Impact of Carbon Dioxide Trapping on Geological Storage

If we are to avoid potentially dangerous climate change, we need to capture and store CO2 emitted by fossil-fuel burning power stations and other industrial plants [123]. Saline aquifers provide the largest potential for storage and the widest geographical spread [66]. Subsequent leakage of CO2 into the atmosphere, even over hundreds of years, would render any sequestration scheme inefficient. However, based on the experience of the oil and gas industry, there is a good understanding of trapping mechanisms that take place in geological formations. Carbon capture and storage (CCS), where carbon dioxide, CO2, is collected from industrial sources and injected underground is one way to mitigate atmospheric emissions of this major greenhouse gas (GHG). Possible sites to accommodate CO2 storage are saline aquifers and oil reservoirs. These two types of location are considered for two reasons: the enormous storage potential in aquifers and the additional hydrocarbon production that could be produced by oil reservoirs. It is important that the injection scheme is designed such that the CO2 is safely stored and will not escape to the surface. Residual trapping offers a potentially quick and effective alternative method by which a non-wetting phase is rendered immobile as recent modelling has suggested that up to 90% of CO2 can be effectively immobilised by residual trapping in a short (years to decades) timescale [133]. There are only a few experimental measurements of capillary trapping in unconsolidated media in the literature. This is because the experimental measurements of multi-phase flow are extremely difficult to perform and the results are frequently not reliable at low saturations [119]. Most of the studies concentrate on trapped gas and rather than the residual saturation of a liquid phase: CO2 stored underground will be super-critical and liquid-like. In this work, we focus on measuring reliably and precisely residual saturations for both two- and three-phase flow covering the entire saturation range, including very low residual saturations. We performed drainage-imbibition and buoyancy-driven experiments for two-phase flow (oil-water and gas-water systems) and three-phase gravity drainage experiments for an oil-gas-water system on unconsolidated sand (LV60). The measured porosity of the sand was 0.37 obtained from three replicates (each replicate is a completely new experiment). The mean absolute permeability was 3.1 x 10-11 m2. The initial water saturation (Swi), residual oil saturation (Sor) and residual gas saturation (Sgr) were measured by two methods, namely mass balance (MB) and volume balance (VB). Mean values were 0.27 for Swi, 0.13 for Sor, and 0.14 for Sgr. Accuracy was maintained to be within 0.1% for every measurement. The buoyancy-driven experiments results show that Sor and Sgr are 11% and 14% respectively and generally lower than consolidated media. The trapped saturations initially rise linearly with initial saturation to a maximum value, followed by a constant residual as the initial saturation increases further. This behaviour is not predicted by the most commonly-used empirical models, but is physically consistent with poorly consolidated media where most of the larger pores can easily be invaded at relatively low saturation and there is, overall, relatively little trapping. The best match to our experimental data was achieved with the trapping model proposed by Aissaoui [2]. The three-phase gravity drainage experiments results show that for high initial gas saturations more gas can be trapped in the presence of oil than in a two-phase (gaswater) system. This is unlike previous measurements on consolidated media, where the trapped gas saturation is either similar or lower to that reached in an equivalent twophase experiment. The maximum residual gas saturation is over 20%, compared to 14% for two-phase flow. For lower initial gas saturation, the amount of trapping follows the initial-residual trend seen in two-phase experiments, although some values lie below the two-phase correlation These results are discussed in relation to pore-scale displacement processes and compared to literature values – mainly on consolidated media – that find that both gas and oil residuals are lower in three-phase than twophase flow [32, 52, 70, 81, 95, 97, 101, 108, 143-145]. This work implies that CO2 injection in poorly consolidated media would lead to rather poor storage efficiencies, with at most 4-6% of the rock volume occupied by trapped CO2; this is at the lower end of the compilation of literature results shown in Fig. 5.2. Using the Land correlation to predict the behaviour would tend to over-estimate the degree of trapping except for high initial saturations. The presence of a third phase (such as in an oil field, for instance) may improve the trapping efficiency

An Overview of Mobile Ad Hoc Networks for the Existing Protocols and Applications

Mobile Ad Hoc Network (MANET) is a collection of two or more devices or nodes or terminals with wireless communications and networking capability that communicate with each other without the aid of any centralized administrator also the wireless nodes that can dynamically form a network to exchange information without using any existing fixed network infrastructure. And it's an autonomous system in which mobile hosts connected by wireless links are free to be dynamically and some time act as routers at the same time, and we discuss in this paper the distinct characteristics of traditional wired networks, including network configuration may change at any time, there is no direction or limit the movement and so on, and thus needed a new optional path Agreement (Routing Protocol) to identify nodes for these actions communicate with each other path, An ideal choice way the agreement should not only be able to find the right path, and the Ad Hoc Network must be able to adapt to changing network of this type at any time. and we talk in details in this paper all the information of Mobile Ad Hoc Network which include the History of ad hoc, wireless ad hoc, wireless mobile approaches and types of mobile ad Hoc networks, and then we present more than 13 types of the routing Ad Hoc Networks protocols have been proposed. In this paper, the more representative of routing protocols, analysis of individual characteristics and advantages and disadvantages to collate and compare, and present the all applications or the Possible Service of Ad Hoc Networks.Comment: 24 Pages, JGraph-Hoc Journa

Mixture and Non-Mixture Bayesian Hierarchical Study of Seizure Count Data Using New Generalized Poisson Model

In this paper Bayesian methods is performed on a medical trial Seizure count data set by introducing the new three parameter generalized Poisson model GPM(α,β,l) as an alternative model to the standard Poisson model SPM(l) which is considered on an earlier work for the generalized linear mixed model. The new model is developed by introducing two more parameters α and β called indicator parameters. The main advantage of an indicator parameter is that it gives the new Poisson model the mixture (when α>0,β=1,2) and non-mixture (when α=0) options. Another feature of proposed new model is that it generalize the posterior of the parameters to predict the behavior of the Seizure counts data, in agreement with generalized linear mixed model. Unlike earlier authors, who confined and limited their work only on standard Poisson model SPM(l), to analyze the counts data in generalized linear mixed model, which make the new model more resilience and litheness. The parameters of the new model will be estimated using Bayesian approach that serves as a subtle tool for model selection and identification. An illustration is provided using the Seizure count data. The posterior summaries using Markov Chain Monte Carlo (MCMC) Gibbs sampling approach are presented for the new model for different values of the parameters. The study of the estimated parameters would help the users to have more prospect and clarity about the role of the new model. It is found that using proposed new model in generalized linear mixed model has more resiliency than standard Poisson model considered earlier. The proposed model is fully adaptive to the available data and gives scientists another option for modeling the data

Inflammatory Bowel Disease: The Association of Inflammatory Cytokine Gene Polymorphisms

The frequencies of alleles and genotypes of TNF-α, TNF-β, and IL-10 genes were examined in Saudi subjects including IBD patients (UC and CD) and matched controls. Venous blood samples were collected from IBD patients and healthy control subjects, and genomic DNA was extracted using commercially available kit (Qiagen, CA, USA). In order to detect TNF-α (-308G/A), TNF-β (+252A/G), IL-10 (-1082G/A), (-819C/T), and (-592C/A) polymorphisms, the TNF-α, TNF-β, and IL-10 genes were amplified using an amplification refractory mutation systems PCR methodology. Analysis of data showed that the frequencies of alleles and genotype of TNF-α (-308G/A), TNF-β (+252A/G), and IL-10 (-1082G/A), (-819C/T), and (-592C/A) polymorphisms differ between IBD patients and control subjects. Our study clearly indicated that the TNF-α (-308G/A), TNF-β (+252A/G), and IL-10 (-1082 G/A) polymorphisms are associated significantly with the risk of IBD susceptibility while other two, IL-10-819C/T and IL-10-592C/A, polymorphisms are not associated with IBD in Saudi population. However, well-designed epidemiological as well as genetic association studies with large sample size among different ethnicities should be performed in order to have better understanding of this relationship

Conceptual Understanding for Systems of Linear Equations: Difficulties and Challenges

The conceptual understanding of algebraic concepts is challenging to students, adds to the challenge of teaching them. This study aimed to reveal the level of conceptual understanding about Systems of Linear Equations (SLEs) and its difficulties that faced students while they solving problems. The study sample consisted of (68) male and female students of linear algebra course. The conceptual understanding test was prepared with two qualitative and quantitative rubrics to classify students’ levels. The study results showed a decrease in the conceptual understanding of SLEs in general, and in the area of realizing the relationships between concepts related to SLEs in particular. In contrast, the level of conceptual understanding of students was medium in the context of multiple representations. The results also showed many difficulties that students face while solving SLEs. The study recommended the need to focus on the conceptual understanding of concepts related to SLEs and to use multiple representations and connect them together when teaching mathematical concepts

Forecasting Monthly Maximum Temperatures in Kerbala Using Seasonal ARIMA Models.

يعتبر التنبؤ بالعوامل الجوية مسالة مهمة في مجال الأرصاد الجوي والبحث العلمي. في هذا البحث، تم اعتماد نموذج الانحدار الذاتي المتكامل والمتوسط المتحرك الفصلي (ARIMA) والذي يستند على نظرية Box- Jenkins. استخدمت البيانات الشهرية لمعدل درجة الحرارة العظمى لمدينة كربلاء للفترة (من يناير 1980 إلى ديسمبر 2016). تم اقتراح عدة نماذج للتنبؤ اعتمادا على دالة الارتباط الذاتي ودالة الارتباط الذاتي الجزئي لبيانات السلسلة الزمنية للسنوات من 1980 الى 2015. تم اختبار النماذج المقترحة باستخدام درجات الحرارة الشهرية العظمى لسنة 2016. من اجل اختبار دقة النماذج والمقارنة بينها استخدمت المعلمات الاحصائية مثل معدل الخطأ المطلق MAE، الجذر التربيعي لمتوسط مربعات الخطأ RMSE، المتوسط المطلق للخطأ النسبي   MAPEومعامل التحديد R2. بينت النتائج ان النموذج (2, 1, 2) × (1, 1, 1)12 كان الاكثر دقة واستخدم للتنبؤ بمعدل درجات الحرارة الشهرية العظمى لمنطقة الدراسة للفترة من 2017 الى 2021.Weather forecasting is an important issue in meteorology and scientific research.In this research, the Seasonal Auto Regressive.Integrated Moving Average.(ARIMA) model which is based on Box-Jenkins method was adopted to build the forecasting model. The max. Monthly temperature data for Kerbala city for the period (Jan.1980 to Dec.2016) was employed. The autocorrelation and partial autocorrelation functions for time series data from years 1980 to 2015 were used to identify the most appropriate orders of the ARIMA models. The validation test of these models were performed using the monthly max. Temperature of the year 2016. To calculate the model's accuracy and compare among them, statistical criteria such as MAE, RMSE, MAPE, and R2 were used. The model (2, 1, 2) × (1, 1, 1)12 gave the most accurate results and used to forecast the monthly max. Temperature for the period (2017 to 2021) for study region