The map of permeability distribution in the reservoirs is considered one of the most essential steps of the geologic model building due to its governing the fluid flow through the reservoir which makes it the most influential parameter on the history matching than other parameters. For that, it is the most petrophysical properties that are tuned during the history matching. Unfortunately, the prediction of the relationship between static petrophysics (porosity) and dynamic petrophysics (permeability) from conventional wells logs has a sophisticated problem to solve by conventional statistical methods for heterogeneous formations. For that, this paper examines the ability and performance of the artificial intelligence method in permeability prediction and compared its results with the flow zone indicator methods for a carbonate heterogeneous Iraqi formation. The methodology of the research can be Summarized by permeability was estimated by using two methods: Flow zone indicator and Artificial intelligence, two reservoir models are built, where the difference between them is in permeability method estimation, and the simulation run will be conducted on both of the models, and the permeability estimation methods will be examined by comparing their effect on the model history matching. The results showed that the model with permeability predicted by using artificial intelligence matched the observed data for different reservoir responses more accurately than the model with permeability predicted by the flow zone indicator method. That conclusion is represented by good matching between observed data and simulated results for all reservoir responses such for the artificial intelligence model than the flow zone indicator model.
Incremental sheet metal forming is a modern technique of sheet metal forming in which a uniform sheet is locally deformed during the progressive action of a forming tool. The tool movement is governed by a CNC milling machine. The tool locally deforms by this way the sheet with pure deformation stretching. In SPIF process, the research is concentrate on the development of predict models for estimate the product quality. Using simulated annealing algorithm (SAA), Surface quality in SPIF has been modeled. In the development of this predictive model, spindle speed, feed rate and step depth have been considered as model parameters. Maximum peak height (Rz) and Arithmetic mean surface roughness (Ra) are used as response parameter to assess th
... Show MoreThis investigation was conducted to recognize the structure for (RHETI version 2.5 1999) by using exploratory and confirmatory factor analysis. Sample of (620) student of Al-Mustansrya University were administered the (RHETI).
The data of their responses was analyzed by using (PAF) and oblique rotating .
The findings explored (9) factors as one factor for each type and (184) items were loaded by the factors: (60) item for feeling center, (61) items for instinctive center and (63) items for thinking center.
Results of confirmatory factorial analysis supported a model designed by the researcher depended upon a theoretical views of Riso and Hudson
... Show MoreThe change in the optical band gap and optical activation energy have been investigated for pure Poly (vinyl alcohol)and Poly (vinyl alcohol) doped with Aluminum sulphate to proper films from their optical absorption spectra. The absorption spectra were measured in the wave range from (200-700) nm at temperature range (25-140) 0C. The optical band gap (Eg) for allowed direct transition decrease with increase the concentration of Aluminum sulphate. The optical activation energy for allowed direct transition band gap was evaluated using Urbach- edges method. It was found that ?E increases with increasing the concentration of Al2 (SO4)3 and decreases when temperature increases.
Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re
... Show MoreThis study investigates the challenges encountered by first-grade intermediate students in learning the Arabic language. It aims to identify specific obstacles that hinder language acquisition and proficiency among this demographic. Through qualitative and quantitative methods, including surveys and interviews with students, teachers, and parents, the research highlights key issues such as limited vocabulary, difficulties in grammar, lack of engagement with the material, and inadequate teaching resources. The findings reveal a complex interplay between cognitive, social, and educational factors that contribute to these challenges. The study underscores the need for targeted interventions, such as enhanced pedagogical strategies and improved
... Show MoreSAGD is a thermal recovery process in which steam oil ratio, SOR, is a key parameter that can affect the economic outcome of the process. Reservoirs with underlying or overlying lean bitumen present challenges for SAGD as they can act as a heat sink. Water has higher heat capacity than the bitumen and thus requires more steam to heat up the reservoir leading to higher SOR. The potential outcome of operating SAGD in these conditions may be lower bitumen rate and higher steam injection rate, both of which affect plant throughput and thus the economic matrix of SAGD. This paper looks at the performance of SAGD process in the presence of top lean bitumen. It will examine the theoretical CSOR that is needed to produce the bitumen with different
... Show MoreAbstract This study explores the extent to which public relations (PR) departments within Traqj governmental institutions are integrating artificial intelligence (AI) applications into their communication activities. The research adresses the growing importanc of AI in enhancing administrative efficieney, communication transparency, and stakeholder engagement. Adopting a descriptive research design, the study relied on an electtonic questionnaire distributed to PR profesionals across various ministries and government bodies, collecting 100 valid responses. The indings reveal that while younger PR practitioners are actively embracing AI, older employees show limited engagement. Most participants acquired AI-related skills through self- learn
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