Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
... Show MoreIn the present research we the study the deposition of radioactive elements naturally and particularly radioactive radon gas in parts of the body of organisms which are of direct relevance to human life in the city of Baghdad as the samples which were collected from the bones and skin of some kinds of birds and chicken based on the principle that radioactive elements are concentrated always on the bones. We use of this as the exercise detector impact nuclear (CR-39), using the technology Cylindrical diffusion , the results indicated that the largest concentration of radon found in the bone bird Seagull tapered as it was (625 ± 37) Bq.cm-3, and less concentration of radon gas in the chicken bones of Al-kafeel as it was (105 ± 10) Bq.c
... Show MoreSix isolates of A. pullulans were collected from many sources including Hibiscus sabdariffa (Roselle), old Roofs of houses and bathroom surface that referred as Ap ros1, Ap or2, 3, 4 and Ap bs5, 6 respectively, all these isolates were identified based on morphological characteristics and nutritional physiology profiles, all were able to utilize various carbon and nitrogen sources such as glucose, xylose, sucrose, maltose, ammonium sulfate, ammonium nitrate and ammonium chloride, also they showed positive test for starch and amylase, while α-cellulose, ethanol, and methanol were could not be ass
... Show MoreIn this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and
... Show MoreThis paper attempted to study the effect of cutting parameters (spindle speed and feed rate) on delamination phenomena during the drilling glass-polyester composites. Drilling process was done by CNC machine with 10 mm diameter of high-speed steel (HSS) drill bit. Taguchi technique with L16 orthogonal layout was used to analyze the effective parameters on delamination factor. The optimal experiment was no. 13 with spindle speed 1273 rpm and feed 0.05 mm/rev with minimum delamination factor 1.28. &
... Show MoreTraditionally, path selection within routing is formulated as a shortest path optimization problem. The objective function for optimization could be any one variety of parameters such as number of hops, delay, cost...etc. The problem of least cost delay constraint routing is studied in this paper since delay constraint is very common requirement of many multimedia applications and cost minimization captures the need to
distribute the network. So an iterative algorithm is proposed in this paper to solve this problem. It is appeared from the results of applying this algorithm that it gave the optimal path (optimal solution) from among multiple feasible paths (feasible solutions).
In this paper waste natural material (date seed) and polymer particles(UF) were used for investigation of removal dye of the potassium permanganate. Also study effect some variables such as pH, dye concentration and adsorbent concentration on dye removal. 15 experimental runs were done using the itemized conditions designed established on the Box-Wilson design employed to optimize dye removal. The optimum conditions for the dye removal were found: (pH) 12, (dye con.) 2.38 ppm, (adsorbant con.) 0.0816 gm for date seed with 95.22% removal and for UF (pH) 12, (dye con.) 18 ppm, (adsorbant con.) 0.2235 gm with 91.43%. The value of R-square was 85.47% for Date seed and (88.77%) for UF.
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In the past two decades, maritime transport traffic has increased, especially in the case of container flow. The BAP (Berth Allocation Problem) (BAP) is a main problem to optimize the port terminals. The current manuscript explains the DBAP problems in a typical arrangement that varies from the conventional separate design station, where each berth can simultaneously accommodate several ships when their entire length is less or equal to length. Be a pier, serve. This problem was then solved by crossing the Red Colobuses Monkey Optimization (RCM) with the Genetic Algorithm (GA). In conclusion, the comparison and the computational experiments are approached to demonstrate the effectiveness of the proposed method contrasted with other
... Show MoreThe paper aims to propose Teaching Learning based Optimization (TLBO) algorithm to solve 3-D packing problem in containers. The objective which can be presented in a mathematical model is optimizing the space usage in a container. Besides the interaction effect between students and teacher, this algorithm also observes the learning process between students in the classroom which does not need any control parameters. Thus, TLBO provides the teachers phase and students phase as its main updating process to find the best solution. More precisely, to validate the algorithm effectiveness, it was implemented in three sample cases. There was small data which had 5 size-types of items with 12 units, medium data which had 10 size-types of items w
... Show MoreDrilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.
In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation
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