Problem solving methods and mechanisms contribute to facilitating human life by providing tools to solve simple and complex daily problems. These mechanisms have been essential tools for professional designers and design students in solving design problems.
This research dealt with one of those mechanisms, which is the (the substance-field model model), as it has been mentioning that this mechanism is characterized by the difficulty of its application, which formed the main research problem. In home gardens (the sub-problem of research), an analysis of this problem was applied and then a solution was found to address it. The researcher used the 3dsmax program to implement the proposed design.
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In this work, we construct the projectively distinct (k, n)-arcs in PG (3, 4) over Galois field GF (4), where k 5, and we found that the complete (k, n)-arcs, where 3 n 21, moreover we prove geometrically that the maximum complete (k, n)-arc in PG (3, 4) is (85, 21)-arc. A (k, n)-arcs is a set of k points no n+ 1 of which are collinear. A (k, n)-arcs is complete if it is not contained in a (k+ 1, n)-arcs
The Late Cretaceous-Early Paleocene Shiranish and Aliji formations have been studied in three selected wells in Jambur Oil Field (Ja-50, Ja-53, and Ja-67) in Kirkuk, Northeastern Iraq. This study included lithostratigraphy and biostratigraphy. The Late Campanian-Maastrichtian Shiranish Formation consist mainly of thin marly and chalky limestone beds overlain by thin marl beds, with some beds of marly limestone representing an outer shelf basinal environment, the unconformable contact with the above Middle Paleocene-Early Eocene Aliji Formation contain layers of limestone with marly limestone and chalky limestone which represents an outer shelf basinal environment. Five Biozones in the Shiranish Formation were determined which are: 1
... Show MoreThe Aaliji Formation in wells (BH.52, BH.90, BH.138, and BH.188) in Bai Hassan Oil Field in Low Folded Zone northern Iraq has been studied to recognize the palaeoenvironment and sequence stratigraphic development. The formation is bounded unconformably with the underlain Shiranish Formation and the overlain Jaddala Formation. The microfacies analysis and the nature of accumulation of both planktonic and benthonic foraminifera indicate the two microfacies associations; where the first one represents deep shelf environment, which is responsible for the deposition of the Planktonic Foraminiferal Lime Wackestone Microfacies and Planktonic Foraminiferal Lime Packstone Microfacies, while the second association represents the deep-sea environme
... Show MoreThis study aimed to investigate the role of Big Data in forecasting corporate bankruptcy and that is through a field analysis in the Saudi business environment, to test that relationship. The study found: that Big Data is a recently used variable in the business context and has multiple accounting effects and benefits. Among the benefits is forecasting and disclosing corporate financial failures and bankruptcies, which is based on three main elements for reporting and disclosing that, these elements are the firms’ internal control system, the external auditing, and financial analysts' forecasts. The study recommends: Since the greatest risk of Big Data is the slow adaptation of accountants and auditors to these technologies, wh
... Show MoreGas-lift technique plays an important role in sustaining oil production, especially from a mature field when the reservoirs’ natural energy becomes insufficient. However, optimally allocation of the gas injection rate in a large field through its gas-lift network system towards maximization of oil production rate is a challenging task. The conventional gas-lift optimization problems may become inefficient and incapable of modelling the gas-lift optimization in a large network system with problems associated with multi-objective, multi-constrained, and limited gas injection rate. The key objective of this study is to assess the feasibility of utilizing the Genetic Algorithm (GA) technique to optimize t
The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti
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