Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables are vertical depth, bulk density, and acoustic compressional wave velocity, with the activation function of tangent sigmoid. The average percent error, absolute average percent error, mean square error, root mean square error, and correlation coefficient (R2) were applied for evaluation. The results revealed that the best artificial neural network structure was (3-8-1), with average percent error, absolute average percent error, mean square error, root mean square error, and correlation coefficient R2 of -0.52, 1.01, 3994, 63.2, and 0.995, respectively. A C++ computer program is provided with a calculation sample to simplify the implementation of the proposed artificial neural network. The dependency degree of pore pressure on each input parameter is investigated, revealing the highest impact of depth on pore pressure prediction. Furthermore, to check the validity of the artificial neural network against the different datasets, the artificial neural network performance was compared with 84 new data points and showed an advantage over the existing models. The very good performance of artificial neural network for different types of oil reservoirs and formations reveals an insignificant effect of lithology on the prediction of pore pressure.
Today, the prediction system and survival rate became an important request. A previous paper constructed a scoring system to predict breast cancer mortality at 5 to 10 years by using age, personal history of breast cancer, grade, TNM stage and multicentricity as prognostic factors in Spain population. This paper highlights the improvement of survival prediction by using fuzzy logic, through upgrading the scoring system to make it more accurate and efficient in cases of unknown factors, age groups, and in the way of how to calculate the final score. By using Matlab as a simulator, the result shows a wide variation in the possibility of values for calculating the risk percentage instead of only 16. Additionally, the accuracy will be calculate
... Show MoreThe oil sector in Iraq suffered from several difficulties led to the decline and reduction of oil production and the deterioration of refineries and transport pipeline status in addition to the weakness of the technology side and the prevalence of financial and administrative corruption besides the high costs of rehabilitation of the oil sector and also administrative and institutional problems still ongoing.
In spite of Iraq's possession of vast oil wealth allows him to play an important role in the international energy market، he is still under the level. The production of oil doesn'<
... Show MoreThis paper addresses the nature of Spatial Data Infrastructure (SDI), considered as one of the most important concepts to ensure effective functioning in a modern society. It comprises a set of continually developing methods and procedures providing the geospatial base supporting a country’s governmental, environmental, economic, and social activities. In general, the SDI framework consists of the integration of various elements including standards, policies, networks, data, and end users and application areas. The transformation of previously paper-based map data into a digital format, the emergence of GIS, and the Internet and a host of online applications (e.g., environmental impact analysis, navigation, applications of VGI dat
... Show MoreThe Compressional-wave (Vp) data are useful for reservoir exploration, drilling operations, stimulation, hydraulic fracturing employment, and development plans for a specific reservoir. Due to the different nature and behavior of the influencing parameters, more complex nonlinearity exists for Vp modeling purposes. In this study, a statistical relationship between compressional wave velocity and petrophysical parameters was developed from wireline log data for Jeribe formation in Fauqi oil field south Est Iraq, which is studied using single and multiple linear regressions. The model concentrated on predicting compressional wave velocity from petrophysical parameters and any pair of shear waves velocity, porosity, density, and
... Show MoreThe Compressional-wave (Vp) data are useful for reservoir exploration, drilling operations, stimulation, hydraulic fracturing employment, and development plans for a specific reservoir. Due to the different nature and behavior of the influencing parameters, more complex nonlinearity exists for Vp modeling purposes. In this study, a statistical relationship between compressional wave velocity and petrophysical parameters was developed from wireline log data for Jeribe formation in Fauqi oil field south Est Iraq, which is studied using single and multiple linear regressions. The model concentrated on predicting compressional wave velocity from petrophysical parameters and any pair of shear waves velocity, porosity, density, a
... Show MoreThe research aims to assess the claystone exposed in the Nfayil Formation (Middle Miocene) for Portland cement (P.C.) manufacturing based on mineralogy and geochemistry. The importance of the study is to avoid the miming of the agricultural soils that are mining now for the cement industry. Claystones of Nfayil Formation and the limestone of the Euphrates Formation were used to design the raw mixture as clay to limestone (1:3). The chemical composition (%) of the designed mixture was calculated using the Alligation Alternative Method (A.A.M.) as CaO (65.52), MgO (1.05), SiO2 (21.65), Al2O3 (7.43), Fe2O3 (2.62), Na2O3+K2O (1.52) and SO3 (0.26), which are suitable for P.C. The lime saturation factor (LSF = 92.8), silica saturation fac
... Show MoreAbstract. Al-Abbawy DAH, Al-Thahaibawi BMH, Al-Mayaly IKA, Younis KH. 2021. Assessment of some heavy metals in various aquatic plants of Al-Hawizeh Marsh, southern of Iraq. Biodiversitas 22: 338-345. In order to describe the degree of contamination of aquatic environments in Iraq, heavy metals analysis (Fe, Ni, Cr, Cd, Pb, and Zn) was conducted for six aquatic macrophytes from different locations of Al-Hawizeh Marsh in southern Iraq. The six species were Azolla filiculoides (floating plant), Ceratophyllum demersum, Potamogeton pectinatus, Najas marina (submerged plants), Phragmites australis, and Typha domingensis (emergent plants). The results indicate that cadmium, chromium, and iron concentrations in aquatic plants were above the
... Show MoreAbstract
Objectives: The main objective of this study is to find the influence level of nursing incivility on psychological well-being among nurses in southeastern Iraq.
Methods: In this descriptive correlational study, a convenience sample of 250 nurses working in three government hospitals in Missan province in the south of Iraq were surveyed using the nursing incivility scale (NIS) and Ryff's psychological well-being scale (PWB) from November 2021, to July 2022. A multivariate multiple regression analysis was done to analyze the multivariate effect of workplace incivility on the psychological well-being of nurses.
Results: The study results show a
... Show MoreThe physical and morphological characteristics of porous silicon (PS) synthesized via gas sensor was assessed by electrochemical etching for a Si wafer in diluted HF acid in water (1:4) at different etching times and different currents. The morphology for PS wafers by AFM show that the average pore diameter varies from 48.63 to 72.54 nm with increasing etching time from 5 to 15min and from 72.54 to 51.37nm with increasing current from 10 to 30 mA. From the study, it was found that the gas sensitivity of In2O3: CdO semiconductor, against NO2 gas, directly correlated to the nanoparticles size, and its sensitivity increases with increasing operating temperature.