Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

The present study develops an artificial neural network (ANN) to model an analysis and a simulation of the correlation between the average corrosion rate carbon steel and the effective parameter Reynolds number (Re), water concentration (Wc) % temperature (T o) with constant of PH 7 . The water, produced fom oil in Kirkuk oil field in Iraq from well no. k184-Depth2200ft., has been used as a corrosive media and specimen area (400 mm2) for the materials that were used as low carbon steel pipe. The pipes are supplied by Doura Refinery . The used flow system is all made of Q.V.F glass, and the circulation of the two –phase (liquid – liquid ) is affected using a Q.V.F pump .The input parameters of the model consists of Reynolds number , w

In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf

The objective of this study was to develop neural network algorithm, (Multilayer Perceptron), based correlations for the prediction overall volumetric mass-transfer coefficient (k_La), in slurry bubble column for gas-liquid-solid systems. The Multilayer Perceptron is a novel technique based on the feature generation approach using back propagation neural network. Measurements of overall volumetric mass transfer coefficient were made with the air - Water, air - Glycerin and air - Alcohol systems as the liquid phase in bubble column of 0.15 m diameter. For operation with gas velocity in the range 0-20 cm/sec, the overall volumetric mass transfer coefficient was found to decrease w

Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data t

To develop a petrol engine so that it works under the bi-engine pattern (producer gas-petrol) without any additional engine modifications, a single-point injection method inside the intake manifold is a simple and inexpensive method. Still, it leads to poor mixing performance between the air and producer gas. This deficiency can cause unsatisfactory engine performance and high exhaust emissions. In order to improve the mixing inside the intake manifold, nine separate cases were modelled to evaluate the impact of the position and angle orientation inside the intake manifold on the uniformity and spread of the mixture under AFR=2.07. A petrol engine (1.6 L), the maximum engine speed (8000 rpm), and bi-engine mode (petrol-producer ga

The fresh water green alga Chlorella vulgaris was selected to study its ability to degrade one of polycyclic aromatic hydrocarbon (PAHs)known is anthracene (ANT). ANT is widely used in artificial products such as wood preservatives; insecticides; dye and coating materials. This algae was cultivated in CH-10 medium under constant laboratory conditions and exposed to different concentrations (1, 3, and 5 mg/l) of anthracene for 3, 5, 7, 9 and 15 days, with the concentration of ANT measured  by high performance liquid chromatographic analysis (HPLC). The results showed that C.vulgaris has high ability to reduce anthracene to 80% at 1mg/Lcon centration after 3 days and 100% after 5 days, while at 3 and 5 mg / L concent