This paper provides an attempt for modeling rate of penetration (ROP) for an Iraqi oil field with aid of mud logging data. Data of Umm Radhuma formation was selected for this modeling. These data include weight on bit, rotary speed, flow rate and mud density. A statistical approach was applied on these data for improving rate of penetration modeling. As result, an empirical linear ROP model has been developed with good fitness when compared with actual data. Also, a nonlinear regression analysis of different forms was attempted, and the results showed that the power model has good predicting capability with respect to other forms.

Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal.  The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in

   Fracture pressure gradient prediction is complementary in well design and it is must be considered in selecting the safe mud weight, cement design, and determine the optimal casing seat to minimize the common drilling problems. The exact fracture pressure gradient value obtained from tests on the well while drilling such as leak-off test, formation integrity test, cement squeeze ... etc.; however, to minimize the total cost of drilling, there are several methods could be used to calculate fracture pressure gradient classified into two groups: the first one depend on Poisson’s ratio of the rocks and the second is fully empirical methods. In this research, the methods selected are Huubert and willis, Cesaroni I, Cesaroni II,

Background: Assessment is an important part of the learning cascade in education. Students realize it as an influential motivator to direct and guide their learning. The method of assessment determines the way the students reach high levels of learning. It has been documented that one of factor affecting students’ choice of learning approach is the way how assessment is being performed. Many methods of assessment namely multiple choice questions, essay questions and others are mainly used to assess basic science knowledge in undergraduate education. Objectives: The aim of this study is to compare multiple choice questions (MCQ) and essay questions (EQ) (record the success and failure rate of multiple choice questions (MCQ) and essay quest

Amino glycoside derivation including, Neomycin, Streptomycin, Kanamycin and Gentamycin with special reagents, which  are  benzoylchloride; benzene sulfonyl chloride and phthalic anhydride were made to enhance Uv-detectability for HPLC analysis. But there are many problems facing pre column derivation and in order to solve this, the conductivity of antibiotic derivatives were used to calculate the dissociation constant and the hydrolysis rate which determined concern type reaction. In addition the  characteristics those controlling the hydrolysis of antibiotic-derivatives were investigated.

The curriculum is considered one of the most important corners for the educational process, it participates in achieving the educational equilibrium between its elements.

The curriculum of (History of the Art) in the college of Art Education / dept. of plastic art education and ceramic as the other curriculums and due to the subject of history of the art develop the knowledge, taste and experiment for art studier also this subject has a direct relation with practical educational activities like colors and growth the knowledge of the student in field of contents and symbols and evidences of meaning in old , medium and contemporary arts, due to the fact that this curriculum has been applied since long time more than four years, and

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

Experimental and numerical studies have been conducted on the effects of bed roughness elements such as cubic and T-section elements that are regularly half-channel arrayed on one side of the river on turbulent flow characteristics and bed erosion downstream of the roughness elements. The experimental study has been done for two types of bed roughness elements (cubic and T-section shape) to study the effect of these elements on the velocity profile downstream the elements with respect to different water flow discharges and water depths. A comparison between the cubic and T-section artificial bed roughness showed that the velocity profile downstream the T-section increased in smooth side from the river and decrease in the rough side