wells combinations. Cumulative oil production, ultimate recovery percentage are two criteria were used to predict the performance and comparison of scenarios.
Results from simulation model (SRFQ) runs revealed that the productive sector can be continue to gain 1564.331 MMSTB till 2020, without changing to any existing injection and production wells status, which is called the base scenario. While scenario no.1 needs some of work over and remedies jobs, which gives more cumulative oil production reaches to 1698.481 MMSTB till 2020.
On another side, scenarios no. 2 and 4 are the most important scenarios because re-entry horizontal injection wells were implemented. Very good and encourage results were gained over the bas scenario from the sector under study.
At last, scenario no.3 was suggested just to predict the production capacity of the Al Qurainat sector with re-entry horizontal production wells and existing vertical injection and production wells, while the cumulative oil production reaches 3398.481MMSTB.
Abstract
The Classical Normal Linear Regression Model Based on Several hypotheses, one of them is Heteroscedasticity as it is known that the wing of least squares method (OLS), under the existence of these two problems make the estimators, lose their desirable properties, in addition the statistical inference becomes unaccepted table. According that we put tow alternative, the first one is (Generalized Least Square) Which is denoted by (GLS), and the second alternative is to (Robust covariance matrix estimation) the estimated parameters method(OLS), and that the way (GLS) method neat and certified, if the capabilities (Efficient) and the statistical inference Thread on the basis of an acceptable
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... Show MoreAqueous root extract has been used to examine the green production of silver nanoparticles (AgNPs) by reducing the Ag+ ions in a silver nitrate solution. UV-Vis spectroscopy, X-ray diffraction, field emission scanning electron microscopy, and Fourier transform infrared spectroscopy (FTIR) were used to analyze the produced AgNPs. The AgNPs that were created had a maximum absorbance at 416 nm, were spherical in form, polydispersed in nature, and were 685 nm in size.The AgNPs demonstrated antibacterial efficacy against Escherichia coli and Staphylococcus. The dengue vector Aedes aegypti's second instar larvae were very susceptible to the AgNPs' powerful larvicidal action.
Q-switch Nd: YAG laser of wavelengths 235nm and 1,460nm with energy in the range 0.2 J to 1J and 1Hz repetition rate was employed to synthesis Ag/Au (core/shell) nanoparticles (NPs) using pulse laser ablation in water. In this synthesis, initially the silver nano-colloid prepared via ablation target, this ablation related to Au target at various energies to creat Ag/Au NPs. Surface Plasmon Resonance (SPR), surface morphology and average particle size identified employing: UV-visible spectrophotometer, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The absorbance spectra of Ag NPs and Ag/Au NPs showed sharp and single peaks around 400nm and 410nm, respec