The lower Cretaceous sandstones of Zubair and Nahr Umr formations are the main producing reservoirs in Subba oilfield in southern Iraq. Key differences in their petrophysical and depositional attributes exist affecting their reservoir characteristics. The evaluation of well logs and core porosity-permeability data show better reservoir properties in Nahr Formation. The Litho-saturation logs indicate greater thickness of oil-saturated reservoir units for Nahr Unr Formation associated with lower values of shale volume, and higher values of effective porosity. In addition, higher values of permeability for Nahr Umr Formation is suggested by applying porosity-irreducible water saturation cross plot. The reducing reservoir quality of Zub

The hydraulic behavior of the flow can be changed by using large-scale geometric roughness elements in open channels. This change can help in controlling erosions and sedimentations along the mainstream of the channel. Roughness elements can be large stone or concrete blocks placed at the channel's bed to impose more resistance in the bed. The geometry of the roughness elements, numbers used, and configuration are parameters that can affect the flow's hydraulic characteristics. In this paper, velocity distribution along the flume was theoretically investigated using a series of tests of T-shape roughness elements, fixed height, arranged in three different configurations, differ in the number of lines of roughness element

This paper is concerned with the blow-up solutions of a system of two reaction-diffusion equations coupled in both equations and boundary conditions. In order to understand how the reaction terms and the boundary terms affect the blow-up properties, the lower and upper blow-up rate estimates are derived. Moreover, the blow-up set under some restricted assumptions is studied.

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl

the main of this paper is to give a comprehensive presentation of estimating methods namely maximum likelihood bayes and proposed methods for the parameter

THE EFFECT OF SPREACL of KNOWLEDGE ON ETHICS