Increasing hydrocarbon recovery from tight reservoirs is an essential goal of oil industry in the recent years. Building real dynamic simulation models and selecting and designing suitable development strategies for such reservoirs need basically to construct accurate structural static model construction. The uncertainties in building 3-D reservoir models are a real challenge for such micro to nano pore scale structure. Based on data from 24 wells distributed throughout the Sadi tight formation. An application of building a 3-D static model for a tight limestone oil reservoir in Iraq is presented in this study. The most common uncertainties confronted while building the model were illustrated. Such as accurate estimations of cut-off permeability and porosity values. These values directly affect the calculation of net pay thickness for each layer in the reservoir and consequently affect the target of estimating reservoir initial oil in place (IOIP). Also, the main challenge to the static modeling of such reservoirs is dealing with tight reservoir characteristics which cause major reservoir heterogeneity and complexities that are problematic to the process of modeling reservoir simulation. Twenty seven porosity and permeability measurements from Sadi/Tanuma reservoir were used to validate log interpretation data for model construction. The results of the history matching process of the constructed dynamic model is also presented in this paper, including data related to oil production, reservoir pressure, and well flowing pressure due to available production.
The human kidney is one of the most important organs in the human body; it performs many functions
and has a great impact on the work of the rest of the organs. Among the most important possible treatments is
dialysis, which works as an external artificial kidney, and several studies have worked to enhance the
mechanism of dialysate flow and improve the permeability of its membrane. This study introduces a new
numerical model based on previous research discussing the variations in the concentrations of sodium,
potassium, and urea in the extracellular area in the blood during hemodialysis. We simulated the differential
equations related to mass transfer diffusion and we developed the model in MATLAB Simu
This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback
... Show MoreThe auditory system can suffer from exposure to loud noise and human health can be affected. Traffic noise is a primary contributor to noise pollution. To measure the noise levels, 3 variables were examined at 25 locations. It was found that the main factors that determine the increase in noise level are traffic volume, vehicle speed, and road functional class. The data have been taken during three different periods per day so that they represent and cover the traffic noise of the city during heavy traffic flow conditions. Analysis of traffic noise prediction was conducted using a simple linear regression model to accurately predict the equivalent continuous sound level. The difference between the predicted and the measured noise shows that
... Show MoreToday with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned