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Artificial Intelligent Models for Detection and Prediction of Lost Circulation Events: A Review
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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 is to prevent them. Drilling fluid loss is a complex problem that is difficult to predict using simple and traditional methods. Artificial intelligence represents a modern and accurate technology for solving complex problems such as drilling fluid loss. Artificial intelligence through supervised machine learning provides the possibility of predicting these losses before they occur based on field data such as drilling fluid properties, drilling parameters, rock properties, and geomechanical parameters that are related to the loss of circulation of the wells suffered from losses problem located in the same area.

   In this paper, several supervised machine learning models have been reviewed that were used for detecting and predicting of loss of drilling fluids during the drilling process. The paper provides an inclusive review of drilling fluid prediction and detection from simplest to more complected intelligent models.

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Publication Date
Fri Oct 01 2021
Journal Name
International Journal Of Mechanical Engineering And Robotics Research
Proportional-Derivative PD Vibration Control with Adaptive Approximation Compensator for a Nonlinear Smart Thin Beam Interacting with Fluid
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This work is concerned with the vibration attenuation of a smart beam interacting with fluid using proportional-derivative PD control and adaptive approximation compensator AAC. The role of the AAC is to improve the PD performance by compensating for unmodelled dynamics using the concept of function approximation technique FAT. The key idea is to represent the unknown parameters using the weighting coefficient and basis function matrices/vectors. The weighting coefficient vector is updated using Lyapunov theory. This controller is applied to a flexible beam provided with surface bonded piezo-patches while the vibrating beam system is submerged in a fluid. Two main effects are considered: 1) axial stretching of the vibrating beam that leads

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Publication Date
Tue Jun 01 2021
Journal Name
International Journal Of Nonlinear Analysis And Applications
A proposed method for cleaning data from outlier values using the robust rfch method in structural equation modeling
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Publication Date
Fri Dec 01 2023
Journal Name
Baghdad Science Journal
A novelty Multi-Step Associated with Laplace Transform Semi Analytic Technique for Solving Generalized Non-linear Differential Equations
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   In this work, a novel technique to obtain an accurate solutions to nonlinear form by multi-step combination with Laplace-variational approach (MSLVIM) is introduced. Compared with the  traditional approach for variational it overcome all difficulties and enable to provide us more an accurate solutions with extended of the convergence region as well as covering to larger intervals which providing us a continuous representation of approximate analytic solution and it give more better information of the solution over the whole time interval. This technique is more easier for obtaining the general Lagrange multiplier with reduces the time and calculations. It converges rapidly to exact formula with simply computable terms wit

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Publication Date
Sun Sep 30 2012
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Polyvinyl Alcohol – Sodium Nitrite Water Soluble Composite as a Corrosion Inhibitor for Mild Steel in Simulated Cooling Water
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The inhibitive action of polyvinyl alcohol –sodium nitrite (PVASN) composite on the corrosion of mild steel in simulated cooling water (SCW) has been investigated by weight loss and potentiodynamic polarization. The effect of composite concentration (PVA/SN) , pH, and exposure time on corrosion rate of mild steel were verified using 2 levels factorial design and surface response analysis through weight loss approach, while the electrochemical measurements were used to study the behavior of mild steel in (SCW) with pH between 6 and 8 and in absence and presence of (PVA) in solution containing different concentration of NaNO2. It was verified that all three main variables studied were statistically significant while their interaction is

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Publication Date
Sun Feb 22 2026
Journal Name
Al–bahith Al–a'alami
MOTIVES FOR STUDENTS TO BE EXPOSED TO EDUCATIONAL PROGRAMS ON TV CHANNELS (Iraqi educational channels as a model)
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The educational function of television is one of the basic functions in light of the technical development that included the specialized satellite channels in all its fields, including the educational field, as its role became parallel to the role of educational institutions. These studies are among the descriptive studies in terms of the type of study methodology that describes the phenomenon, interprets its and extract the results and relationships between the variables. The study sample was multistage (random and intentional) included the students of the sixth academic and literary preparatory stage in the city of Baghdad.
The study problem was summarized by the following main question:
( What are the motives for the exposure of

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Publication Date
Tue Apr 02 2019
Journal Name
Artificial Intelligence Research
A three-stage learning algorithm for deep multilayer perceptron with effective weight initialisation based on sparse auto-encoder
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A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

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Publication Date
Sun Jan 14 2018
Journal Name
Journal Of Engineering
A Nonlinear MIMO-PID Neural Controller Design for Vehicle Lateral Dynamics model based on Modified Elman Neural Network
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This paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul

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Publication Date
Wed Jan 05 2022
Journal Name
Journal Of Positive School Psychology
Influence Of Variance In Rest Interval For The Explosive Power Training For The Arms And Legs In The Light Of Some Biochemical Indicators For The Cellular Equilibrium Of The Volleyballers
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Publication Date
Thu Sep 13 2018
Journal Name
Baghdad Science Journal
Synthesis and Fabrication of In2O3: CdO Nanoparticles for NO2 Gas Sensor
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The physical and morphological characteristics of porous silicon (PS) synthesized via gas sensor was assessed by electrochemical etching for a Si wafer in diluted HF acid in water (1:4) at different etching times and different currents. The morphology for PS wafers by AFM show that the average pore diameter varies from 48.63 to 72.54 nm with increasing etching time from 5 to 15min and from 72.54 to 51.37nm with increasing current from 10 to 30 mA. From the study, it was found that the gas sensitivity of In2O3: CdO semiconductor, against NO2 gas, directly correlated to the nanoparticles size, and its sensitivity increases with increasing operating temperature.

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Publication Date
Tue Mar 01 2016
Journal Name
Journal Of Engineering
Design and Simulation of Sliding Mode Fuzzy Controller for Nonlinear System
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Sliding Mode Controller (SMC) is a simple method and powerful technique to design a robust controller for nonlinear systems. It is an effective tool with acceptable performance. The major drawback is a classical Sliding Mode controller suffers from the chattering phenomenon which causes undesirable zigzag motion along the sliding surface. To overcome the snag of this classical approach, many methods were proposed and implemented. In this work, a Fuzzy controller was added to classical Sliding Mode controller in order to reduce the impact chattering problem. The new structure is called Sliding Mode Fuzzy controller (SMFC) which will also improve the properties and performance of the classical Sliding Mode control

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