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

The research aims to detect the problems of educational reality faced by university professors and identify statistically significant differences in the academic problems of university instructors. It has adopted an analytical descriptive research approach to achieve research objectives and identifies the study community with professors of public and private universities. A random sample of 250 instructors was selected for the purpose of applying the questionnaire to them, knowing the academic problems encountered in the course of their work at universities, and adopting appropriate statistical means to process and analyze the data. The research concluded with a set of results, including that all fields (infrastructure, admission of

Reservoir rock typing integrates geological, petrophysical, seismic, and reservoir data to identify zones with similar storage and flow capacities. Therefore, three different methods to determine the type of reservoir rocks in the Mushrif Formation of the Amara oil field. The first method represents cluster analysis, a statistical method that classifies data points based on effective porosity, clay volume, and sonic transient time from well logs or core samples. The second method is the electrical rock type, which classifies reservoir rocks based on electrical resistivity. The permeability of rock types varies due to differences in pore geometry, mineral composition, and fluid saturation. Resistivity data are usually obtained from w

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

 The research aims to identify the extent to which the theatrical and musical arts contribute to diagnosing and treating psychological problems among the residents of children’s villages in Jordan, and the methodologies adopted by the theatrical and musical arts to achieve this. It moves on to prove the theory that theatrical and musical arts have an impact on improving the psychology of the residents of children’s villages in Jordan by reviewing the theories and opinions that address the subject from a scientific point of view proven by experiences and expertise. The research took place in the period between (2019-2020), and the spatial limits came within the (SOS) children's villages in Jordan. The importance of the research is to

In the present study, advanced oxidation process / heterogeneous photocatalytic process (UV/TiO₂/Fenton) system was investigated to the treatment of oily wastewater. The present study was conducted to evaluate the effect of hydrogen peroxide concentration H₂O₂, initial amount of the iron catalyst Fe⁺², pH, temperature, amount of TiO₂ and the concentration of oil in the wastewater.  The removal efficiency for the system UV/TiO₂/Fenton at optimal conditions and dosage (H₂O₂ = 400mg/L, Fe⁺² = 40mg/L, pH=5, temperature =30^oC, TiO₂=75mg/L) for 1000mg/L load was found to be 77%.

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In the field of research in the investment of gas fields, this requires that we first look at the center of the contracting parties in terms of the guarantee means granted to them under the contract, which constitute a means of safety and motivation to enter as major parties in the investment project. In turn, we will discuss the minimum guarantees, which are the most important guarantees granted to each of the two parties to the contract, namely the national party and the investor.