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

This study is concerned with the evaluation of the effect of Euphrates River water quality in Al-Samawa region during
the period 1984-2003 on efficiency and reliability of reverse osmosis desalination systems by calculating the calcium
sulfate scaling index depending on the following indicators: - TDS, Ca+2, Mg+2, Na+1, Cl-1, So4-2, HCO3-1. It was
found from data analysis that this index for these units is greater than permissible limit. Also, the fitted relationship
between this index and TDS is logarithmic, i.e. this index varies more rapidly than TDS, and consequently it is more
representative to the water quality than TDS.

In this study, the feasibility of Forward–Reverse osmosis processes was investigated for treating the oily wastewater. The first stage was applied forward osmosis process to recover pure water from oily wastewater. Sodium chloride (NaCl) and magnesium chloride (MgCl2) salts were used as draw solutions and the membrane that was used in forward osmosis (FO) process was cellulose triacetate (CTA) membrane. The operating parameters studied were: draw solution concentrations (0.25 – 0.75 M), oil concentration in feed solution (FS) (100-1000 ppm), the temperature of FS and draw solution (DS) (30 - 45 °C), pH of FS (4-10) and the flow rate of both DS and FS (20 - 60 l/h). It was found that the water flux and oil concentration in FS increas

The present work aimed to study the efficiency of nanofiltration (NF) and reverse osmosis (RO) membrane for heavy metal removal from wastewater and study the factors affecting the performance of these two membranes: feed concentrations for heavy metal ions, pressure, and flow rate. The experimental results showed, heavy metals concentration in permeate increase with raise in feed concentrations, decline with increase in flow rate. The raise of pressure, heavy metals concentration decreases for RO membrane, but for NF membrane the concentration decrease and then at high pressure increase. The rejection percentage for chromium in NF and RO is 99.7% and 99.9%, for copper is 98.4% and 99.3%, for zinc is 97.9% and 99.5%, for nickel is 97.2% and

The present work aimed to study the efficiency of nanofiltration (NF) and reverseosmosis (RO) process for water recovery from electroplating wastewater and study the factors affecting the performance of two membrane processes. Nanofiltration and reverse osmosismembranes are made from polyamide as spiral wound module. The inorganic materials ZnCl 2 CuCl2 .2H2O, NiCl.2.6H2O and CrCl3.6H2O were used as feed solutions. The operating parametersstudied were: operating time, feed concentrations for heavy metal ions, operating pressure, feed flow rate, feed temperature and feed pH. The experimental results showed, the permeateconcentration increased and water flux decreased with increase in time from 0 to 70 min. Thepermeate concentrations incre

The present work aimed to study the efficiency of nanofiltration (NF) and reverse osmosis (RO) process for water recovery from electroplating wastewater and study the factors affecting the performance of two membrane processes. Nanofiltration and reverse osmosis membranes are made from polyamide as spiral wound module. The inorganic materials ZnCl2, CuCl2.2H2O, NiCl2.6H2O and CrCl3.6H2O were used as feed solutions. The operating parameters studied were: operating time, feed concentrations for heavy metal ions, operating pressure, feed flow rate, feed temperature and feed pH. The experimental results showed, the permeate concentration increased and water flux decreased with increase in time from 0 to 70 min. The permeate concentrations incre

Hundreds of infrastructure investment projects are inserted into the Iraqi government budget annually. Many of these projects suffer from suspension, delay in completion, corruption, or other negative issues known in Iraqi construction projects. This may harm the country's economic recovery due to allocating funds to useless projects from one or more feasibility studies' components. Therefore, the best solution is to pay attention to the feasibility study reports. This study highlights the importance of feasibility studies reports for investment projects and their effective role in evaluating project ideas to ensure an equitable distribution of a country's financial resources to the most feasible investment projects. This importance

Art is a language in which the artist expresses himself, his society, and the events he lives in, so new artistic trends emerged, so the artist no longer practices his art as required by any previous artistic rules. And the thoughts wandering inside him, which led him to the abstract method in which the artist tries to employ the elements of the artwork in a plastic construction through which he achieves the relationships of the abstract form through the rhythms of lines, colors, spaces, shapes and textures without these plastic elements having any connection with the visual reality.
The research aims to find a new vision inspired by the school of geometric abstraction to enrich the field of Saudi plastic painting. And to take advan

This research paper studies the alienation of the intellectuals in the modern novel through the study of two alienated characters, John Marcher in Henry James's The Beast in the Jungle, and Mr. Duffy in James's Joyce's "A Painful Case." As a result of the complexity of life in the industrial societies, the individuals, especially the intellectual ones, feel themselves unable to integrate into social life; they fear society and feel that it endangers their individuality and independence. Thus, these characters live on the fringe of the societ

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach