The gas-lift method is crucial for maintaining oil production, particularly from an established field when the natural energy of the reservoirs is depleted. To maximize oil production, a major field's gas injection rate must be distributed as efficiently as possible across its gas-lift network system. Common gas-lift optimization techniques may lose their effectiveness and become unable to replicate the gas-lift optimum in a large network system due to problems with multi-objective, multi-constrained & restricted gas injection rate distribution. The main objective of the research is to determine the possibility of using the genetic algorithm (GA) technique to achieve the optimum distribution for the continuous gas-lift injectio

Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing artificial TABU algorithm to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as sport, chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement.

      Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.

Asmari is the main productive reservoir in Abu Ghirab oilfield in the south-east part of Iraq. It has history production extends from 1976 up to now with several close periods. Recently, the reservoir suffers some problems in production, which are abstracted as water production rising with oil production declining in most wells. The water problem type of the field and wells is identified by using Chan's diagnostic plots (water oil ratio (WOR) and derivative water oil ratio (WOR') against time). The analytical results show that water problem is caused by the channeling due to high permeability zones, high water saturation zones, and faults or fracturing. The numerical approach is also used to study the water movement inside the reser

Inundation floodingmap aimedto find outearly warningsto avoidenvironmental damageandhumanin terms of theheight ofthe wave ofwater, speed time arrival, effects of inundation sideanddepth of the water/ distanceand reduce the impact of the flood wave after obtaining the process of collapse of the dam in the lower part of the river to the dam area. The study has been using a numerical model one-dimensional depends on the development of equations (Saint-Venant) so that parts of the river, any river channel main banks of the right and left treated as separate parts, that’s the difference in the characteristics of the hydraulic and engineering, along the line of the flow will take into account in each section of the sections and flow in the riv

ArcHydro is a model developed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. Raster-based digital elevation models (DEMs) play an important role in distributed hydrologic modeling supported by geographic information systems (GIS). Digital Elevation Model (DEM) data have been used to derive hydrological features, which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions. Detailed delineation of drainage networks is the first step for many natural resource management studies. Compared with interpretation from aerial photographs or topographic maps, auto

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

          In this work, the modified Lyapunov-Schmidt reduction is used to find a nonlinear Ritz approximation of Fredholm functional defined by the nonhomogeneous Camassa-Holm equation and Benjamin-Bona-Mahony. We introduced the modified Lyapunov-Schmidt reduction for nonhomogeneous problems when the dimension of the null space is equal to two.  The nonlinear Ritz approximation for the nonhomogeneous Camassa-Holm equation has been found as a function of codimension twenty-four.