With growing global demand for hydrocarbons and decreasing conventional reserves, the gas industry is shifting its focus in the direction of unconventional reservoirs. Tight gas reservoirs have typically been deemed uneconomical due to their low permeability which is understood to be below 0.1mD, requiring advanced drilling techniques and stimulation to enhance hydrocarbons. However, the first step in determining the economic viability of the reservoir is to see how much gas is initially in place. Numerical simulation has been regarded across the industry as the most accurate form of gas estimation, however, is extremely costly and time consuming. The aim of this study is to provide a framework for a simple analytical method to estimate gas. Usually during production three variables are readily accessible: production rate, production time, and pressure-volume-temperature properties. This paper develops an analytical approach derived from the dynamic material balance proposing a new methodology to calculate pseudo time, with an interactive technique. This model encompasses pseudo functions accounting for pressure dependent fluid and rock variables. With the dynamic material balance yielding weak results in the linear flow regimes, an additional methodology derived from the volumetric tank model has been taken into consideration whereby equivalent drainage area is linked to total reservoir area. It has been shown even with short production data this volumetric approach yields accurate results. This proposed methodology has been validated against previous literature and additional cases considered to determine the sensitivity of each of it to reservoir parameters. Finally, it is shown that this method works for both fractured and unfractured wells in tight gas reservoirs, however, it is sensitive to the quantity of data based within the pseudo steady state flow period.
The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model
... Show MoreImage pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM
... Show MoreIn this research, we find the Bayesian formulas and the estimation of Bayesian expectation for product system of Atlas Company. The units of the system have been examined by helping the technical staff at the company and by providing a real data the company which manufacturer the system. This real data include the failed units for each drawn sample, which represents the total number of the manufacturer units by the company system. We calculate the range for each estimator by using the Maximum Likelihood estimator. We obtain that the expectation-Bayesian estimation is better than the Bayesian estimator of the different partially samples which were drawn from the product system after it checked by the
... Show MoreIn this research the Empirical Bayes method is used to Estimate the affiliation parameter in the clinical trials and then we compare this with the Moment Estimates for this parameter using Monte Carlo stimulation , we assumed that the distribution of the observation is binomial distribution while the distribution with the unknown random parameters is beta distribution ,finally we conclude that the Empirical bayes method for the random affiliation parameter is efficient using Mean Squares Error (MSE) and for different Sample size .
A condense study was done to compare between the ordinary estimators. In particular the maximum likelihood estimator and the robust estimator, to estimate the parameters of the mixed model of order one, namely ARMA(1,1) model.
Simulation study was done for a varieties the model. using: small, moderate and large sample sizes, were some new results were obtained. MAPE was used as a statistical criterion for comparison.
Objective(s): This study aims to evaluate the hardness of two commercially available cold cured acrylic resin material
(Vertex and PAN) when polymerized at different temperature in comparison to those polymerized by conventional
methods in air at 23C ± 5C.
Methodology: Eighty specimens, forty from cold cured acrylic (Vertex Type) and forty from cold cured acrylic (PAN
type) were prepared, flasking and packing procedure were done according to manufacturer direction and divided
according to processing as follow: 20 specimens (10 from Vertex type and 10 from PAN type) were processed in air for
two hours at 23C ± 5C under press (bench curing) as a control, and 60 specimens (30 from Vertex type and 30 from
PAN type) wer
In this research, nanocomposites of poly(methyl methacrylate) (PMMA) and a mixture of nano silica (SiO2) and nano zirconia (ZrO2) were prepared in different weight percentages of the nano fillers to improve some of the properties of PMMA resin to be used as a denture base material. The nano filles were surface modified with a coupling agent and added to the PMMA in different amounts. Impact strength, transverse strength, hardness and roughness were tested for both control and experimental groups. The results indicate that PMMA/silica/zirconia nanocomposites, prepared with 5% by weight of both types of fillers, had a slight increase in impact s
Establishing the systemic character of vocabulary, its relationship with other language systems, their interdependence creates the possibility of a comprehensive scientific study and description of the lexical system of each language, as well as contrastive comparative studies of several languages, including their phraseological composition.
It is known that not all words-components of phraseological units are equivalent in their role in the formation of the semantic content of phraseological units. In this regard, it is necessary to introduce the concept of a lexical dominant. To this we include words, which are kind of centers around which the entire semantic complex of phraseological units, the entire set of its words-componen
... Show MoreThe current research aims to identify the effect of Baska style on students’ achievement and the creative thinking in material rhetoric and the application of students for fifth grade literary. To achieve the study objectives, the researcher has designed an achievement test to measure group of students in the fifth grade literary in material application. The researcher chose a sample of 40 female students from Khansa School for Girls in Baghdad for the academic year (2014 - 2015), he applied the test on an exploratory sample consisted of 30 female students from the research community. The study resulted in being no statistically significant difference at the level of significance (0.05) between the mean scores of the experimental group
... Show More