Prediction of the formation of pore and fracture pressure before constructing a drilling wells program are a crucial since it helps to prevent several drilling operations issues including lost circulation, kick, pipe sticking, blowout, and other issues. IP (Interactive Petrophysics) software is used to calculate and measure pore and fracture pressure. Eaton method, Matthews and Kelly, Modified Eaton, and Barker and Wood equations are used to calculate fracture pressure, whereas only Eaton method is used to measure pore pressure. These approaches are based on log data obtained from six wells, three from the north dome; BUCN-52, BUCN-51, BUCN-43 and the other from the south dome; BUCS-49, BUCS-48, BUCS-47. Along with the overburden pressure gradient and clay volume, which were also established first, data such as gamma ray, density, resistivity, and sonic log data are also required. A key consideration in the design of certain wells is the forecasting of fracture pressure for wells drilled in the southern Iraqi oilfield of Buzurgan. The pressure abnormality is found in MA, MB21, MC1 and MC2 units by depending on pore pressures calculated from resistivity log. In these units, depths and its equivalent normal and abnormal pressure are detected for all sex selected wells; BUCS-47, BUCS-48, BUCS-49, BUCN-43, BUCN-51 and BBCN-52. For MA, MB21, MC1, and MC2 units, the highest difference in pore pressure values are 1698 psi @ 3750 m (BUCN-51), 3420 psi @ 3900 m (BUCN-51), 788 psi @ 3980 m (BUCS-49), and 5705 psi @ 4020 m (BUCN-52). On other hands, MB11 and MB12 units have normal pressure trend in all studied wells. Finally, the results show that the highest pore and fracture pressure values is existed in North dome, in comparison with that obtained in south dome of Mishrif reservoir at Buzurgan oilfield.
Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo
... Show MoreBackground: The normal decline in systolic blood pressure during recovery phase of treadmill exercise dose not occur in most patients with coronary artery disease, in others recovery values systolic blood pressure may even exceed the peak exercise value. Objectives: Treadmill exercise test parameters indicating the presence and extent of coronary artery disease have traditionally included such as exercise duration, blood pressure and ST-segment response to exercise. The three –minute systolic blood pressure ratio is another important indicator of presence and significance of coronary artery disease is useful and obtainable measure that can be applied in all patients who are undergoing stress testing for evaluation of suspected is
... Show MoreThe influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic
... Show MoreBackground: Obesity tends to appear in modern societies and constitutes a significant public health problem with an increased risk of cardiovascular diseases.
Objective: This study aims to determine the agreement between actual and perceived body image in the general population.
Methods: A descriptive cross-sectional study design was conducted with a sample size of 300. The data were collected from eight major populated areas of Northern district of Karachi Sindh with a period of six months (10th January 2020 to 21st June 2020). The Figure rating questionnaire scale (FRS) was applied to collect the demographic data and perception about body weight. Body mass index (BMI) used for ass
... Show MoreGiven the importance that the Iraqi banking system in general and Islamic banks in particular, there must be effective supervisory oversight of these banks, as supervisory oversight has an essential and effective role in the development and evaluation of the performance of banks, through the application of legal controls and rules. Banking aimed at making sure that its financial centers are safe, protecting depositors' funds, and achieving both monetary and economic stability. This research studied and evaluates the mechanisms and tools used by the Central Bank of Iraq in the supervision and supervision of these banks. Therefore, the research aimed to measure the type and direction of the relationship between the requirements of supervis
... Show MoreIn this research, the one of the most important model and widely used in many and applications is linear mixed model, which widely used to analysis the longitudinal data that characterized by the repeated measures form .where estimating linear mixed model by using two methods (parametric and nonparametric) and used to estimate the conditional mean and marginal mean in linear mixed model ,A comparison between number of models is made to get the best model that will represent the mean wind speed in Iraq.The application is concerned with 8 meteorological stations in Iraq that we selected randomly and then we take a monthly data about wind speed over ten years Then average it over each month in corresponding year, so we g
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