Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy and the performance of the algorithms. The random forest algorithm was the most accurate method leading to lowest Root Mean Square Prediction Error (RMSPE) and highest Adjusted R-Square than multiple linear regression algorithm for both training and testing subset respectively. Thus, random Forest algorithm is more trustable in permeability prediction in non-cored intervals and its distribution in the geological model.
Enhanced oil recovery is used in many mature oil reservoirs to increase the oil recovery factor. Surfactant flooding has recently gained interest again. To create micro emulsions at the interface between crude oil and water, surfactant flooding is the injection of surfactants (and co-surfactants) into the reservoir, thus achieving very low interfacial tension, which consequently assists mobilize the trapped oil.
In this study a flooding system, which has been manufactured and described at high pressure. The flooding processes included oil, water and surfactants. 15 core holders has been prepared at first stage of the experiment and filled with washed sand grains 80-500 mm and then packing the sand to obtain sand packs
... Show MoreThis research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.
The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre
... Show MoreBackground: Toxoplasmosis is a very common infection caused by the obligate intracellular protozoan parasite. This parasite is called Toxoplasma gondii widely distributed around the world . Toxoplasma gondii can be vertically transmitted to the fetus during pregnancy and may cause wide range of clinical manifestations in the offspring.
Objective: To determine seroprevalence Immunoglobulin G (IgG) and Immunoglobulin M (IgM ) to toxoplasma gondii among pregnant women and to identify the risk factors.
Type of the study: A cross-sectional study.
Methods: A total of 110 blood samples of pregnant women were collected from
... Show MoreIn 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 =30oC, TiO2=75mg/L) for 1000mg/L load was found to be 77%.
Aluminum foil cover around the re
... Show MoreIn this paper, an enhanced artificial potential field (EAPF) planner is introduced. This planner is proposed to rapidly find online solutions for the mobile robot path planning problems, when the underlying environment contains obstacles with unknown locations and sizes. The classical artificial potential field represents both the repulsive force due to the detected obstacle and the attractive force due to the target. These forces can be considered as the primary directional indicator for the mobile robot. However, the classical artificial potential field has many drawbacks. So, we suggest two secondary forces which are called the midpoint
... Show MoreAnalyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col
... Show MoreSoftware-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
... Show MoreThis research aims at recognizing the concept of Innovation in offering the banking services as well as the concept and dimensions of competitive advantage . And to identify and analyze the relationship ( correlation and impact ) between the concept of Innovation in offering the banking services and the dimensions of the competitive advantage under discussion . The research includes all Iraqi private banks in Baghdad city only of the (20) banks . The researcher adopted , in this study , a random sample of the distribution of the questionnaire to members of the research sample are managers , customers and employees in these banks , and were distributed ( 115 ) form questionnaire , ( 97 ) form w
... Show More