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

This paper introduces a novel nonparametric hybrid cyber-intelligence-based statistical process control and anomaly detection framework in time series data. It is developed to overcome the shortcomings of the classical control schemes in dealing with complex, abnormal, and noisy input data, especially when it is autocorrelated. The proposed methodology combines three technical pillars: First, it utilizes a bidirectional long-short-term memory architecture (Bi-LSTM) to capture long-term time dependency and learn nonlinear patterns, leaving only true deviations as residuals that remove trends and noises from the market. Second, it adopts the Golden Eagle Optimizer (GEO) algorithm for optimal parameter selection. This intelligent algor

In this study, the kinematic analysis including the forward and inverse kinematic developed for a 5 degree of freedom robotic arm. The forward kinematic is elaborated using Denavit-Hartenberg (DH) convention. Inverse Kinematic is established using Deep Neural Network (DNN) model with five hidden layer each contain 50 neurons fully connected using ReLu activation. A data of inputs and outputs are created and trained. The inputs are the end-effector position and orientation. The outputs are the joint angles of the manipulator. The data is generated by the forward kinematics, where a set of joint angles that limited by their corresponding ranges are inserted to the forward kinematic equations to result the end-effector positions and or

Accurate estimation of permeability is essential for reliable reservoir characterization and simulation. However, core data are often limited due to their high cost and operational constraints, leading to significant uncertainty in uncored intervals. This study aims to reduce the dependence on core data for permeability estimation by integrating electrofacies derived from well logs and flow zone indicator analysis in the Mishrif Formation, Nasiriyah oil field. Electrofacies were generated using dynamic clustering based on the log data from 13 wells. This led to the identification of five electrofacies units that represent intervals with similar log responses, formed by merging 75 electrofacies, where flow zone indicators analysis id

Motifs template is the input for many bioinformatics systems such codons finding, transcription, transaction, sequential pattern miner, and bioinformatics databases analysis. The size of motifs arranged from one base up to several Mega bases, therefore, the typing errors increase according to the size of motifs. In addition, when the structures motifs are submitted to bioinformatics systems, the specifications of motifs components are required, i.e. the simple motifs, gaps, and the lower bound and upper bound of each gap. The motifs can be of DNA, RNA, or Protein. In this research, a motif parser and visualization module is designed depending on a proposed a context free grammar, CFG, and colors human recognition system. GFC describes the m

In this research, Mn-doped TiO2 thin films were grown on glass, Si and OIT/glass substrates by R.F magnetron sputtering technique with thicknesses (250 nm) using TiO2:Mn target under Ar gas pressure and power of 100 Watt. Through the results of X-ray diffraction, the prepared thin films are of the polycrystallization type after the process of annealing at 600°C for two hour The average crystalline size were 145.32, 280.97 and 261.23 nm for (TiO2:Mn) thin film on glass, Si and OIT/glass substrates respectively, while the measured surface roughness is between 0.981nm and 1.14 nm. The fabricated (TiO2:Mn) thin film on glass sensors have high sensitivity for hydrogen( H2 reducing gas) compared to the sensitivity for hydrogen gas on Si and OIT/