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 g

     This research deals with the study of the types and distribution of petrographic microfacies and Paleoenvironments of Mishrif Formation in Halfaya oil field, to define specific sedimentary environments. These environments were identified by microscopic examination of 35 thin sections of cutting samples for well HF-9H as well as 150 thin sections of core and cutting samples for well HF-I. Depending on log interpretation of wells HF-1, HF-316, HF-109, IIF-115, and IIF-272, the sedimentary facies were traced vertically through the use of various logs by Petrel 2013 software in addition to previous studies. Microfacies analysis showed the occurrence of six main Paleoenvironments within Mishrif succession, represented

Exploitation of mature oil fields around the world has forced researchers to develop new ways to optimize reservoir performance from such reservoirs. To achieve that, drilling horizontal wells is an effective method. The effectiveness of this kind of wells is to increase oil withdrawal. The objective of this study is to optimize the location, design, and completion of a new horizontal well as an oil producer to improve oil recovery in a real field located in Iraq. “A” is an oil and gas condensate field located in the Northeast of Iraq. From field production history, it is realized the difficulty to control gas and water production in this kind of complex carbonate reservoir with vertical producer wells. In this study, a horizont

A .technology analysis image using crops agricultural of grading and sorting the test to conducted was experiment The device coupling the of sensor a with camera a and 75 * 75 * 50 dimensions with shape cube studio made-factory locally the study to studio the in taken were photos and ,)blue-green - red (lighting triple with equipped was studio The .used were neural artificial and technology processing image using maturity and quality ,damage of fruits the of characteristics external value the quality 0.92062, of was value regression the damage predict to used was network neural artificial The .network the using scheme regression a of means by 0.98654 of was regression the of maturity and 0.97981 of was regression the of .algorithm Marr

The Artificial Neural Network methodology is a very important & new subjects that build's the models for Analyzing, Data Evaluation, Forecasting & Controlling without depending on an old model or classic statistic method that describe the behavior of statistic phenomenon, the methodology works by simulating the data to reach a robust optimum model that represent the statistic phenomenon & we can use the model in any time & states, we used the Box-Jenkins (ARMAX) approach for comparing, in this paper depends on the received power to build a robust model for forecasting, analyzing & controlling in the sod power, the received power come from

The river water salinity is a major concern in many countries, and salinity can be expressed as total dissolved solids. So, the water salinity impact of the river is one of the major factors effects of water quality. Tigris river water salinity increase with streamline and time due to the decrease in the river flow and dam construction from neighboring countries. The major objective of this research to developed salinity model to study the change of salinity and its impact on the Al-Karkh, Sharq Dijla, Al-Karama, Al-Wathba, Al-Dora, and Al-Wihda water treatment plant along Tigris River in Baghdad city using artificial neural network model (ANN). The parameter used in a model built is (Turbidity, Ec, T.s, S.s, and TDS in)

Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

    This study aims to set up a 3D static model to characterize and evaluate Mishrif Formation which represents the main reservoir in Buzurgan Oilfield, southern Iraq. Six wells have been selected to set up structural, facies and petrophysical models of Mishrif reservoir by using Petrel Software. The structural model has been built based on the structural contour map of the top of Mishrif Formation, which derived from seismic interpretation, and by using different static algorithms in Petrel Software. The structural model showed that the Buzurgan Oilfield represents an anticlinal fold with two domes north and south separated by a depression. The petrophysical model included the porosity model and water saturation model. Th

The aim of the present study was to develop theophylline (TP) inhalable sustained delivery system by preparing solid lipid microparticles using glyceryl behenate (GB) and poloxamer 188 (PX) as a lipid carrier and a surfactant respectively. The method involves loading TP nanoparticles into the lipid using high shear homogenization – ultrasonication technique followed by lyophilization. The compositional variations and interactions were evaluated using response surface methodology, a Box – Behnken design of experiment (DOE). The DOE constructed using TP (X₁), GB (X₂) and PX (X₃) levels as independent factors. Responses measured were the entrapment efficiency (% EE) (Y₁), mass median