A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited grids and used in the training and testing of the used network. A comparison between the calculated and observed cumulative oil production has been carried out through the testing steps of the constructed ANN, an absolute average percentage error of the used network was reached to 4.044%, and this is consider to be an acceptable limit within engineering applications, in addition to that, a good behavior was reached with (FFNNW) and suitable re-entry wells location were identified according to the reservoir configuration (pressure and saturation distribution) output from SRF simulation model at the end of 2005.
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One of the most important enhanced oil recoveries methods is miscible displacement. During this method preferably access to the conditions of miscibility to improve the extraction process and the most important factor in these conditions is miscibility pressure. This study focused on establishing a suitable correlation to calculate the minimum miscibility pressure (MMP) required for injecting hydrocarbon gases into southern Iraq oil reservoir. MMPs were estimated for thirty oil samples from southern Iraqi oil fields by using modified Peng and Robinson equation of state. The obtained PVT reports properties were used for tunning the equation of state parameters by making a match between the equation of state results with experimenta
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The Albian Carbonate-clastic succession in the present study is represented by the Mauddud and Nahr Umr formations were deposited during the Albian stage within the Wasia Group More than 200 thin sections of cores and cuttings in addition to well logs data for Nahr Umr and Mauddud formations from 4 boreholes within two oil fields (Ba-4, Ba-8, Ns-2 and Ns-4) were used to interpret the different associations facies as well as the facies architectures to describe the sedimentary framework of the basin and development the petrophysical properties. Seven major microfacies were diagnosed in the carbonate succession of the Mauddud Formation, while the Nar Umr Formation includes five lithofacies; their grain types characteristic and deposit
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In the present work, a closed loop circulation system consist of three testing sections was designed and constructed. The testing sections made from (3m) of commercial carbon steel pipe of diameters(5.08, 2.54 and 1.91 cm) . Anionic surfactant (SDBS )with concentrations of (50, 100, 150, 200 and 250 ppm) was tested as a drag reducing agent. The additive(SDBS)studied using crude oil from south of Iraq. The flow rates of crude oil were used in 5.08 and 2.54 cm I.D. pipes are (1 - 12) m3/hr while (1-6) m3/hr were used in 1.91 cm J .D. pipe . Percentage drag reduction (%Dr) was found to increase by increasing solution velocity, pipe diameter and additives concentration (i.e. increasi
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In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained
The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T
Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer
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In Libya, there are multiple sources of pollution, one of which is animal waste. The anaerobic digestion (AD) of organic wastes to produce biogas has the advantage of producing valuable, renewable energy while reducing the environmental impact of these wastes. Cowmanure have the potential to produce biogas due to their high organic content. This study aimed to study different concentrations for the feedstock (1:1 and 2:1 cow manure: water v/v) to monitor which one gives higher biogas production. A plastic tank with a capacity of 72 liters and a feedstock volume of 60 liters was used to create a pilot scale. The biogas was analyzed using a GC device at the end of the experiment in the Zawiya Oil Refining Company. The result indicated that th
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