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

Lost circulation or losses in drilling fluid is one of the most important problems in the oil and gas industry, and it appeared at the beginning of this industry, which caused many problems during the drilling process, which may lead to closing the well and stopping the drilling process. The drilling muds are relatively expensive, especially the muds that contain oil-based mud or that contain special additives, so it is not economically beneficial to waste and lose these muds. The treatment of drilling fluid losses is also somewhat expensive as a result of the wasted time that it caused, as well as the high cost of materials used in the treatment such as heavy materials, cement, and others. The best way to deal with drilling fluid losses

Based on the results of standard penetration tests (SPTs) conducted in Al-Basrah governorate, this research aims to present thematic maps and equations for estimating the bearing capacity of driven piles having several lengths. The work includes drilling 135 boreholes to a depth of 10 m below the existing ground level and three standard penetration tests (SPT) at depths of 1.5, 6, and 9.5 m were conducted in each borehole. MATLAB software and corrected SPT values were used to determine the bearing capacity of driven piles in Al-Basrah. Several-order interpolation polynomials are suggested to estimate the bearing capacity of driven piles, but the first-order polynomial is considered the most straightforward. Furthermore, the root means squar

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Some new cyclic imides are prepared by the reaction of ampicillin drug with different cyclic anhydrides as a first step to form amic acids for ampicillin drug. The second step includes the reaction of prepared amic acids with acetic anhydride and anhydrous sodium acetate with heating in THF as a solvent to give cyclic imide compounds. These compounds are identified by melting points, FT-IR, 1H-NMR, and biological activity

The purpose of this study is to demonstrate a simple high sensitivity vapor sensor for propanol ((CH3)2CHOH). A free space gap was employed in two arms of a Mach-Zehnder interferometer to serve as the sensing mechanism by adding propanol volume (0.2, 0.4, 0.6, 0.8, and 1) ml and to set the phase reference with a physical spacing of (0.5, 1, 1.5, and 2) mm. The propagation constant of transmitted light in the Mach-Zehnder interferometer’s gap changes due to the small variation in the refractive index inside sensing arm that will further shift the optical phase of the signal. Experimental results indicated that the highest sensitivity of propanol was about 0.0275 nm/ml in different liquid volume while highest phase shift was 0.182×103 i

The reaction of some new Schiff bases ( 2-[(2-Amino – ethylimino)-methyl]-R , 2-({2-[(R-benzylidene)-amino]-ethylimino}-methyl)-R with Benzoyl chloride or Acetyl chloride were carried out. Subsequent reactions of these products N-(2-Amino-ethyl)-N-[Chloro-(R) –methyl]-benzamide or N-(2-{?-[chloro-(R) –methyl]-amino}-ethyl)-N-[chloro-(R) –methyl]- benzamide with thiourea afforded thioureas compounds. The synthesized compounds were confirmed by their IR,UV,spectra and C.H.N. analysis.

Incremental Sheet Metal Forming (ISMF) is a modern sheet metal forming technology which offers the possibility of manufacturing 3D complex parts of thin sheet metals using the CNC milling machine. The surface quality is a very important aspect in any manufacturing process. Therefore, this study focuses on the resultant residual stresses by forming parameters, namely; (tool shape, step over, feed rate, and slope angle) using Taguchi method for the products formed by single point incremental forming process (SPIF). For evaluating the surface quality, practical experiments to produce pyramid like shape have been implemented on aluminum sheets (AA1050) for thickness (0.9) mm. Three types of tool shape used in this work, the spherical tool ga